Abstract

We use functional near-infrared spectroscopy (fNIRS) to discriminate the alert and drowsy states for a passive brain-computer interface (BCI). The passive brain signals for the drowsy state are acquired from the prefrontal and dorsolateral prefrontal cortex. The experiment is performed on 13 healthy subjects using a driving simulator, and their brain activity is recorded using a continuous-wave fNIRS system. Linear discriminant analysis (LDA) is employed for training and testing, using the data from the prefrontal, left- and right-dorsolateral prefrontal regions. For classification, eight features are tested: mean oxyhemoglobin, mean deoxyhemoglobin, skewness, kurtosis, signal slope, number of peaks, sum of peaks, and signal peak, in 0~5, 0~10, and 0~15 second time windows, respectively. The results show that the best performance for classification is achieved using mean oxyhemoglobin, the signal peak, and the sum of peaks as features. The average accuracies in the right dorsolateral prefrontal cortex (83.1, 83.4 and 84.9% in the 0~5, 0~10 and 0~15 second time windows, respectively) show that the proposed method has an effective utility for detection of drowsiness for a passive BCI.

© 2015 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Detection and classification of three-class initial dips from prefrontal cortex

Amad Zafar and Keum-Shik Hong
Biomed. Opt. Express 8(1) 367-383 (2017)

Increased prefrontal cortex connectivity during cognitive challenge assessed by fNIRS imaging

Frigyes Samuel Racz, Peter Mukli, Zoltan Nagy, and Andras Eke
Biomed. Opt. Express 8(8) 3842-3855 (2017)

Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study

Fares Al-Shargie, Tong Boon Tang, and Masashi Kiguchi
Biomed. Opt. Express 8(5) 2583-2598 (2017)

References

  • View by:
  • |
  • |
  • |

  1. L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors (Basel) 12(2), 1211–1279 (2012).
    [Crossref] [PubMed]
  2. S. Coyle, T. Ward, and C. Markham, “Brain-computer interfaces: A review,” Interdiscip. Sci. Rev. 28(2), 112–118 (2003).
    [Crossref]
  3. A. Turnip, K.-S. Hong, and M.-Y. Jeong, “Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis,” Biomed. Eng. Online 10(1), 83 (2011).
    [Crossref] [PubMed]
  4. N. K. Logothetis, “What we can do and what we cannot do with fMRI,” Nature 453(7197), 869–878 (2008).
    [Crossref] [PubMed]
  5. M. P. van den Heuvel and H. E. Hulshoff Pol, “Exploring the brain network: A review on resting-state fMRI functional connectivity,” Eur. Neuropsychopharmacol. 20(8), 519–534 (2010).
    [Crossref] [PubMed]
  6. M. Welvaert and Y. Rosseel, “A review of fMRI simulation studies,” PLoS One 9(7), e101953 (2014).
    [Crossref] [PubMed]
  7. N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: A review,” Front. Hum. Neurosci. 9, 3 (2015).
    [PubMed]
  8. M. Strait and M. Scheutz, “What we can and cannot (yet) do with functional near infrared spectroscopy,” Front. Neurosci. 8, 117 (2014).
    [Crossref] [PubMed]
  9. D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
    [Crossref] [PubMed]
  10. M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
    [Crossref] [PubMed]
  11. M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
    [Crossref] [PubMed]
  12. S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage 59(1), 519–529 (2012).
    [Crossref] [PubMed]
  13. T. O. Zander and C. Kothe, “Towards passive brain-computer interfaces: Applying brain-computer interface technology to human-machine systems in general,” J. Neural Eng. 8(2), 025005 (2011).
    [Crossref] [PubMed]
  14. N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
    [Crossref] [PubMed]
  15. N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett. 553, 84–89 (2013).
    [Crossref] [PubMed]
  16. K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
    [Crossref] [PubMed]
  17. R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
    [Crossref] [PubMed]
  18. S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
    [Crossref] [PubMed]
  19. L. C. Schudlo and T. Chau, “Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest,” J. Neural Eng. 11(1), 016003 (2014).
    [Crossref] [PubMed]
  20. S. D. Power, A. Kushki, and T. Chau, “Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS,” PLoS One 7(7), e37791 (2012).
    [Crossref] [PubMed]
  21. S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: Differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
    [Crossref] [PubMed]
  22. H.-J. Hwang, D. Hwan Kim, C.-H. Han, and C.-H. Im, “A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface (BCI),” Brain Res. 1515, 66–77 (2013).
    [Crossref] [PubMed]
  23. Y. Tomita, F.-B. Vialatte, G. Dreyfus, Y. Mitsukura, H. Bakardjian, and A. Cichocki, “Bimodal BCI using simultaneously NIRS and EEG,” IEEE Trans. Biomed. Eng. 61(4), 1274–1284 (2014).
    [Crossref] [PubMed]
  24. H. Santosa, M. J. Hong, and K.-S. Hong, “Lateralization of music processing auditory cortex: An fNIRS study,” Front. Behav. Neurosci. 8, UNSP 418 (2014).
  25. K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed. Opt. Express 5(6), 1778–1798 (2014).
    [Crossref] [PubMed]
  26. X.-S. Hu, K.-S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
    [Crossref] [PubMed]
  27. B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, “Using EEG spectral components to assess algorithms for detecting fatigue,” Expert Syst. Appl. 36(2), 2352–2359 (2009).
    [Crossref]
  28. W. Vanlaar, H. Simpson, D. Mayhew, and R. Robertson, “Fatigued and drowsy driving: A survey of attitudes, opinions and behaviors,” J. Safety Res. 39(3), 303–309 (2008).
    [Crossref] [PubMed]
  29. T. Liu, “Positive correlation between drowsiness and prefrontal activation during a simulated speed-control driving task,” Neuroreport 25(16), 1316–1319 (2014).
    [Crossref] [PubMed]
  30. Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
    [Crossref] [PubMed]
  31. P. P. Caffier, U. Erdmann, and P. Ullsperger, “Experimental evaluation of eye-blink parameters as a drowsiness measure,” Eur. J. Appl. Physiol. 89(3), 319–325 (2003).
    [Crossref] [PubMed]
  32. Q. Ji, Z. W. Zhu, and P. L. Lan, “Real-time nonintrusive monitoring and prediction of driver fatigue,” IEEE Trans. Vehicular Technol. 53(4), 1052–1068 (2004).
    [Crossref]
  33. J. Horne and L. Reyner, “Vehicle accidents related to sleep: A review,” Occup. Environ. Med. 56(5), 289–294 (1999).
    [Crossref] [PubMed]
  34. K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway,” Front. Hum. Neurosci. 7, 882 (2013).
    [Crossref] [PubMed]
  35. C. T. Lin, C. H. Chuang, C. S. Huang, S. F. Tsai, S. W. Lu, Y. H. Chen, and L. W. Ko, “Wireless and wearable EEG system for evaluating driver vigilance,” IEEE Trans. Biomed. Circuits Syst. 8(2), 165–176 (2014).
    [Crossref] [PubMed]
  36. F. X. Graydon, R. Young, M. D. Benton, R. J. Genik, S. Posse, L. Hsieh, and C. Green, “Visual event detection during simulated driving: Identifying the neural correlates with functional neuroimaging,” Transp. Res. Pt. F-Traffic Psychol. Behav. 7, 271–286 (2004).
  37. E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
    [Crossref] [PubMed]
  38. Y. Uchiyama, H. Toyoda, H. Sakai, D. Shin, K. Ebe, and N. Sadato, “Suppression of brain activity related to a car-following task with an auditory task: An fMRI study,” Transp. Res. Pt. F-Traffic Psychol. Behav. 15, 25–37 (2012).
  39. T. A. Schweizer, K. Kan, Y. Hung, F. Tam, G. Naglie, and S. J. Graham, “Brain activity during driving with distraction: An immersive fMRI study,” Front. Hum. Neurosci. 7, 53 (2013).
    [Crossref] [PubMed]
  40. B.-G. Lee, B.-L. Lee, and W.-Y. Chung, “Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals,” Sensors (Basel) 14(10), 17915–17936 (2014).
    [Crossref] [PubMed]
  41. A. Garcés Correa, L. Orosco, and E. Laciar, “Automatic detection of drowsiness in EEG records based on multimodal analysis,” Med. Eng. Phys. 36(2), 244–249 (2014).
    [Crossref] [PubMed]
  42. J. Wang, Y. Y. Wu, H. Qu, and G. H. Xu, “EEG-based fatigue driving detection using correlation dimension,” J. Vibroeng. 16, 407–413 (2014).
  43. S. Hu, G. Zheng, and B. Peters, “Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal,” IET Intell. Transp. Syst. 7(1), 105–113 (2013).
    [Crossref]
  44. G. Li and W.-Y. Chung, “Estimation of eye closure degree using EEG sensors and its application in driver drowsiness detection,” Sensors (Basel) 14(9), 17491–17515 (2014).
    [Crossref] [PubMed]
  45. R. N. Roy, S. Charbonnier, and S. Bonnet, “Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms,” Biomed. Signal Process. Control 14, 256–264 (2014).
    [Crossref]
  46. A. Picot, S. Charbonnier, and A. Caplier, “On-line detection of drowsiness using brain and visual information,” IEEE Trans. Syst. Man Cybern. A Syst. Hum. 42(3), 764–775 (2012).
    [Crossref]
  47. H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
    [Crossref] [PubMed]
  48. M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: Three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
    [Crossref] [PubMed]
  49. M. A. Kamran and K.-S. Hong, “Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: An fNIRS study,” J. Neural Eng. 10(5), 056002 (2013).
    [Crossref] [PubMed]
  50. J. W. Barker, A. Aarabi, and T. J. Huppert, “Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS,” Biomed. Opt. Express 4(8), 1366–1379 (2013).
    [Crossref] [PubMed]
  51. J. Li and L. Qiu, “Temporal correlation of spontaneous hemodynamic activity in language areas measured with functional near-infrared spectroscopy,” Biomed. Opt. Express 5(2), 587–595 (2014).
    [Crossref] [PubMed]
  52. W. B. Baker, A. B. Parthasarathy, D. R. Busch, R. C. Mesquita, J. H. Greenberg, and A. G. Yodh, “Modified Beer-Lambert law for blood flow,” Biomed. Opt. Express 5(11), 4053–4075 (2014).
    [Crossref] [PubMed]
  53. S. Lemm, B. Blankertz, T. Dickhaus, and K. R. Müller, “Introduction to machine learning for brain imaging,” Neuroimage 56(2), 387–399 (2011).
    [Crossref] [PubMed]
  54. F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” J. Neural Eng. 4(2), R1–R13 (2007).
    [Crossref] [PubMed]
  55. C. Bogler, J. Mehnert, J. Steinbrink, and J. D. Haynes, “Decoding vigilance with NIRS,” PLoS One 9(7), e101729 (2014).
    [Crossref] [PubMed]
  56. C. Kaufmann, R. Wehrle, T. C. Wetter, F. Holsboer, D. P. Auer, T. Pollmächer, and M. Czisch, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study,” Brain 129(3), 655–667 (2006).
    [Crossref] [PubMed]
  57. G. R. Poudel, C. R. H. Innes, and R. D. Jones, “Cerebral perfusion differences between drowsy and nondrowsy individuals after acute sleep restriction,” Sleep 35(8), 1085–1096 (2012).
    [PubMed]
  58. M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
    [Crossref] [PubMed]
  59. N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near-infrared spectroscopy,” J. Near Infrared Spectrosc. 23(1), 23–31 (2015).
    [Crossref]
  60. X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
    [Crossref] [PubMed]
  61. S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
    [Crossref] [PubMed]
  62. S. Fazli, J. Mehnert, J. Steinbrink, and B. Blankertz, “Using NIRS as a predictor for EEG-based BCI performance,” in Proceedings of IEEE Conference of Engineering in Medicine and Biology Society (IEEE EMBC, 2012), pp. 4911–4914.
    [Crossref]

2015 (4)

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: A review,” Front. Hum. Neurosci. 9, 3 (2015).
[PubMed]

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
[Crossref] [PubMed]

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near-infrared spectroscopy,” J. Near Infrared Spectrosc. 23(1), 23–31 (2015).
[Crossref]

2014 (19)

B.-G. Lee, B.-L. Lee, and W.-Y. Chung, “Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals,” Sensors (Basel) 14(10), 17915–17936 (2014).
[Crossref] [PubMed]

A. Garcés Correa, L. Orosco, and E. Laciar, “Automatic detection of drowsiness in EEG records based on multimodal analysis,” Med. Eng. Phys. 36(2), 244–249 (2014).
[Crossref] [PubMed]

J. Wang, Y. Y. Wu, H. Qu, and G. H. Xu, “EEG-based fatigue driving detection using correlation dimension,” J. Vibroeng. 16, 407–413 (2014).

G. Li and W.-Y. Chung, “Estimation of eye closure degree using EEG sensors and its application in driver drowsiness detection,” Sensors (Basel) 14(9), 17491–17515 (2014).
[Crossref] [PubMed]

R. N. Roy, S. Charbonnier, and S. Bonnet, “Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms,” Biomed. Signal Process. Control 14, 256–264 (2014).
[Crossref]

J. Li and L. Qiu, “Temporal correlation of spontaneous hemodynamic activity in language areas measured with functional near-infrared spectroscopy,” Biomed. Opt. Express 5(2), 587–595 (2014).
[Crossref] [PubMed]

W. B. Baker, A. B. Parthasarathy, D. R. Busch, R. C. Mesquita, J. H. Greenberg, and A. G. Yodh, “Modified Beer-Lambert law for blood flow,” Biomed. Opt. Express 5(11), 4053–4075 (2014).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: Three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

C. Bogler, J. Mehnert, J. Steinbrink, and J. D. Haynes, “Decoding vigilance with NIRS,” PLoS One 9(7), e101729 (2014).
[Crossref] [PubMed]

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
[Crossref] [PubMed]

M. Welvaert and Y. Rosseel, “A review of fMRI simulation studies,” PLoS One 9(7), e101953 (2014).
[Crossref] [PubMed]

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
[Crossref] [PubMed]

M. Strait and M. Scheutz, “What we can and cannot (yet) do with functional near infrared spectroscopy,” Front. Neurosci. 8, 117 (2014).
[Crossref] [PubMed]

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

Y. Tomita, F.-B. Vialatte, G. Dreyfus, Y. Mitsukura, H. Bakardjian, and A. Cichocki, “Bimodal BCI using simultaneously NIRS and EEG,” IEEE Trans. Biomed. Eng. 61(4), 1274–1284 (2014).
[Crossref] [PubMed]

K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed. Opt. Express 5(6), 1778–1798 (2014).
[Crossref] [PubMed]

L. C. Schudlo and T. Chau, “Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest,” J. Neural Eng. 11(1), 016003 (2014).
[Crossref] [PubMed]

T. Liu, “Positive correlation between drowsiness and prefrontal activation during a simulated speed-control driving task,” Neuroreport 25(16), 1316–1319 (2014).
[Crossref] [PubMed]

C. T. Lin, C. H. Chuang, C. S. Huang, S. F. Tsai, S. W. Lu, Y. H. Chen, and L. W. Ko, “Wireless and wearable EEG system for evaluating driver vigilance,” IEEE Trans. Biomed. Circuits Syst. 8(2), 165–176 (2014).
[Crossref] [PubMed]

2013 (9)

H.-J. Hwang, D. Hwan Kim, C.-H. Han, and C.-H. Im, “A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface (BCI),” Brain Res. 1515, 66–77 (2013).
[Crossref] [PubMed]

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway,” Front. Hum. Neurosci. 7, 882 (2013).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett. 553, 84–89 (2013).
[Crossref] [PubMed]

M. A. Kamran and K.-S. Hong, “Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: An fNIRS study,” J. Neural Eng. 10(5), 056002 (2013).
[Crossref] [PubMed]

J. W. Barker, A. Aarabi, and T. J. Huppert, “Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS,” Biomed. Opt. Express 4(8), 1366–1379 (2013).
[Crossref] [PubMed]

T. A. Schweizer, K. Kan, Y. Hung, F. Tam, G. Naglie, and S. J. Graham, “Brain activity during driving with distraction: An immersive fMRI study,” Front. Hum. Neurosci. 7, 53 (2013).
[Crossref] [PubMed]

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

S. Hu, G. Zheng, and B. Peters, “Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal,” IET Intell. Transp. Syst. 7(1), 105–113 (2013).
[Crossref]

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
[Crossref] [PubMed]

2012 (9)

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

G. R. Poudel, C. R. H. Innes, and R. D. Jones, “Cerebral perfusion differences between drowsy and nondrowsy individuals after acute sleep restriction,” Sleep 35(8), 1085–1096 (2012).
[PubMed]

A. Picot, S. Charbonnier, and A. Caplier, “On-line detection of drowsiness using brain and visual information,” IEEE Trans. Syst. Man Cybern. A Syst. Hum. 42(3), 764–775 (2012).
[Crossref]

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage 59(1), 519–529 (2012).
[Crossref] [PubMed]

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors (Basel) 12(2), 1211–1279 (2012).
[Crossref] [PubMed]

Y. Uchiyama, H. Toyoda, H. Sakai, D. Shin, K. Ebe, and N. Sadato, “Suppression of brain activity related to a car-following task with an auditory task: An fMRI study,” Transp. Res. Pt. F-Traffic Psychol. Behav. 15, 25–37 (2012).

S. D. Power, A. Kushki, and T. Chau, “Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS,” PLoS One 7(7), e37791 (2012).
[Crossref] [PubMed]

X.-S. Hu, K.-S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
[Crossref] [PubMed]

2011 (5)

S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: Differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
[Crossref] [PubMed]

Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
[Crossref] [PubMed]

A. Turnip, K.-S. Hong, and M.-Y. Jeong, “Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis,” Biomed. Eng. Online 10(1), 83 (2011).
[Crossref] [PubMed]

T. O. Zander and C. Kothe, “Towards passive brain-computer interfaces: Applying brain-computer interface technology to human-machine systems in general,” J. Neural Eng. 8(2), 025005 (2011).
[Crossref] [PubMed]

S. Lemm, B. Blankertz, T. Dickhaus, and K. R. Müller, “Introduction to machine learning for brain imaging,” Neuroimage 56(2), 387–399 (2011).
[Crossref] [PubMed]

2010 (1)

M. P. van den Heuvel and H. E. Hulshoff Pol, “Exploring the brain network: A review on resting-state fMRI functional connectivity,” Eur. Neuropsychopharmacol. 20(8), 519–534 (2010).
[Crossref] [PubMed]

2009 (1)

B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, “Using EEG spectral components to assess algorithms for detecting fatigue,” Expert Syst. Appl. 36(2), 2352–2359 (2009).
[Crossref]

2008 (2)

W. Vanlaar, H. Simpson, D. Mayhew, and R. Robertson, “Fatigued and drowsy driving: A survey of attitudes, opinions and behaviors,” J. Safety Res. 39(3), 303–309 (2008).
[Crossref] [PubMed]

N. K. Logothetis, “What we can do and what we cannot do with fMRI,” Nature 453(7197), 869–878 (2008).
[Crossref] [PubMed]

2007 (3)

R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
[Crossref] [PubMed]

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
[Crossref] [PubMed]

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” J. Neural Eng. 4(2), R1–R13 (2007).
[Crossref] [PubMed]

2006 (1)

C. Kaufmann, R. Wehrle, T. C. Wetter, F. Holsboer, D. P. Auer, T. Pollmächer, and M. Czisch, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study,” Brain 129(3), 655–667 (2006).
[Crossref] [PubMed]

2005 (1)

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

2004 (2)

F. X. Graydon, R. Young, M. D. Benton, R. J. Genik, S. Posse, L. Hsieh, and C. Green, “Visual event detection during simulated driving: Identifying the neural correlates with functional neuroimaging,” Transp. Res. Pt. F-Traffic Psychol. Behav. 7, 271–286 (2004).

Q. Ji, Z. W. Zhu, and P. L. Lan, “Real-time nonintrusive monitoring and prediction of driver fatigue,” IEEE Trans. Vehicular Technol. 53(4), 1052–1068 (2004).
[Crossref]

2003 (2)

P. P. Caffier, U. Erdmann, and P. Ullsperger, “Experimental evaluation of eye-blink parameters as a drowsiness measure,” Eur. J. Appl. Physiol. 89(3), 319–325 (2003).
[Crossref] [PubMed]

S. Coyle, T. Ward, and C. Markham, “Brain-computer interfaces: A review,” Interdiscip. Sci. Rev. 28(2), 112–118 (2003).
[Crossref]

1999 (1)

J. Horne and L. Reyner, “Vehicle accidents related to sleep: A review,” Occup. Environ. Med. 56(5), 289–294 (1999).
[Crossref] [PubMed]

Aarabi, A.

Arai, H.

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Aritake, S.

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

Arnaldi, B.

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” J. Neural Eng. 4(2), R1–R13 (2007).
[Crossref] [PubMed]

Auer, D. P.

C. Kaufmann, R. Wehrle, T. C. Wetter, F. Holsboer, D. P. Auer, T. Pollmächer, and M. Czisch, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study,” Brain 129(3), 655–667 (2006).
[Crossref] [PubMed]

Bakardjian, H.

Y. Tomita, F.-B. Vialatte, G. Dreyfus, Y. Mitsukura, H. Bakardjian, and A. Cichocki, “Bimodal BCI using simultaneously NIRS and EEG,” IEEE Trans. Biomed. Eng. 61(4), 1274–1284 (2014).
[Crossref] [PubMed]

Baker, W. B.

Barker, J. W.

Bekiaris, E.

B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, “Using EEG spectral components to assess algorithms for detecting fatigue,” Expert Syst. Appl. 36(2), 2352–2359 (2009).
[Crossref]

Benton, M. D.

F. X. Graydon, R. Young, M. D. Benton, R. J. Genik, S. Posse, L. Hsieh, and C. Green, “Visual event detection during simulated driving: Identifying the neural correlates with functional neuroimaging,” Transp. Res. Pt. F-Traffic Psychol. Behav. 7, 271–286 (2004).

Bhutta, M. R.

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: Three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

Birbaumer, N.

R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
[Crossref] [PubMed]

Blankertz, B.

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage 59(1), 519–529 (2012).
[Crossref] [PubMed]

S. Lemm, B. Blankertz, T. Dickhaus, and K. R. Müller, “Introduction to machine learning for brain imaging,” Neuroimage 56(2), 387–399 (2011).
[Crossref] [PubMed]

Boas, D. A.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

Bogler, C.

C. Bogler, J. Mehnert, J. Steinbrink, and J. D. Haynes, “Decoding vigilance with NIRS,” PLoS One 9(7), e101729 (2014).
[Crossref] [PubMed]

Bonnet, S.

R. N. Roy, S. Charbonnier, and S. Bonnet, “Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms,” Biomed. Signal Process. Control 14, 256–264 (2014).
[Crossref]

Busch, D. R.

Caffier, P. P.

P. P. Caffier, U. Erdmann, and P. Ullsperger, “Experimental evaluation of eye-blink parameters as a drowsiness measure,” Eur. J. Appl. Physiol. 89(3), 319–325 (2003).
[Crossref] [PubMed]

Caplier, A.

A. Picot, S. Charbonnier, and A. Caplier, “On-line detection of drowsiness using brain and visual information,” IEEE Trans. Syst. Man Cybern. A Syst. Hum. 42(3), 764–775 (2012).
[Crossref]

Charbonnier, S.

R. N. Roy, S. Charbonnier, and S. Bonnet, “Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms,” Biomed. Signal Process. Control 14, 256–264 (2014).
[Crossref]

A. Picot, S. Charbonnier, and A. Caplier, “On-line detection of drowsiness using brain and visual information,” IEEE Trans. Syst. Man Cybern. A Syst. Hum. 42(3), 764–775 (2012).
[Crossref]

Chau, T.

L. C. Schudlo and T. Chau, “Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest,” J. Neural Eng. 11(1), 016003 (2014).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS,” PLoS One 7(7), e37791 (2012).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: Differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
[Crossref] [PubMed]

Chen, Y. H.

C. T. Lin, C. H. Chuang, C. S. Huang, S. F. Tsai, S. W. Lu, Y. H. Chen, and L. W. Ko, “Wireless and wearable EEG system for evaluating driver vigilance,” IEEE Trans. Biomed. Circuits Syst. 8(2), 165–176 (2014).
[Crossref] [PubMed]

Chuang, C. H.

C. T. Lin, C. H. Chuang, C. S. Huang, S. F. Tsai, S. W. Lu, Y. H. Chen, and L. W. Ko, “Wireless and wearable EEG system for evaluating driver vigilance,” IEEE Trans. Biomed. Circuits Syst. 8(2), 165–176 (2014).
[Crossref] [PubMed]

Chung, W.-Y.

G. Li and W.-Y. Chung, “Estimation of eye closure degree using EEG sensors and its application in driver drowsiness detection,” Sensors (Basel) 14(9), 17491–17515 (2014).
[Crossref] [PubMed]

B.-G. Lee, B.-L. Lee, and W.-Y. Chung, “Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals,” Sensors (Basel) 14(10), 17915–17936 (2014).
[Crossref] [PubMed]

Cichocki, A.

Y. Tomita, F.-B. Vialatte, G. Dreyfus, Y. Mitsukura, H. Bakardjian, and A. Cichocki, “Bimodal BCI using simultaneously NIRS and EEG,” IEEE Trans. Biomed. Eng. 61(4), 1274–1284 (2014).
[Crossref] [PubMed]

Congedo, M.

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” J. Neural Eng. 4(2), R1–R13 (2007).
[Crossref] [PubMed]

Coyle, S.

S. Coyle, T. Ward, and C. Markham, “Brain-computer interfaces: A review,” Interdiscip. Sci. Rev. 28(2), 112–118 (2003).
[Crossref]

Coyle, S. M.

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
[Crossref] [PubMed]

Curio, G.

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage 59(1), 519–529 (2012).
[Crossref] [PubMed]

Czisch, M.

C. Kaufmann, R. Wehrle, T. C. Wetter, F. Holsboer, D. P. Auer, T. Pollmächer, and M. Czisch, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study,” Brain 129(3), 655–667 (2006).
[Crossref] [PubMed]

Dickhaus, T.

S. Lemm, B. Blankertz, T. Dickhaus, and K. R. Müller, “Introduction to machine learning for brain imaging,” Neuroimage 56(2), 387–399 (2011).
[Crossref] [PubMed]

Dreyfus, G.

Y. Tomita, F.-B. Vialatte, G. Dreyfus, Y. Mitsukura, H. Bakardjian, and A. Cichocki, “Bimodal BCI using simultaneously NIRS and EEG,” IEEE Trans. Biomed. Eng. 61(4), 1274–1284 (2014).
[Crossref] [PubMed]

Ebe, K.

Y. Uchiyama, H. Toyoda, H. Sakai, D. Shin, K. Ebe, and N. Sadato, “Suppression of brain activity related to a car-following task with an auditory task: An fMRI study,” Transp. Res. Pt. F-Traffic Psychol. Behav. 15, 25–37 (2012).

Elwell, C. E.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

Enomoto, M.

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

Erdmann, U.

P. P. Caffier, U. Erdmann, and P. Ullsperger, “Experimental evaluation of eye-blink parameters as a drowsiness measure,” Eur. J. Appl. Physiol. 89(3), 319–325 (2003).
[Crossref] [PubMed]

Fazli, S.

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage 59(1), 519–529 (2012).
[Crossref] [PubMed]

Ferrari, M.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

Fischer, P.

B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, “Using EEG spectral components to assess algorithms for detecting fatigue,” Expert Syst. Appl. 36(2), 2352–2359 (2009).
[Crossref]

Garcés Correa, A.

A. Garcés Correa, L. Orosco, and E. Laciar, “Automatic detection of drowsiness in EEG records based on multimodal analysis,” Med. Eng. Phys. 36(2), 244–249 (2014).
[Crossref] [PubMed]

Ge, S. S.

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
[Crossref] [PubMed]

X.-S. Hu, K.-S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
[Crossref] [PubMed]

Genik, R. J.

F. X. Graydon, R. Young, M. D. Benton, R. J. Genik, S. Posse, L. Hsieh, and C. Green, “Visual event detection during simulated driving: Identifying the neural correlates with functional neuroimaging,” Transp. Res. Pt. F-Traffic Psychol. Behav. 7, 271–286 (2004).

Gomez-Gil, J.

L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors (Basel) 12(2), 1211–1279 (2012).
[Crossref] [PubMed]

Graham, S. J.

T. A. Schweizer, K. Kan, Y. Hung, F. Tam, G. Naglie, and S. J. Graham, “Brain activity during driving with distraction: An immersive fMRI study,” Front. Hum. Neurosci. 7, 53 (2013).
[Crossref] [PubMed]

Graydon, F. X.

F. X. Graydon, R. Young, M. D. Benton, R. J. Genik, S. Posse, L. Hsieh, and C. Green, “Visual event detection during simulated driving: Identifying the neural correlates with functional neuroimaging,” Transp. Res. Pt. F-Traffic Psychol. Behav. 7, 271–286 (2004).

Green, C.

F. X. Graydon, R. Young, M. D. Benton, R. J. Genik, S. Posse, L. Hsieh, and C. Green, “Visual event detection during simulated driving: Identifying the neural correlates with functional neuroimaging,” Transp. Res. Pt. F-Traffic Psychol. Behav. 7, 271–286 (2004).

Greenberg, J. H.

Guan, C.

R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
[Crossref] [PubMed]

Han, C.-H.

H.-J. Hwang, D. Hwan Kim, C.-H. Han, and C.-H. Im, “A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface (BCI),” Brain Res. 1515, 66–77 (2013).
[Crossref] [PubMed]

Haynes, J. D.

C. Bogler, J. Mehnert, J. Steinbrink, and J. D. Haynes, “Decoding vigilance with NIRS,” PLoS One 9(7), e101729 (2014).
[Crossref] [PubMed]

Hida, A.

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

Higuchi, S.

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

Holsboer, F.

C. Kaufmann, R. Wehrle, T. C. Wetter, F. Holsboer, D. P. Auer, T. Pollmächer, and M. Czisch, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study,” Brain 129(3), 655–667 (2006).
[Crossref] [PubMed]

Hong, K.-S.

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near-infrared spectroscopy,” J. Near Infrared Spectrosc. 23(1), 23–31 (2015).
[Crossref]

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: A review,” Front. Hum. Neurosci. 9, 3 (2015).
[PubMed]

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
[Crossref] [PubMed]

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: Three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed. Opt. Express 5(6), 1778–1798 (2014).
[Crossref] [PubMed]

M. A. Kamran and K.-S. Hong, “Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: An fNIRS study,” J. Neural Eng. 10(5), 056002 (2013).
[Crossref] [PubMed]

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett. 553, 84–89 (2013).
[Crossref] [PubMed]

X.-S. Hu, K.-S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
[Crossref] [PubMed]

A. Turnip, K.-S. Hong, and M.-Y. Jeong, “Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis,” Biomed. Eng. Online 10(1), 83 (2011).
[Crossref] [PubMed]

Hong, M. J.

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: Three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
[Crossref] [PubMed]

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
[Crossref] [PubMed]

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

Horikawa, E.

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Horita, S.

Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
[Crossref] [PubMed]

Horne, J.

J. Horne and L. Reyner, “Vehicle accidents related to sleep: A review,” Occup. Environ. Med. 56(5), 289–294 (1999).
[Crossref] [PubMed]

Hoshi, Y.

R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
[Crossref] [PubMed]

Hsieh, L.

F. X. Graydon, R. Young, M. D. Benton, R. J. Genik, S. Posse, L. Hsieh, and C. Green, “Visual event detection during simulated driving: Identifying the neural correlates with functional neuroimaging,” Transp. Res. Pt. F-Traffic Psychol. Behav. 7, 271–286 (2004).

Hu, S.

S. Hu, G. Zheng, and B. Peters, “Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal,” IET Intell. Transp. Syst. 7(1), 105–113 (2013).
[Crossref]

Hu, X.-S.

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
[Crossref] [PubMed]

X.-S. Hu, K.-S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
[Crossref] [PubMed]

Huang, C. S.

C. T. Lin, C. H. Chuang, C. S. Huang, S. F. Tsai, S. W. Lu, Y. H. Chen, and L. W. Ko, “Wireless and wearable EEG system for evaluating driver vigilance,” IEEE Trans. Biomed. Circuits Syst. 8(2), 165–176 (2014).
[Crossref] [PubMed]

Hulshoff Pol, H. E.

M. P. van den Heuvel and H. E. Hulshoff Pol, “Exploring the brain network: A review on resting-state fMRI functional connectivity,” Eur. Neuropsychopharmacol. 20(8), 519–534 (2010).
[Crossref] [PubMed]

Hung, Y.

T. A. Schweizer, K. Kan, Y. Hung, F. Tam, G. Naglie, and S. J. Graham, “Brain activity during driving with distraction: An immersive fMRI study,” Front. Hum. Neurosci. 7, 53 (2013).
[Crossref] [PubMed]

Huppert, T. J.

Hwan Kim, D.

H.-J. Hwang, D. Hwan Kim, C.-H. Han, and C.-H. Im, “A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface (BCI),” Brain Res. 1515, 66–77 (2013).
[Crossref] [PubMed]

Hwang, H.-J.

H.-J. Hwang, D. Hwan Kim, C.-H. Han, and C.-H. Im, “A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface (BCI),” Brain Res. 1515, 66–77 (2013).
[Crossref] [PubMed]

Im, C.-H.

H.-J. Hwang, D. Hwan Kim, C.-H. Han, and C.-H. Im, “A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface (BCI),” Brain Res. 1515, 66–77 (2013).
[Crossref] [PubMed]

Innes, C. R. H.

G. R. Poudel, C. R. H. Innes, and R. D. Jones, “Cerebral perfusion differences between drowsy and nondrowsy individuals after acute sleep restriction,” Sleep 35(8), 1085–1096 (2012).
[PubMed]

Ishikawa, A.

R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
[Crossref] [PubMed]

Itoh, M.

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Jap, B. T.

B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, “Using EEG spectral components to assess algorithms for detecting fatigue,” Expert Syst. Appl. 36(2), 2352–2359 (2009).
[Crossref]

Jeong, M.-Y.

A. Turnip, K.-S. Hong, and M.-Y. Jeong, “Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis,” Biomed. Eng. Online 10(1), 83 (2011).
[Crossref] [PubMed]

Ji, Q.

Q. Ji, Z. W. Zhu, and P. L. Lan, “Real-time nonintrusive monitoring and prediction of driver fatigue,” IEEE Trans. Vehicular Technol. 53(4), 1052–1068 (2004).
[Crossref]

Jones, R. D.

G. R. Poudel, C. R. H. Innes, and R. D. Jones, “Cerebral perfusion differences between drowsy and nondrowsy individuals after acute sleep restriction,” Sleep 35(8), 1085–1096 (2012).
[PubMed]

Kamran, M. A.

M. A. Kamran and K.-S. Hong, “Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: An fNIRS study,” J. Neural Eng. 10(5), 056002 (2013).
[Crossref] [PubMed]

Kan, K.

T. A. Schweizer, K. Kan, Y. Hung, F. Tam, G. Naglie, and S. J. Graham, “Brain activity during driving with distraction: An immersive fMRI study,” Front. Hum. Neurosci. 7, 53 (2013).
[Crossref] [PubMed]

Kato, T.

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway,” Front. Hum. Neurosci. 7, 882 (2013).
[Crossref] [PubMed]

Kaufmann, C.

C. Kaufmann, R. Wehrle, T. C. Wetter, F. Holsboer, D. P. Auer, T. Pollmächer, and M. Czisch, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study,” Brain 129(3), 655–667 (2006).
[Crossref] [PubMed]

Khan, M. J.

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
[Crossref] [PubMed]

Kim, B.-M.

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: Three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

Kim, S.-P.

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

Kim, Y.-H.

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: Three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

Kitamura, S.

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

Ko, L. W.

C. T. Lin, C. H. Chuang, C. S. Huang, S. F. Tsai, S. W. Lu, Y. H. Chen, and L. W. Ko, “Wireless and wearable EEG system for evaluating driver vigilance,” IEEE Trans. Biomed. Circuits Syst. 8(2), 165–176 (2014).
[Crossref] [PubMed]

Kondo, M.

Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
[Crossref] [PubMed]

Kothe, C.

T. O. Zander and C. Kothe, “Towards passive brain-computer interfaces: Applying brain-computer interface technology to human-machine systems in general,” J. Neural Eng. 8(2), 025005 (2011).
[Crossref] [PubMed]

Kubota, Y.

Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
[Crossref] [PubMed]

Kuriyama, K.

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

Kushki, A.

S. D. Power, A. Kushki, and T. Chau, “Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS,” PLoS One 7(7), e37791 (2012).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: Differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
[Crossref] [PubMed]

Laciar, E.

A. Garcés Correa, L. Orosco, and E. Laciar, “Automatic detection of drowsiness in EEG records based on multimodal analysis,” Med. Eng. Phys. 36(2), 244–249 (2014).
[Crossref] [PubMed]

Lal, S.

B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, “Using EEG spectral components to assess algorithms for detecting fatigue,” Expert Syst. Appl. 36(2), 2352–2359 (2009).
[Crossref]

Lamarche, F.

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” J. Neural Eng. 4(2), R1–R13 (2007).
[Crossref] [PubMed]

Lan, P. L.

Q. Ji, Z. W. Zhu, and P. L. Lan, “Real-time nonintrusive monitoring and prediction of driver fatigue,” IEEE Trans. Vehicular Technol. 53(4), 1052–1068 (2004).
[Crossref]

Lécuyer, A.

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” J. Neural Eng. 4(2), R1–R13 (2007).
[Crossref] [PubMed]

Lee, B.-G.

B.-G. Lee, B.-L. Lee, and W.-Y. Chung, “Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals,” Sensors (Basel) 14(10), 17915–17936 (2014).
[Crossref] [PubMed]

Lee, B.-L.

B.-G. Lee, B.-L. Lee, and W.-Y. Chung, “Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals,” Sensors (Basel) 14(10), 17915–17936 (2014).
[Crossref] [PubMed]

Lee, S.-H.

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: Three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

Lemm, S.

S. Lemm, B. Blankertz, T. Dickhaus, and K. R. Müller, “Introduction to machine learning for brain imaging,” Neuroimage 56(2), 387–399 (2011).
[Crossref] [PubMed]

Li, G.

G. Li and W.-Y. Chung, “Estimation of eye closure degree using EEG sensors and its application in driver drowsiness detection,” Sensors (Basel) 14(9), 17491–17515 (2014).
[Crossref] [PubMed]

Li, J.

Lin, C. T.

C. T. Lin, C. H. Chuang, C. S. Huang, S. F. Tsai, S. W. Lu, Y. H. Chen, and L. W. Ko, “Wireless and wearable EEG system for evaluating driver vigilance,” IEEE Trans. Biomed. Circuits Syst. 8(2), 165–176 (2014).
[Crossref] [PubMed]

Liu, T.

T. Liu, “Positive correlation between drowsiness and prefrontal activation during a simulated speed-control driving task,” Neuroreport 25(16), 1316–1319 (2014).
[Crossref] [PubMed]

Logothetis, N. K.

N. K. Logothetis, “What we can do and what we cannot do with fMRI,” Nature 453(7197), 869–878 (2008).
[Crossref] [PubMed]

Lotte, F.

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” J. Neural Eng. 4(2), R1–R13 (2007).
[Crossref] [PubMed]

Lu, S. W.

C. T. Lin, C. H. Chuang, C. S. Huang, S. F. Tsai, S. W. Lu, Y. H. Chen, and L. W. Ko, “Wireless and wearable EEG system for evaluating driver vigilance,” IEEE Trans. Biomed. Circuits Syst. 8(2), 165–176 (2014).
[Crossref] [PubMed]

Markham, C.

S. Coyle, T. Ward, and C. Markham, “Brain-computer interfaces: A review,” Interdiscip. Sci. Rev. 28(2), 112–118 (2003).
[Crossref]

Markham, C. M.

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
[Crossref] [PubMed]

Maruyama, M.

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Mayhew, D.

W. Vanlaar, H. Simpson, D. Mayhew, and R. Robertson, “Fatigued and drowsy driving: A survey of attitudes, opinions and behaviors,” J. Safety Res. 39(3), 303–309 (2008).
[Crossref] [PubMed]

Mehnert, J.

C. Bogler, J. Mehnert, J. Steinbrink, and J. D. Haynes, “Decoding vigilance with NIRS,” PLoS One 9(7), e101729 (2014).
[Crossref] [PubMed]

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage 59(1), 519–529 (2012).
[Crossref] [PubMed]

Mesquita, R. C.

Mishima, K.

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

Mitsukura, Y.

Y. Tomita, F.-B. Vialatte, G. Dreyfus, Y. Mitsukura, H. Bakardjian, and A. Cichocki, “Bimodal BCI using simultaneously NIRS and EEG,” IEEE Trans. Biomed. Eng. 61(4), 1274–1284 (2014).
[Crossref] [PubMed]

Müller, K. R.

S. Lemm, B. Blankertz, T. Dickhaus, and K. R. Müller, “Introduction to machine learning for brain imaging,” Neuroimage 56(2), 387–399 (2011).
[Crossref] [PubMed]

Müller, K.-R.

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage 59(1), 519–529 (2012).
[Crossref] [PubMed]

Naglie, G.

T. A. Schweizer, K. Kan, Y. Hung, F. Tam, G. Naglie, and S. J. Graham, “Brain activity during driving with distraction: An immersive fMRI study,” Front. Hum. Neurosci. 7, 53 (2013).
[Crossref] [PubMed]

Naseer, N.

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: A review,” Front. Hum. Neurosci. 9, 3 (2015).
[PubMed]

N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near-infrared spectroscopy,” J. Near Infrared Spectrosc. 23(1), 23–31 (2015).
[Crossref]

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett. 553, 84–89 (2013).
[Crossref] [PubMed]

Nguyen, H.-D.

Nicolas-Alonso, L. F.

L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors (Basel) 12(2), 1211–1279 (2012).
[Crossref] [PubMed]

Oka, N.

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway,” Front. Hum. Neurosci. 7, 882 (2013).
[Crossref] [PubMed]

Okada, T.

Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
[Crossref] [PubMed]

Okamura, N.

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Orosco, L.

A. Garcés Correa, L. Orosco, and E. Laciar, “Automatic detection of drowsiness in EEG records based on multimodal analysis,” Med. Eng. Phys. 36(2), 244–249 (2014).
[Crossref] [PubMed]

Parthasarathy, A. B.

Peters, B.

S. Hu, G. Zheng, and B. Peters, “Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal,” IET Intell. Transp. Syst. 7(1), 105–113 (2013).
[Crossref]

Picot, A.

A. Picot, S. Charbonnier, and A. Caplier, “On-line detection of drowsiness using brain and visual information,” IEEE Trans. Syst. Man Cybern. A Syst. Hum. 42(3), 764–775 (2012).
[Crossref]

Pollmächer, T.

C. Kaufmann, R. Wehrle, T. C. Wetter, F. Holsboer, D. P. Auer, T. Pollmächer, and M. Czisch, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study,” Brain 129(3), 655–667 (2006).
[Crossref] [PubMed]

Posse, S.

F. X. Graydon, R. Young, M. D. Benton, R. J. Genik, S. Posse, L. Hsieh, and C. Green, “Visual event detection during simulated driving: Identifying the neural correlates with functional neuroimaging,” Transp. Res. Pt. F-Traffic Psychol. Behav. 7, 271–286 (2004).

Poudel, G. R.

G. R. Poudel, C. R. H. Innes, and R. D. Jones, “Cerebral perfusion differences between drowsy and nondrowsy individuals after acute sleep restriction,” Sleep 35(8), 1085–1096 (2012).
[PubMed]

Power, S. D.

S. D. Power, A. Kushki, and T. Chau, “Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS,” PLoS One 7(7), e37791 (2012).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: Differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
[Crossref] [PubMed]

Qiu, L.

Qu, H.

J. Wang, Y. Y. Wu, H. Qu, and G. H. Xu, “EEG-based fatigue driving detection using correlation dimension,” J. Vibroeng. 16, 407–413 (2014).

Quaresima, V.

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

Reyner, L.

J. Horne and L. Reyner, “Vehicle accidents related to sleep: A review,” Occup. Environ. Med. 56(5), 289–294 (1999).
[Crossref] [PubMed]

Robertson, R.

W. Vanlaar, H. Simpson, D. Mayhew, and R. Robertson, “Fatigued and drowsy driving: A survey of attitudes, opinions and behaviors,” J. Safety Res. 39(3), 303–309 (2008).
[Crossref] [PubMed]

Rosseel, Y.

M. Welvaert and Y. Rosseel, “A review of fMRI simulation studies,” PLoS One 9(7), e101953 (2014).
[Crossref] [PubMed]

Roy, R. N.

R. N. Roy, S. Charbonnier, and S. Bonnet, “Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms,” Biomed. Signal Process. Control 14, 256–264 (2014).
[Crossref]

Sadato, N.

Y. Uchiyama, H. Toyoda, H. Sakai, D. Shin, K. Ebe, and N. Sadato, “Suppression of brain activity related to a car-following task with an auditory task: An fMRI study,” Transp. Res. Pt. F-Traffic Psychol. Behav. 15, 25–37 (2012).

Sakai, H.

Y. Uchiyama, H. Toyoda, H. Sakai, D. Shin, K. Ebe, and N. Sadato, “Suppression of brain activity related to a car-following task with an auditory task: An fMRI study,” Transp. Res. Pt. F-Traffic Psychol. Behav. 15, 25–37 (2012).

Sakurada, Y.

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Santosa, H.

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

Sasaki, H.

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Scheutz, M.

M. Strait and M. Scheutz, “What we can and cannot (yet) do with functional near infrared spectroscopy,” Front. Neurosci. 8, 117 (2014).
[Crossref] [PubMed]

Schudlo, L. C.

L. C. Schudlo and T. Chau, “Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest,” J. Neural Eng. 11(1), 016003 (2014).
[Crossref] [PubMed]

Schweizer, T. A.

T. A. Schweizer, K. Kan, Y. Hung, F. Tam, G. Naglie, and S. J. Graham, “Brain activity during driving with distraction: An immersive fMRI study,” Front. Hum. Neurosci. 7, 53 (2013).
[Crossref] [PubMed]

Shimizu, K.

R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
[Crossref] [PubMed]

Shimizu, M.

Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
[Crossref] [PubMed]

Shin, D.

Y. Uchiyama, H. Toyoda, H. Sakai, D. Shin, K. Ebe, and N. Sadato, “Suppression of brain activity related to a car-following task with an auditory task: An fMRI study,” Transp. Res. Pt. F-Traffic Psychol. Behav. 15, 25–37 (2012).

Simpson, H.

W. Vanlaar, H. Simpson, D. Mayhew, and R. Robertson, “Fatigued and drowsy driving: A survey of attitudes, opinions and behaviors,” J. Safety Res. 39(3), 303–309 (2008).
[Crossref] [PubMed]

Sitaram, R.

R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
[Crossref] [PubMed]

Soshi, T.

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

Steinbrink, J.

C. Bogler, J. Mehnert, J. Steinbrink, and J. D. Haynes, “Decoding vigilance with NIRS,” PLoS One 9(7), e101729 (2014).
[Crossref] [PubMed]

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage 59(1), 519–529 (2012).
[Crossref] [PubMed]

Strait, M.

M. Strait and M. Scheutz, “What we can and cannot (yet) do with functional near infrared spectroscopy,” Front. Neurosci. 8, 117 (2014).
[Crossref] [PubMed]

Suzuki, H.

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

Taga, G.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

Takahashi, H.

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway,” Front. Hum. Neurosci. 7, 882 (2013).
[Crossref] [PubMed]

Takasu, N. N.

Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
[Crossref] [PubMed]

Tam, F.

T. A. Schweizer, K. Kan, Y. Hung, F. Tam, G. Naglie, and S. J. Graham, “Brain activity during driving with distraction: An immersive fMRI study,” Front. Hum. Neurosci. 7, 53 (2013).
[Crossref] [PubMed]

Tashiro, M.

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Thulasidas, M.

R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
[Crossref] [PubMed]

Toichi, M.

Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
[Crossref] [PubMed]

Tomita, Y.

Y. Tomita, F.-B. Vialatte, G. Dreyfus, Y. Mitsukura, H. Bakardjian, and A. Cichocki, “Bimodal BCI using simultaneously NIRS and EEG,” IEEE Trans. Biomed. Eng. 61(4), 1274–1284 (2014).
[Crossref] [PubMed]

Toyoda, H.

Y. Uchiyama, H. Toyoda, H. Sakai, D. Shin, K. Ebe, and N. Sadato, “Suppression of brain activity related to a car-following task with an auditory task: An fMRI study,” Transp. Res. Pt. F-Traffic Psychol. Behav. 15, 25–37 (2012).

Tsai, S. F.

C. T. Lin, C. H. Chuang, C. S. Huang, S. F. Tsai, S. W. Lu, Y. H. Chen, and L. W. Ko, “Wireless and wearable EEG system for evaluating driver vigilance,” IEEE Trans. Biomed. Circuits Syst. 8(2), 165–176 (2014).
[Crossref] [PubMed]

Turnip, A.

A. Turnip, K.-S. Hong, and M.-Y. Jeong, “Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis,” Biomed. Eng. Online 10(1), 83 (2011).
[Crossref] [PubMed]

Uchiyama, Y.

Y. Uchiyama, H. Toyoda, H. Sakai, D. Shin, K. Ebe, and N. Sadato, “Suppression of brain activity related to a car-following task with an auditory task: An fMRI study,” Transp. Res. Pt. F-Traffic Psychol. Behav. 15, 25–37 (2012).

Ullsperger, P.

P. P. Caffier, U. Erdmann, and P. Ullsperger, “Experimental evaluation of eye-blink parameters as a drowsiness measure,” Eur. J. Appl. Physiol. 89(3), 319–325 (2003).
[Crossref] [PubMed]

van den Heuvel, M. P.

M. P. van den Heuvel and H. E. Hulshoff Pol, “Exploring the brain network: A review on resting-state fMRI functional connectivity,” Eur. Neuropsychopharmacol. 20(8), 519–534 (2010).
[Crossref] [PubMed]

Vanlaar, W.

W. Vanlaar, H. Simpson, D. Mayhew, and R. Robertson, “Fatigued and drowsy driving: A survey of attitudes, opinions and behaviors,” J. Safety Res. 39(3), 303–309 (2008).
[Crossref] [PubMed]

Vialatte, F.-B.

Y. Tomita, F.-B. Vialatte, G. Dreyfus, Y. Mitsukura, H. Bakardjian, and A. Cichocki, “Bimodal BCI using simultaneously NIRS and EEG,” IEEE Trans. Biomed. Eng. 61(4), 1274–1284 (2014).
[Crossref] [PubMed]

Villringer, A.

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage 59(1), 519–529 (2012).
[Crossref] [PubMed]

Wakamura, T.

Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
[Crossref] [PubMed]

Wang, J.

J. Wang, Y. Y. Wu, H. Qu, and G. H. Xu, “EEG-based fatigue driving detection using correlation dimension,” J. Vibroeng. 16, 407–413 (2014).

Ward, T.

S. Coyle, T. Ward, and C. Markham, “Brain-computer interfaces: A review,” Interdiscip. Sci. Rev. 28(2), 112–118 (2003).
[Crossref]

Ward, T. E.

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
[Crossref] [PubMed]

Wehrle, R.

C. Kaufmann, R. Wehrle, T. C. Wetter, F. Holsboer, D. P. Auer, T. Pollmächer, and M. Czisch, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study,” Brain 129(3), 655–667 (2006).
[Crossref] [PubMed]

Welvaert, M.

M. Welvaert and Y. Rosseel, “A review of fMRI simulation studies,” PLoS One 9(7), e101953 (2014).
[Crossref] [PubMed]

Wetter, T. C.

C. Kaufmann, R. Wehrle, T. C. Wetter, F. Holsboer, D. P. Auer, T. Pollmächer, and M. Czisch, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study,” Brain 129(3), 655–667 (2006).
[Crossref] [PubMed]

Wu, Y. Y.

J. Wang, Y. Y. Wu, H. Qu, and G. H. Xu, “EEG-based fatigue driving detection using correlation dimension,” J. Vibroeng. 16, 407–413 (2014).

Xu, G. H.

J. Wang, Y. Y. Wu, H. Qu, and G. H. Xu, “EEG-based fatigue driving detection using correlation dimension,” J. Vibroeng. 16, 407–413 (2014).

Yamaguchi, K.

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Yamamoto, K.

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway,” Front. Hum. Neurosci. 7, 882 (2013).
[Crossref] [PubMed]

Yanai, K.

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Yodh, A. G.

Yoshino, K.

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway,” Front. Hum. Neurosci. 7, 882 (2013).
[Crossref] [PubMed]

Young, R.

F. X. Graydon, R. Young, M. D. Benton, R. J. Genik, S. Posse, L. Hsieh, and C. Green, “Visual event detection during simulated driving: Identifying the neural correlates with functional neuroimaging,” Transp. Res. Pt. F-Traffic Psychol. Behav. 7, 271–286 (2004).

Zander, T. O.

T. O. Zander and C. Kothe, “Towards passive brain-computer interfaces: Applying brain-computer interface technology to human-machine systems in general,” J. Neural Eng. 8(2), 025005 (2011).
[Crossref] [PubMed]

Zhang, H.

R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
[Crossref] [PubMed]

Zheng, G.

S. Hu, G. Zheng, and B. Peters, “Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal,” IET Intell. Transp. Syst. 7(1), 105–113 (2013).
[Crossref]

Zhu, Z. W.

Q. Ji, Z. W. Zhu, and P. L. Lan, “Real-time nonintrusive monitoring and prediction of driver fatigue,” IEEE Trans. Vehicular Technol. 53(4), 1052–1068 (2004).
[Crossref]

Biomed. Eng. Online (1)

A. Turnip, K.-S. Hong, and M.-Y. Jeong, “Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis,” Biomed. Eng. Online 10(1), 83 (2011).
[Crossref] [PubMed]

Biomed. Opt. Express (4)

Biomed. Signal Process. Control (1)

R. N. Roy, S. Charbonnier, and S. Bonnet, “Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms,” Biomed. Signal Process. Control 14, 256–264 (2014).
[Crossref]

BMC Neurosci. (1)

S. Aritake, S. Higuchi, H. Suzuki, K. Kuriyama, M. Enomoto, T. Soshi, S. Kitamura, A. Hida, and K. Mishima, “Increased cerebral blood flow in the right frontal lobe area during sleep precedes self-awakening in humans,” BMC Neurosci. 13(1), 153 (2012).
[Crossref] [PubMed]

Brain (1)

C. Kaufmann, R. Wehrle, T. C. Wetter, F. Holsboer, D. P. Auer, T. Pollmächer, and M. Czisch, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study,” Brain 129(3), 655–667 (2006).
[Crossref] [PubMed]

Brain Cogn. (1)

E. Horikawa, N. Okamura, M. Tashiro, Y. Sakurada, M. Maruyama, H. Arai, K. Yamaguchi, H. Sasaki, K. Yanai, and M. Itoh, “The neural correlates of driving performance identified using positron emission tomography,” Brain Cogn. 58(2), 166–171 (2005).
[Crossref] [PubMed]

Brain Res. (2)

Y. Kubota, N. N. Takasu, S. Horita, M. Kondo, M. Shimizu, T. Okada, T. Wakamura, and M. Toichi, “Dorsolateral prefrontal cortical oxygenation during REM sleep in humans,” Brain Res. 1389, 83–92 (2011).
[Crossref] [PubMed]

H.-J. Hwang, D. Hwan Kim, C.-H. Han, and C.-H. Im, “A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface (BCI),” Brain Res. 1515, 66–77 (2013).
[Crossref] [PubMed]

Eur. J. Appl. Physiol. (1)

P. P. Caffier, U. Erdmann, and P. Ullsperger, “Experimental evaluation of eye-blink parameters as a drowsiness measure,” Eur. J. Appl. Physiol. 89(3), 319–325 (2003).
[Crossref] [PubMed]

Eur. Neuropsychopharmacol. (1)

M. P. van den Heuvel and H. E. Hulshoff Pol, “Exploring the brain network: A review on resting-state fMRI functional connectivity,” Eur. Neuropsychopharmacol. 20(8), 519–534 (2010).
[Crossref] [PubMed]

Exp. Brain Res. (1)

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res. 232(2), 555–564 (2014).
[Crossref] [PubMed]

Expert Syst. Appl. (1)

B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, “Using EEG spectral components to assess algorithms for detecting fatigue,” Expert Syst. Appl. 36(2), 2352–2359 (2009).
[Crossref]

Front. Hum. Neurosci. (4)

K. Yoshino, N. Oka, K. Yamamoto, H. Takahashi, and T. Kato, “Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway,” Front. Hum. Neurosci. 7, 882 (2013).
[Crossref] [PubMed]

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci. 8, 244 (2014).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: A review,” Front. Hum. Neurosci. 9, 3 (2015).
[PubMed]

T. A. Schweizer, K. Kan, Y. Hung, F. Tam, G. Naglie, and S. J. Graham, “Brain activity during driving with distraction: An immersive fMRI study,” Front. Hum. Neurosci. 7, 53 (2013).
[Crossref] [PubMed]

Front. Neurosci. (1)

M. Strait and M. Scheutz, “What we can and cannot (yet) do with functional near infrared spectroscopy,” Front. Neurosci. 8, 117 (2014).
[Crossref] [PubMed]

Front. Psychol. (1)

M. R. Bhutta, M. J. Hong, Y.-H. Kim, and K.-S. Hong, “Single-trial lie detection using a combined fNIRS-polygraph system,” Front. Psychol. 6, 709 (2015).
[Crossref] [PubMed]

IEEE Trans. Biomed. Circuits Syst. (1)

C. T. Lin, C. H. Chuang, C. S. Huang, S. F. Tsai, S. W. Lu, Y. H. Chen, and L. W. Ko, “Wireless and wearable EEG system for evaluating driver vigilance,” IEEE Trans. Biomed. Circuits Syst. 8(2), 165–176 (2014).
[Crossref] [PubMed]

IEEE Trans. Biomed. Eng. (1)

Y. Tomita, F.-B. Vialatte, G. Dreyfus, Y. Mitsukura, H. Bakardjian, and A. Cichocki, “Bimodal BCI using simultaneously NIRS and EEG,” IEEE Trans. Biomed. Eng. 61(4), 1274–1284 (2014).
[Crossref] [PubMed]

IEEE Trans. Syst. Man Cybern. A Syst. Hum. (1)

A. Picot, S. Charbonnier, and A. Caplier, “On-line detection of drowsiness using brain and visual information,” IEEE Trans. Syst. Man Cybern. A Syst. Hum. 42(3), 764–775 (2012).
[Crossref]

IEEE Trans. Vehicular Technol. (1)

Q. Ji, Z. W. Zhu, and P. L. Lan, “Real-time nonintrusive monitoring and prediction of driver fatigue,” IEEE Trans. Vehicular Technol. 53(4), 1052–1068 (2004).
[Crossref]

IET Intell. Transp. Syst. (1)

S. Hu, G. Zheng, and B. Peters, “Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal,” IET Intell. Transp. Syst. 7(1), 105–113 (2013).
[Crossref]

Interdiscip. Sci. Rev. (1)

S. Coyle, T. Ward, and C. Markham, “Brain-computer interfaces: A review,” Interdiscip. Sci. Rev. 28(2), 112–118 (2003).
[Crossref]

J. Biomed. Opt. (1)

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt. 18(1), 017003 (2013).
[Crossref] [PubMed]

J. Near Infrared Spectrosc. (1)

N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near-infrared spectroscopy,” J. Near Infrared Spectrosc. 23(1), 23–31 (2015).
[Crossref]

J. Neural Eng. (7)

M. A. Kamran and K.-S. Hong, “Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: An fNIRS study,” J. Neural Eng. 10(5), 056002 (2013).
[Crossref] [PubMed]

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” J. Neural Eng. 4(2), R1–R13 (2007).
[Crossref] [PubMed]

T. O. Zander and C. Kothe, “Towards passive brain-computer interfaces: Applying brain-computer interface technology to human-machine systems in general,” J. Neural Eng. 8(2), 025005 (2011).
[Crossref] [PubMed]

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
[Crossref] [PubMed]

L. C. Schudlo and T. Chau, “Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest,” J. Neural Eng. 11(1), 016003 (2014).
[Crossref] [PubMed]

S. D. Power, A. Kushki, and T. Chau, “Towards a system-paced near-infrared spectroscopy brain-computer interface: Differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state,” J. Neural Eng. 8(6), 066004 (2011).
[Crossref] [PubMed]

X.-S. Hu, K.-S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
[Crossref] [PubMed]

J. Safety Res. (1)

W. Vanlaar, H. Simpson, D. Mayhew, and R. Robertson, “Fatigued and drowsy driving: A survey of attitudes, opinions and behaviors,” J. Safety Res. 39(3), 303–309 (2008).
[Crossref] [PubMed]

J. Vibroeng. (1)

J. Wang, Y. Y. Wu, H. Qu, and G. H. Xu, “EEG-based fatigue driving detection using correlation dimension,” J. Vibroeng. 16, 407–413 (2014).

Med. Eng. Phys. (1)

A. Garcés Correa, L. Orosco, and E. Laciar, “Automatic detection of drowsiness in EEG records based on multimodal analysis,” Med. Eng. Phys. 36(2), 244–249 (2014).
[Crossref] [PubMed]

Nature (1)

N. K. Logothetis, “What we can do and what we cannot do with fMRI,” Nature 453(7197), 869–878 (2008).
[Crossref] [PubMed]

Neuroimage (5)

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: Introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

R. Sitaram, H. Zhang, C. Guan, M. Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, and N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface,” Neuroimage 34(4), 1416–1427 (2007).
[Crossref] [PubMed]

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage 59(1), 519–529 (2012).
[Crossref] [PubMed]

S. Lemm, B. Blankertz, T. Dickhaus, and K. R. Müller, “Introduction to machine learning for brain imaging,” Neuroimage 56(2), 387–399 (2011).
[Crossref] [PubMed]

Neuroreport (1)

T. Liu, “Positive correlation between drowsiness and prefrontal activation during a simulated speed-control driving task,” Neuroreport 25(16), 1316–1319 (2014).
[Crossref] [PubMed]

Neurosci. Lett. (2)

N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett. 553, 84–89 (2013).
[Crossref] [PubMed]

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI,” Neurosci. Lett. 587, 87–92 (2015).
[Crossref] [PubMed]

Occup. Environ. Med. (1)

J. Horne and L. Reyner, “Vehicle accidents related to sleep: A review,” Occup. Environ. Med. 56(5), 289–294 (1999).
[Crossref] [PubMed]

PLoS One (3)

S. D. Power, A. Kushki, and T. Chau, “Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS,” PLoS One 7(7), e37791 (2012).
[Crossref] [PubMed]

M. Welvaert and Y. Rosseel, “A review of fMRI simulation studies,” PLoS One 9(7), e101953 (2014).
[Crossref] [PubMed]

C. Bogler, J. Mehnert, J. Steinbrink, and J. D. Haynes, “Decoding vigilance with NIRS,” PLoS One 9(7), e101729 (2014).
[Crossref] [PubMed]

Rev. Sci. Instrum. (2)

H. Santosa, M. J. Hong, S.-P. Kim, and K.-S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013).
[Crossref] [PubMed]

M. R. Bhutta, K.-S. Hong, B.-M. Kim, M. J. Hong, Y.-H. Kim, and S.-H. Lee, “Note: Three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum. 85(2), 026111 (2014).
[Crossref] [PubMed]

Sensors (Basel) (3)

B.-G. Lee, B.-L. Lee, and W.-Y. Chung, “Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals,” Sensors (Basel) 14(10), 17915–17936 (2014).
[Crossref] [PubMed]

G. Li and W.-Y. Chung, “Estimation of eye closure degree using EEG sensors and its application in driver drowsiness detection,” Sensors (Basel) 14(9), 17491–17515 (2014).
[Crossref] [PubMed]

L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review,” Sensors (Basel) 12(2), 1211–1279 (2012).
[Crossref] [PubMed]

Sleep (1)

G. R. Poudel, C. R. H. Innes, and R. D. Jones, “Cerebral perfusion differences between drowsy and nondrowsy individuals after acute sleep restriction,” Sleep 35(8), 1085–1096 (2012).
[PubMed]

Transp. Res. Pt. F-Traffic Psychol. Behav. (2)

F. X. Graydon, R. Young, M. D. Benton, R. J. Genik, S. Posse, L. Hsieh, and C. Green, “Visual event detection during simulated driving: Identifying the neural correlates with functional neuroimaging,” Transp. Res. Pt. F-Traffic Psychol. Behav. 7, 271–286 (2004).

Y. Uchiyama, H. Toyoda, H. Sakai, D. Shin, K. Ebe, and N. Sadato, “Suppression of brain activity related to a car-following task with an auditory task: An fMRI study,” Transp. Res. Pt. F-Traffic Psychol. Behav. 15, 25–37 (2012).

Other (2)

H. Santosa, M. J. Hong, and K.-S. Hong, “Lateralization of music processing auditory cortex: An fNIRS study,” Front. Behav. Neurosci. 8, UNSP 418 (2014).

S. Fazli, J. Mehnert, J. Steinbrink, and B. Blankertz, “Using NIRS as a predictor for EEG-based BCI performance,” in Proceedings of IEEE Conference of Engineering in Medicine and Biology Society (IEEE EMBC, 2012), pp. 4911–4914.
[Crossref]

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (7)

Fig. 1
Fig. 1 The experimental scheme used for drowsiness detection.
Fig. 2
Fig. 2 The placement of optodes over the prefrontal and dorsolateral prefrontal cortex regions.
Fig. 3
Fig. 3 ∆HbO and ∆HbR changes in the prefrontal and dorsolateral prefrontal brain regions (Subject 3): Region A consists of channels 1~8, region B channels 9~20, and region C channels 21~28.
Fig. 4
Fig. 4 Comparison of the regional averages in A, B, and C showing the drowsy and alert states (Subject 3).
Fig. 5
Fig. 5 HbX corresponding to the drowsy state (Subs. 1, 2, 4 and 6; see Fig. 4 for Sub. 3, Sub. 5 is omitted)
Fig. 6
Fig. 6 28 2-class feature spaces combining the mean ∆HbO, mean ∆HbR, skewness, kurtosis, slope, number of peaks, sum of peaks and signal peak for separating the drowsy and non-drowsy states: The red triangle represents the non-drowsy (alert) state and the blue circle represents the drowsy state (Subject 2, region A, 0~5 sec time window).
Fig. 7
Fig. 7 (a) The overall average of the classification accuracies over 13 subjects in different time windows; (b) the individual average accuracies and standard deviations of 13 subjects in regions A, B, and C; (c) the channel-wise number of occurrence of the drowsy state over 13 subjects (i.e., four subjects showed the drowsy state in Ch. 1).

Tables (9)

Tables Icon

Table 1 Eight features in region A (before normalization): 0~5 sec window

Tables Icon

Table 2 Eight features in region A (before normalization): 0~10 sec window

Tables Icon

Table 3 Eight features in region A (before normalization): 0~15 sec window

Tables Icon

Table 4 Difference of the feature values between the drowsy and alert states (region B, 5 sec window)

Tables Icon

Table 5 Difference of the feature values between the drowsy and alert states (region C, 5 sec window)

Tables Icon

Table 6 Accuracies obtained by a combination of two features (0~15 sec window, Subject 2)

Tables Icon

Table 7 Averaged accuracies over all the subjects (0~15 sec window)

Tables Icon

Table 8 Classification accuracies (%) in three different brain regions

Tables Icon

Table 9 Performance comparison of two classical classifiers (for region A)

Equations (10)

Equations on this page are rendered with MathJax. Learn more.

A(t;λ)=ln I in (λ) I out (t;λ) =α(λ)×c(λ)×l×d(λ)+η,
[ Δ c HbO (t) Δ c HbR (t) ]= [ α HbO ( λ 1 ) α HbR ( λ 1 ) α HbO ( λ 2 ) α HbR ( λ 2 ) ] 1 [ ΔA(t; λ 1 ) ΔA(t; λ 2 ) ] 1 l×d(λ) ,
M k = 1 N j=1 window k X j ,
ske w k = E (X M k ) 3 σ 3 ,
kur t k = E (X M k ) 4 σ 4 ,
a = amin a max amin a
μ i = 1 n i xclass i x ,μ= 1 n l x l ,
J(V)= det( V T S B V) det( V T S W V) ,
S B = i=1 m n i ( μ i μ) ( μ i μ) T ,
S W = i=1 m x l classi ( x l μ i ) ( x l μ i ) T ,

Metrics