NDLI: EEG-based emotion recognition using Recurrence Plot analysis and K nearest neighbor classifier

Content Provider	IEEE Xplore Digital Library
Author	Bahari, F. Janghorbani, A.
Copyright Year	2013
Description	Author affiliation: Dept. of Biomed. Eng., Amirkabir Univ. of Technol., Tehran, Iran (Bahari, F.; Janghorbani, A.)
Abstract	Electroencephalogram (EEG)-based emotion recognition has been a rapidly growing field. However, accurate and sufficient performance rates are yet to be obtained. This paper presents the classification of EEG correlates on emotion using the relatively new non-linear feature extraction method, namely, Recurrence Plot analysis to extract thirteen non-linear features. This method is compared with feature extraction method based on spectral power analysis. The K nearest neighbor is applied to classify extracted features into the emotional states based on arousal-valence (high/low arousal, valence) plane with the addition of liking axis (positive/negative). Leading to performance rates of 58.05%, 64.56% and 67.42% for 3 classes of valence, arousal and liking; which confirm the advantage of a non-linear feature extraction method over previous frequency based feature extraction techniques.
Starting Page	228
Ending Page	233
File Size	1006280
Page Count	6
File Format	PDF
e-ISBN	9781479932320
DOI	10.1109/ICBME.2013.6782224
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-18
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Emotion recognition EEG Educational institutions Electroencephalography Recurrence Plot Emotion Recognition Accuracy K Nearest Neighbor Feature extraction Trajectory Non-linear Analysis Biomedical engineering
Content Type	Text
Resource Type	Article

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