EEG based emotion detection using fourth order spectral moment and deep learning

• Published in: Biomedical signal processing and control Vol. 68; p. 102755
• Main Authors: Joshi, Vaishali M., Ghongade, Rajesh B.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2021
• Subjects: Bidirectional long short-term memory; Electroencephalography; Emotions; Linear formulation of differential entropy; Electroencephalography; Bidirectional long short-term memory; Emotions; Linear formulation of differential entropy
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102755

•Subject contingent and noncontingent experiment are conducted on SEED and DEAP database for emotion detection.•Proposed LF-DfE feature extraction method detects nonlinearity and non Gaussianity of EEG signal.•BiLSTM network is adapted to learn spatial features from different brain region and temporal dependency of the EEG signal.•The experimental results using LF-DfE and BiLSTM network outperform the classical methods. This paper proposes emotion detection using Electroencephalography (EEG) signal based on Linear Formulation of Differential Entropy (LF-DfE) feature extractor and BiLSTM network classifier. LF-DfE effectively detects nonlinearity and non-Gaussianity of the EEG signal. BiLSTM network captures long term dependency of the EEG signal and learns spatial information from different brain regions. Proposed model is used to discriminate positive, negative, and neutral emotions on SEED database, valence and arousal on DEAP database. To assess the proposed model subject contingent, noncontingent and inter-dependent (cross-session) experiments are performed on the SEED database. The average accuracy of emotion detection on SEED database for subject contingent approach is improved by 4.12 %, for noncontingent approach by 4.5 % and for inter-dependent approach it is improved by 1.3 %. To reconfirm the above findings, one more experiment is conducted for subject noncontingent approach on DEAP database. On DEAP database for subject noncontingent experiment average accuracy is improved by 7.04 %. Experimental results of the proposed feature extractor LF-DfE with the BiLSTM network found to be improved over existing methods.