NDLI: Single-trial EEG data to classify type and intensity of facial emotion from P100 and N170

Content Provider	IEEE Xplore Digital Library
Author	Utama, N.P. Takemoto, A. Nakamura, K. Koike, Y.
Copyright Year	2009
Description	Author affiliation: Precision and Intelligence Laboratory, Kanagawa, Japan (Koike, Y.) \|\| Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Japan (Utama, N.P.) \|\| Department of Animal Models for Human Disease, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan (Takemoto, A.; Nakamura, K.)
Abstract	Two aspects of facial emotions are type and intensity level. This study is an attempt to find the relation of electrode location and latencies of EEG data with specific aspects of facial emotions. A neural network was applied to single trial EEG data for classifying the type and intensity level of facial emotions. Images of two types of facial emotions (happiness and disgust) with two intensity levels (high and low) were parametrically controlled and presented as main stimuli to evoke brain signals. Results of this study indicated that data from electrodes over the right-posterior area in the time-range window 80 to 200 ms post stimulus presentation contains P100 and N170 components strongly related to the process of recognition of facial emotions. Relative to other components, N170 was strongly related to the intensity levels of facial emotion, where it could be used for classifying low and high intensity levels of facial emotion. P100 and N170 can be used for classifying the type of facial emotions, with the accuracy when using P100 being slightly higher. Time-range window 200 to 280 ms contains N2, which can also be used for classifying the intensity level of facial emotion, but its accuracy is less than that of N170. Data from time-range window 300 to 360 ms, which contains the P300 component, was not significant for classifying type and intensity level of facial emotion relative to that of other components. This study is the first to show the possibility of using other components besides P300 for EEG-based BCIs. The results also suggest the mechanism of recognition of facial emotion, started by recognizing the type of facial emotion followed by how intense the facial emotion is expressed.
Starting Page	3156
Ending Page	3163
File Size	11109199
Page Count	8
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178818
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Emotion recognition Face recognition Delay Biological neural networks Enterprise resource planning Electrodes Humans Signal processing Brain modeling
Content Type	Text
Resource Type	Article

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