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This study investigates the potential of wavelet domain features on the classification of fNIR data for automated attention monitoring. Performance of two different neural classifiers is tested using wavelet based features and it is shown that fast and accurate classification of targets and non-targets can be reached.
© 2008 Optical Society of America
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