Abstract

Previous studies reported mental stress as one of the major contributing factors leading to various diseases such as heart attack, depression and stroke. An accurate stress assessment method may thus be of importance to clinical intervention and disease prevention. We propose a joint independent component analysis (jICA) based approach to fuse simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) on the prefrontal cortex (PFC) as a means of stress assessment. For the purpose of this study, stress was induced by using an established mental arithmetic task under time pressure with negative feedback. The induction of mental stress was confirmed by salivary alpha amylase test. Experiment results showed that the proposed fusion of EEG and fNIRS measurements improves the classification accuracy of mental stress by +3.4% compared to EEG alone and +11% compared to fNIRS alone. Similar improvements were also observed in sensitivity and specificity of proposed approach over unimodal EEG/fNIRS. Our study suggests that combination of EEG (frontal alpha rhythm) and fNIRS (concentration change of oxygenated hemoglobin) could be a potential means to assess mental stress objectively.

© 2016 Optical Society of America

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  101. Y. Blokland, L. Spyrou, D. Thijssen, T. Eijsvogels, W. Colier, M. Floor-Westerdijk, R. Vlek, J. Bruhn, and J. Farquhar, “Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia,” IEEE Trans. Neural Syst. Rehabil. Eng. 22(2), 222–229 (2014).
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  102. X. Yin, B. Xu, C. Jiang, Y. Fu, Z. Wang, H. Li, and G. Shi, “A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching,” J. Neural Eng. 12(3), 036004 (2015).
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  103. I. Tachtsidis and F. Scholkmann, “False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward,” Neurophotonics 3(3), 031405 (2016).
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  104. T. Katura, H. Sato, Y. Fuchino, T. Yoshida, H. Atsumori, M. Kiguchi, A. Maki, M. Abe, and N. Tanaka, “Extracting task-related activation components from optical topography measurement using independent components analysis,” J. Biomed. Opt. 13(5), 054008 (2008).
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2016 (4)

A. Alberdi, A. Aztiria, and A. Basarab, “Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review,” J. Biomed. Inform. 59, 49–75 (2016).
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S. Sutoko, H. Sato, A. Maki, M. Kiguchi, Y. Hirabayashi, H. Atsumori, A. Obata, T. Funane, and T. Katura, “Tutorial on platform for optical topography analysis tools,” Neurophotonics 3(1), 010801 (2016).
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T. J. Huppert, “Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy,” Neurophotonics 3(1), 010401 (2016).
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I. Tachtsidis and F. Scholkmann, “False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward,” Neurophotonics 3(3), 031405 (2016).
[Crossref] [PubMed]

2015 (7)

X. Yin, B. Xu, C. Jiang, Y. Fu, Z. Wang, H. Li, and G. Shi, “A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching,” J. Neural Eng. 12(3), 036004 (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(1), 87–92 (2015).
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M. Gärtner, S. Grimm, and M. Bajbouj, “Frontal midline theta oscillations during mental arithmetic: effects of stress,” Front. Behav. Neurosci. 9, 96 (2015).
[PubMed]

L. Xin, C. Zetao, Z. Yunpeng, X. Jiali, W. Shuicai, and Z. Yanjun, “Stress State Evaluation by Improved Support Vector Machine,” J. Med Imag. Health Inform 5(4), 742–747 (2015).
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A. C. Marshall, N. R. Cooper, R. Segrave, and N. Geeraert, “The effects of long-term stress exposure on aging cognition: a behavioral and EEG investigation,” Neurobiol. Aging 36(6), 2136–2144 (2015).
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Y. Choi, M. Kim, and C. Chun, “Measurement of occupants’ stress based on electroencephalograms (EEG) in twelve combined environments,” Build. Environ. 88, 65–72 (2015).
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L. Vézard, P. Legrand, M. Chavent, F. Faïta-Aïnseba, and L. Trujillo, “EEG classification for the detection of mental states,” Appl. Soft Comput. 32, 113–131 (2015).
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2014 (12)

G. K. Verma and U. S. Tiwary, “Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals,” Neuroimage 102(Pt 1), 162–172 (2014).
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N. Sharma and T. Gedeon, “Modeling observer stress for typical real environments,” Expert Syst. Appl. 41(5), 2231–2238 (2014).
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T.-K. Liu, Y.-P. Chen, Z.-Y. Hou, C.-C. Wang, and J.-H. Chou, “Noninvasive evaluation of mental stress using by a refined rough set technique based on biomedical signals,” Artif. Intell. Med. 61(2), 97–103 (2014).
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H.-J. Hwang, J.-H. Lim, D.-W. Kim, and C.-H. Im, “Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces,” J. Biomed. Opt. 19(7), 077005 (2014).
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G. Derosière, S. Dalhoumi, S. Perrey, G. Dray, and T. Ward, “Towards a near infrared spectroscopy-based estimation of operator attentional state,” PLoS One 9(3), e92045 (2014).
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R. Takizawa, M. Fukuda, S. Kawasaki, K. Kasai, M. Mimura, S. Pu, T. Noda, S. Niwa, Y. Okazaki, and Joint Project for Psychiatric Application of Near-Infrared Spectroscopy (JPSY-NIRS) Group, “Neuroimaging-aided differential diagnosis of the depressive state,” Neuroimage 85(Pt 1), 498–507 (2014).
[Crossref] [PubMed]

V. T. Nguyen, M. Breakspear, and R. Cunnington, “Fusing concurrent EEG-fMRI with dynamic causal modeling: application to effective connectivity during face perception,” Neuroimage 102(Pt 1), 60–70 (2014).
[Crossref] [PubMed]

F. Putze, S. Hesslinger, C. Y. Tse, Y. Huang, C. Herff, C. Guan, and T. Schultz, “Hybrid fNIRS-EEG based classification of auditory and visual perception processes,” Front. Neurosci. 8, 373 (2014).
[Crossref] [PubMed]

Y. Blokland, L. Spyrou, D. Thijssen, T. Eijsvogels, W. Colier, M. Floor-Westerdijk, R. Vlek, J. Bruhn, and J. Farquhar, “Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia,” IEEE Trans. Neural Syst. Rehabil. Eng. 22(2), 222–229 (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).
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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).
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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).
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2013 (6)

S. D. Power and T. Chau, “Automatic single-trial classification of prefrontal hemodynamic activity in an individual with Duchenne muscular dystrophy,” Dev. Neurorehabil. 16(1), 67–72 (2013).
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M. Stangl, G. Bauernfeind, J. Kurzmann, R. Scherer, and C. Neuper, “A haemodynamic brain-computer interface based on real-time classification of near infrared spectroscopy signals during motor imagery and mental arithmetic,” J. Near Infra Spec 21(3), 157–171 (2013).
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D. N. Lenkov, A. B. Volnova, A. R. Pope, and V. Tsytsarev, “Advantages and limitations of brain imaging methods in the research of absence epilepsy in humans and animal models,” J. Neurosci. Methods 212(2), 195–202 (2013).
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Z. Yuan and J. Ye, “Fusion of fNIRS and fMRI data: Identifying when and where hemodynamic signals are changing in human brains,” Front. Hum. Neurosci. 7, 676 (2013).
[PubMed]

R. R. Singh, S. Conjeti, and R. Banerjee, “A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals,” Biomed Sig Proc. Cont 8(6), 740–754 (2013).
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C. M. Vander Weele, C. Saenz, J. Yao, S. S. Correia, and K. A. Goosens, “Restoration of hippocampal growth hormone reverses stress-induced hippocampal impairment,” Front. Behav. Neurosci. 7, 66 (2013).
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2012 (11)

N. L. Lopez-Duran, R. Nusslock, C. George, and M. Kovacs, “Frontal EEG asymmetry moderates the effects of stressful life events on internalizing symptoms in children at familial risk for depression,” Psychophysiology 49(4), 510–521 (2012).
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C. M. Michel and M. M. Murray, “Towards the utilization of EEG as a brain imaging tool,” Neuroimage 61(2), 371–385 (2012).
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C. Zhao, M. Zhao, J. Liu, and C. Zheng, “Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator,” Accid. Anal. Prev. 45, 83–90 (2012).
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S. D. Power, A. Kushki, and T. Chau, “Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI,” BMC Res. Notes 5(1), 141 (2012).
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N. Sharma and T. Gedeon, “Objective measures, sensors and computational techniques for stress recognition and classification: A survey,” Comput. Methods Programs Biomed. 108(3), 1287–1301 (2012).
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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).
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M. Hinwood, J. Morandini, T. A. Day, and F. R. Walker, “Evidence that microglia mediate the neurobiological effects of chronic psychological stress on the medial prefrontal cortex,” Cereb. Cortex 22(6), 1442–1454 (2012).
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N. R. Lighthall, M. Sakaki, S. Vasunilashorn, L. Nga, S. Somayajula, E. Y. Chen, N. Samii, and M. Mather, “Gender differences in reward-related decision processing under stress,” Soc. Cogn. Affect. Neurosci. 7(4), 476–484 (2012).
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B. Abibullaev and J. An, “Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms,” Med. Eng. Phys. 34(10), 1394–1410 (2012).
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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).
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S. Cutini, S. B. Moro, and S. Bisconti, “Functional near infrared optical imaging in cognitive neuroscience: an introductory,” J. Near Infrared Spectrosc. 20(1), 75–92 (2012).
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2011 (12)

L. Ossewaarde, S. Qin, H. J. Van Marle, G. A. van Wingen, G. Fernández, and E. J. Hermans, “Stress-induced reduction in reward-related prefrontal cortex function,” Neuroimage 55(1), 345–352 (2011).
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G. Bauernfeind, R. Scherer, G. Pfurtscheller, and C. Neuper, “Single-trial classification of antagonistic oxyhemoglobin responses during mental arithmetic,” Med. Biol. Eng. Comput. 49(9), 979–984 (2011).
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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).
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H. Sato, R. Aoki, T. Katura, R. Matsuda, and H. Koizumi, “Correlation of within-individual fluctuation of depressed mood with prefrontal cortex activity during verbal working memory task: optical topography study,” J. Biomed. Opt. 16(12), 126007 (2011).
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A. Machado, J.-M. Lina, J. Tremblay, M. Lassonde, D. K. Nguyen, F. Lesage, and C. Grova, “Detection of hemodynamic responses to epileptic activity using simultaneous Electro-EncephaloGraphy (EEG)/Near Infra Red Spectroscopy (NIRS) acquisitions,” Neuroimage 56(1), 114–125 (2011).
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C. Zhao, C. Zheng, M. Zhao, Y. Tu, and J. Liu, “Multivariate autoregressive models and kernel learning algorithms for classifying driving mental fatigue based on electroencephalographic,” Expert Syst. Appl. 38(3), 1859–1865 (2011).
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K. Mizuno, M. Tanaka, K. Yamaguti, O. Kajimoto, H. Kuratsune, and Y. Watanabe, “Mental fatigue caused by prolonged cognitive load associated with sympathetic hyperactivity,” Behav. Brain Funct. 7(1), 17 (2011).
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A. R. Groves, C. F. Beckmann, S. M. Smith, and M. W. Woolrich, “Linked independent component analysis for multimodal data fusion,” Neuroimage 54(3), 2198–2217 (2011).
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C. C. Chang and C. J. Lin, “LIBSVM: A Library for support vector machines,” ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011).
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V. Engert, S. Vogel, S. I. Efanov, A. Duchesne, V. Corbo, N. Ali, and J. C. Pruessner, “Investigation into the cross-correlation of salivary cortisol and alpha-amylase responses to psychological stress,” Psychoneuroendocrinology 36(9), 1294–1302 (2011).
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P. Missonnier, F. R. Herrmann, C. Rodriguez, M.-P. Deiber, P. Millet, L. Fazio-costa, G. Gold, and P. Giannakopoulos, “Age-related differences on event-related potentials and brain rhythm oscillations during working memory activation,” J Neural Transm (Vienna) 118(6), 945–955 (2011).
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A. Tsutsumi, K. Kayaba, and S. Ishikawa, “Impact of occupational stress on stroke across occupational classes and genders,” Soc. Sci. Med. 72(10), 1652–1658 (2011).
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2010 (6)

J. Sui, T. Adali, G. Pearlson, H. Yang, S. R. Sponheim, T. White, and V. D. Calhoun, “A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia,” Neuroimage 51(1), 123–134 (2010).
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H. Atsumori, M. Kiguchi, T. Katura, T. Funane, A. Obata, H. Sato, T. Manaka, M. Iwamoto, A. Maki, H. Koizumi, and K. Kubota, “Noninvasive imaging of prefrontal activation during attention-demanding tasks performed while walking using a wearable optical topography system,” J. Biomed. Opt. 15(4), 046002 (2010).
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X. Cui, S. Bray, and A. L. Reiss, “Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics,” Neuroimage 49(4), 3039–3046 (2010).
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S. D. Power, T. H. Falk, and T. Chau, “Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy,” J. Neural Eng. 7(2), 026002 (2010).
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T. Li, Q. Luo, and H. Gong, “Gender-specific hemodynamics in prefrontal cortex during a verbal working memory task by near-infrared spectroscopy,” Behav. Brain Res. 209(1), 148–153 (2010).
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Z. F. Zaidi, “Gender differences in human brain: a review,” The Open Anatomy Journal 2(1), 1 (2010).

2009 (8)

K. Dedovic, M. Rexroth, E. Wolff, A. Duchesne, C. Scherling, T. Beaudry, S. D. Lue, C. Lord, V. Engert, and J. C. Pruessner, “Neural correlates of processing stressful information: an event-related fMRI study,” Brain Res. 1293, 49–60 (2009).
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C. Liston, B. S. McEwen, and B. J. Casey, “Psychosocial stress reversibly disrupts prefrontal processing and attentional control,” Proc. Natl. Acad. Sci. U.S.A. 106(3), 912–917 (2009).
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S. Qin, E. J. Hermans, H. J. van Marle, J. Luo, and G. Fernández, “Acute psychological stress reduces working memory-related activity in the dorsolateral prefrontal cortex,” Biol. Psychiatry 66(1), 25–32 (2009).
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V. D. Calhoun, J. Liu, and T. Adali, “A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data,” Neuroimage 45(1Suppl), S163–S172 (2009).
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V. D. Calhoun and T. Adal, “Feature-based fusion of medical imaging data,” IEEE Trans Info. Technol. Biomed. 13(5), 711–720 (2009).

D. H. Hellhammer, S. Wüst, and B. M. Kudielka, “Salivary cortisol as a biomarker in stress research,” Psychoneuroendocrinology 34(2), 163–171 (2009).
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G. Chanel, J. J. Kierkels, M. Soleymani, and T. Pun, “Short-term emotion assessment in a recall paradigm,” Int. J. Hum. Comput. Stud. 67(8), 607–627 (2009).
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C. Babiloni, V. Pizzella, C. D. Gratta, A. Ferretti, and G. L. Romani, “Fundamentals of electroencefalography, magnetoencefalography, and functional magnetic resonance imaging,” Int. Rev. Neurobiol. 86, 67–80 (2009).
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2008 (4)

B. S. McEwen, “Central effects of stress hormones in health and disease: Understanding the protective and damaging effects of stress and stress mediators,” Eur. J. Pharmacol. 583(2-3), 174–185 (2008).
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Å. M. Hansen, A. H. Garde, and R. Persson, “Sources of biological and methodological variation in salivary cortisol and their impact on measurement among healthy adults: a review,” Scand. J. Clin. Lab. Invest. 68(6), 448–458 (2008).
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M. Moosmann, T. Eichele, H. Nordby, K. Hugdahl, and V. D. Calhoun, “Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation,” Int. J. Psychophysiol. 67(3), 212–221 (2008).
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T. Katura, H. Sato, Y. Fuchino, T. Yoshida, H. Atsumori, M. Kiguchi, A. Maki, M. Abe, and N. Tanaka, “Extracting task-related activation components from optical topography measurement using independent components analysis,” J. Biomed. Opt. 13(5), 054008 (2008).
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2007 (5)

R. S. Lewis, N. Y. Weekes, and T. H. Wang, “The effect of a naturalistic stressor on frontal EEG asymmetry, stress, and health,” Biol. Psychol. 75(3), 239–247 (2007).
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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).
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M. Boecker, M. M. Buecheler, M. L. Schroeter, and S. Gauggel, “Prefrontal brain activation during stop-signal response inhibition: an event-related functional near-infrared spectroscopy study,” Behav. Brain Res. 176(2), 259–266 (2007).
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D. A. Granger, K. T. Kivlighan, M. el-Sheikh, E. B. Gordis, and L. R. Stroud, “Salivary α-amylase in biobehavioral research: recent developments and applications,” Ann. N. Y. Acad. Sci. 1098(1), 122–144 (2007).
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M. Huiku, K. Uutela, M. van Gils, I. Korhonen, M. Kymäläinen, P. Meriläinen, M. Paloheimo, M. Rantanen, P. Takala, H. Viertiö-Oja, and A. Yli-Hankala, “Assessment of surgical stress during general anaesthesia,” Br. J. Anaesth. 98(4), 447–455 (2007).
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2006 (5)

G. Chanel, J. Kronegg, D. Grandjean, and T. Pun, “Emotion assessment: Arousal evaluation using EEG’s and peripheral physiological signals,” Multimedia Content Representation, Classification and Security 4105, 530–537 (2006).
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R. A. Ajjan and P. J. Grant, “Cardiovascular disease prevention in patients with type 2 diabetes: The role of oral anti-diabetic agents,” Diab. Vasc. Dis. Res. 3(3), 147–158 (2006).
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V. D. Calhoun, T. Adali, N. R. Giuliani, J. J. Pekar, K. A. Kiehl, and G. D. Pearlson, “Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data,” Hum. Brain Mapp. 27(1), 47–62 (2006).
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V. D. Calhoun, T. Adali, G. D. Pearlson, and K. A. Kiehl, “Neuronal chronometry of target detection: fusion of hemodynamic and event-related potential data,” Neuroimage 30(2), 544–553 (2006).
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R. Thibodeau, R. S. Jorgensen, and S. Kim, “Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review,” J. Abnorm. Psychol. 115(4), 715–729 (2006).
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2005 (6)

R. Sinha, C. Lacadie, P. Skudlarski, R. K. Fulbright, B. J. Rounsaville, T. R. Kosten, and B. E. Wexler, “Neural activity associated with stress-induced cocaine craving: a functional magnetic resonance imaging study,” Psychopharmacology (Berl.) 183(2), 171–180 (2005).
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K. Dedovic, R. Renwick, N. K. Mahani, V. Engert, S. J. Lupien, and J. C. Pruessner, “The Montreal Imaging Stress Task: using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain,” J. Psychiatry Neurosci. 30(5), 319–325 (2005).
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M. J. Herrmann, M. M. Plichta, A.-C. Ehlis, and A. J. Fallgatter, “Optical topography during a Go-NoGo task assessed with multi-channel near-infrared spectroscopy,” Behav. Brain Res. 160(1), 135–140 (2005).
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C. Hammen, “Stress and depression,” Annu. Rev. Clin. Psychol. 1(1), 293–319 (2005).
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J. A. Healey and R. W. Picard, “Detecting stress during real-world driving tasks using physiological sensors,” IEEE Trans. Intell. Transp. Syst. 6(2), 156–166 (2005).
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T. Takahashi, T. Murata, T. Hamada, M. Omori, H. Kosaka, M. Kikuchi, H. Yoshida, and Y. Wada, “Changes in EEG and autonomic nervous activity during meditation and their association with personality traits,” Int. J. Psychophysiol. 55(2), 199–207 (2005).
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2004 (3)

A. Sassaroli and S. Fantini, “Comment on the modified Beer-Lambert law for scattering media,” Phys. Med. Biol. 49(14), N255–N257 (2004).
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A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” J. Neurosci. Methods 134(1), 9–21 (2004).
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H. Sato, M. Kiguchi, F. Kawaguchi, and A. Maki, “Practicality of wavelength selection to improve signal-to-noise ratio in near-infrared spectroscopy,” Neuroimage 21(4), 1554–1562 (2004).
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2003 (2)

P. C. Strike and A. Steptoe, “Systematic review of mental stress-induced myocardial ischaemia,” Eur. Heart J. 24(8), 690–703 (2003).
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M. Gröschl, M. Rauh, and H.-G. Dörr, “Circadian rhythm of salivary cortisol, 17α-hydroxyprogesterone, and progesterone in healthy children,” Clin. Chem. 49(10), 1688–1691 (2003).
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2002 (1)

A. Vyas, R. Mitra, B. S. Shankaranarayana Rao, and S. Chattarji, “Chronic stress induces contrasting patterns of dendritic remodeling in hippocampal and amygdaloid neurons,” J. Neurosci. 22(15), 6810–6818 (2002).
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2001 (1)

B. Czéh, T. Michaelis, T. Watanabe, J. Frahm, G. de Biurrun, M. van Kampen, A. Bartolomucci, and E. Fuchs, “Stress-induced changes in cerebral metabolites, hippocampal volume, and cell proliferation are prevented by antidepressant treatment with tianeptine,” Proc. Natl. Acad. Sci. U.S.A. 98(22), 12796–12801 (2001).
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2000 (1)

T. G. Vrijkotte, L. J. van Doornen, and E. J. de Geus, “Effects of work stress on ambulatory blood pressure, heart rate, and heart rate variability,” Hypertension 35(4), 880–886 (2000).
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1999 (1)

T. W. Lee, M. Girolami, and T. J. Sejnowski, “Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources,” Neural Comput. 11(2), 417–441 (1999).
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1997 (1)

A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20(10), 435–442 (1997).
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1996 (1)

L. R. Murphy, “Stress management in work settings: a critical review of the health effects,” Am. J. Health Promot. 11(2), 112–135 (1996).
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1995 (1)

A. J. Bell and T. J. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution,” Neural Comput. 7(6), 1129–1159 (1995).
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1993 (1)

R. E. Wheeler, R. J. Davidson, and A. J. Tomarken, “Frontal brain asymmetry and emotional reactivity: A biological substrate of affective style,” Psychophysiology 30(1), 82–89 (1993).
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1972 (1)

H. Ashton, R. D. Savage, J. W. Thompson, and D. W. Watson, “A method for measuring human behavioural and physiological responses at different stress levels in a driving simulator,” Br. J. Pharmacol. 45(3), 532–545 (1972).
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1967 (1)

T. H. Holmes and R. H. Rahe, “The social readjustment rating scale,” J. Psychosom. Res. 11(2), 213–218 (1967).
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1965 (1)

J. Decker, “The Stress Syndrome,” Am. J. Nurs. 65(3), 97–99 (1965).
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Figures (9)

Fig. 1
Fig. 1 Experiment block design. A total of five active blocks existed for each of the (a) control and (b) stress condition. In each block, arithmetic tasks produced for thirty second followed by twenty second rest. During the 30 s task, several arithmetic questions would be posted depend on how fast the respond of the participant in answering each question. If the respond rate is 2 s per question, the total number of questions per block would be 15 questions and the total number of questions would be 75 questions, for example. During the 20 s rest, the computer screen would display with a fixation cross with black background and participants were instructed to look at the fixation cross as a visual cue for trial onset. The red dashed-line marks the start of the task and the green dashed-line marks the end of the task (the marker was presented in every block). The stressors were based on time pressure and negative feedback of individual performance as demonstrated in (b). Five samples (S1-S5) of alpha amylase were collected; 5-minutes before the control condition as baseline for control, immediately after control condition, 5-minutes before stress condition, immediately after stress condition and 5-minutes after stress condition as marked in the figure with yellow rectangular.
Fig. 2
Fig. 2 EEG + fNIRS probe holder, (a) fNIRS channels and electrodes marking in the probe holder, (b) inner-view of the probe holder, (c) outer-view of the probe holder. The holder consisted of eight sources/emission probes and eight detection probes. Total of twenty three channels and seven active EEG electrodes involved in the probe holder.
Fig. 3
Fig. 3 Salivary alpha amylase responses under control and stress condition. Blue color shows the salivary alpha amylase response under control condition at three measurement instances (5 min before (baseline), at the end of control condition (Task), 5 min after the task (recovery). Red colour shows the salivary alpha amylase response under stress condition with three measurement times (5 min before (baseline), at the end of stress condition (Task), 5 min after the stress task (recovery).The marks “***” indicate that, the task is significant with p<0.001.
Fig. 4
Fig. 4 Normalized Alpha and Beta rhythm power values in two mental state: control and stress for average of 22-subjects. The Alpha and Beta rhythm power values were calculated from all the measured electrodes on the PFC.
Fig. 5
Fig. 5 Mean time-courses of oxygenated hemoglobin concentration changes, (a) control and stress conditions at Ch14 and (b) control and stress conditions at Ch17. The vertical red dash-line marks the start of the task and the vertical green dash-line marks the end of the task condition.
Fig. 6
Fig. 6 Topographical map of oxygenated hemoglobin activation for average of 22 subjects, (a) under control condition and (b) under stress condition. Red colour indicates higher activation and blue colour indicates less activation. Under stress condition, reduced hemodynamic response around the right PFC region.
Fig. 7
Fig. 7 Boxplots representing the classification accuracy measured by SVM for 22 subjects. The results demonstrate significant improvements in the mean classification accuracy when combining both modalities, p<0.001. High improvement in the classification due to combining both modalities with + 3.4% compared to EEG alone and + 11% compared to fNIRS alone.
Fig. 8
Fig. 8 Boxplots representing the classification sensitivity calculated for 22 subjects. High improvement in the sensitivity occurred when combining both modalities with + 3.8% compared to EEG alone and + 11.8% compared to fNIRS alone, p<0.001.
Fig. 9
Fig. 9 Boxplots representing the classification specificity calculated for 22 subjects. High improvements in the specificity obtained when combining both modalities with + 3.2% compared to EEG alone and + 10.6% compared to fNIRS alone, p<0.001.

Tables (1)

Tables Icon

Table 1 Statistical analysis of EEG alpha and beta and O2Hb of fNIRS measurements based on two-sample t-test. In terms of electrode naming, F4A represents EEG electrode F4 in Alpha band, F4B represents EEG electrode F4 in Beta band, and so on.

Equations (15)

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

P j = 1 N n=1 N | x j (n) | 2 ,j=1,2 .
O 2 Hb= 1 N n=1 N (Δ O 2 Hb) n
K(x,y)=exp( ||xy| | 2 2 σ 2 )
X EEG =A S EEG , X fNIRS =A S fNIRS
S EEG = [ S 1 EEG , S 2 EEG ] T ,
S fNIRS = [ S 1 fNIRS , S 2 fNIRS ] T ,
A=[ a 11 a 12 a 21 a 22 ],
[ X 1 EEG X 1 fNIRS X 2 EEG X 2 fNIRS ]=[ a 11 a 12 a 21 a 22 ][ S 1 EEG S 1 fNIRS S 2 EEG S 2 fNIRS ],
ΔW=η{I2 y EEG ( S ' EEG ) T 2 y fNIRS ( S ' fNIRS ) T }W,
S ' EEG =W X EEG ,
S ' fNIRS =W X fNIRS ,
y EEG =g( S ' EEG )
y fNIRS =g( S ' fNIRS ),
g(x)= 1 1+ e x ,
t= X ¯ 1 X ¯ 2 s X 1 X 2 · 1 n

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