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Emotion Detection using EEG Signals with Ensemble Learning

Emotions have a significant influence on how we choose and absorb media. Recent developments in affect detection have centered on continually sensing emotions. For the first time, we identify valence in electroencephalogram (EEG) signals and facial expressions in reaction to films in this article. Several annotators produced valence levels by continually observing the frontal face recordings of participants who viewed brief emotional movies. The characteristics utilized to assess valence levels for each frame include power spectral features from EEG data and face fiducial points. For the DEAP data, binary classification is used. Two emotions, arousal and valence, are categorised by ranking them below five and above five and labelling them low and high, respectively. To identify the high and low-rated emotions, ensemble learning is used. This is compared to those other existing approaches like Random Forest, SVM, & AdaBoost to determine how well it performs to make it successful. The characteristics show that the proposed method is quite effective at identifying emotions from EEG signals.