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

Fatemeh Bahari · Amin Janghorbani

80 citations0/0 files storedIranian Conference on Biomedical Engineering

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Face and Expression Recognition

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.

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References

1980A circumplex model of affect.1997Affective Computing 2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 1999Basic Emotions 1990What's basic about basic emotions?1979Affective space is bipolar.2009Emotion Recognition From EEG Using Higher Order Crossings 2001Emotions in social psychology : essential readings

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