Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4364
Title: SimpleRNN based Human Emotion Recognition using EEG Signals
Authors: Sonwani, Harsh
Banoth, Earu
Jain, Puneet Kumar
Keywords: DT-CWT
DT-CWT
SimpleRNN
EEG Signals
Emotion Classification
Issue Date: Jan-2024
Citation: Sixth International Conference on Computational Intelligence in Communications and Business Analytics (CICBA - 2024)
Abstract: The research focuses on developing an electroencephalography (EEG) based emotion recognition system to identify happy, neutral, and negative emotions. The suggested framework uses Simple Recurrent Neural Networks (SimpleRNN) networks to capture the temporal information of the EEG data. Identifying prominent EEG bands for emotion classification and the ensemble of Simple RNN based on these bands are significant contributions of this research. The proposed model achieved 83.39% on a publicly available dataset SJTU Emotion EEG Dataset (SEED). The dual-tree Complex Wavelet Transform (DT-CWT) is used to decompose the signal into five bands, and then the features are extracted from each. By drawing parallels between the capabilities of shallow, deep, and ensemble models, the authors show how their suggested emotion detection system may provide adequate identification performance at a reasonable computational cost. The results indicate that higher frequency bands of EEG signals are more effective in identifying emotions. The study contributes particularly to addressing the timedependence trait of emotion processing. The proposed method has the potential for practical applications in various fields, such as psychology and human-computer interaction, where identifying emotional states is crucial.
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/4364
Appears in Collections:Conference Papers

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