A Comparative Study of Subject-Dependent and Subject-Independent Strategies for EEG-Based Emotion Recognition using LSTM Network

This paper addresses the problem of EEG-based emotion recognition and classification and investigates the performance of classifiers for subject-independent and subject-dependent models separately. The results are compared with other classifiers and also with existing work in the concerned domain as well. We perform the experiments on the publicly available DEAP dataset with band power as the feature and classification accuracies are found pertaining to the widely accepted Valence-Arousal Model. The best results were reported by the LSTM model in case of the subject-dependent model with accuracies of 94.69% and 93.13% on valence and arousal scales respectively. SVM performed the best for the subject-independent model with accuracies of 72.19% on valence scale and 71.25% on arousal scale.

[1]  Ming Qiu,et al.  Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[2]  Zhenqi Li,et al.  Emotion Recognition from EEG Using RASM and LSTM , 2017, ICIMCS.

[3]  J. Russell.  A circumplex model of affect. , 1980 .

[4]  M. Bradley,et al.  Remembering pictures: pleasure and arousal in memory. , 1992, Journal of experimental psychology. Learning, memory, and cognition.

[5]  Martin Buss,et al.  Feature Extraction and Selection for Emotion Recognition from EEG , 2014, IEEE Transactions on Affective Computing.

[6]  Divyashikha Sethia,et al.  Emotion Classification Using EEG Signals , 2018, CSAI '18.

[7]  Olga Sourina,et al.  Real-Time Subject-Dependent EEG-Based Emotion Recognition Algorithm , 2014, Trans. Comput. Sci..

[8]  Reda A. El-Khoribi,et al.  Emotion Recognition based on EEG using LSTM Recurrent Neural Network , 2017 .

[9]  K. R. Seeja,et al.  Subject independent emotion recognition from EEG using VMD and deep learning , 2019, J. King Saud Univ. Comput. Inf. Sci..

[10]  J. Stainer,et al.  The Emotions , 1882, Nature.

[11]  Zhenqi Li,et al.  SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG , 2019, Front. Neurorobot..

[12]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[13]  D. Watson,et al.  Toward a consensual structure of mood. , 1985, Psychological bulletin.

[14]  Peerapon Vateekul,et al.  An evaluation of feature extraction in EEG-based emotion prediction with support vector machines , 2014, 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE).