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گواهی نمایه سازی مقاله EEG-based Emotion Recognition Using Recurrence Plot Analysis and K Nearest Neighbor Classifier

عنوان مقاله: EEG-based Emotion Recognition Using Recurrence Plot Analysis and K Nearest Neighbor Classifier
شناسه (COI) مقاله: ICBME20_093
منتشر شده در بیستمین کنفرانس مهندسی زیست پزشکی ایران در سال ۱۳۹۲
مشخصات نویسندگان مقاله:

Fatemeh Bahari - Department of Biomedical Engineering Amirkabir University of Technology Tehran, Iran
Amin Janghorbani - Department of Biomedical Engineering Amirkabir University of Technology Tehran, Iran

خلاصه مقاله:
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

کلمات کلیدی:
Emotion Recognition , Chaos , Non-linear Analysis , EEG , Recurrence Plot , K Nearest Neighbor

صفحه اختصاصی مقاله و دریافت فایل کامل: https://www.civilica.com/Paper-ICBME20-ICBME20_093.html