Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals
Article
Maheshwari, Daksh, Ghosh, S.K., Tripathy, R.K., Sharma, Manish and Acharya, U. Rajendra. 2021. "Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals." Computers in Biology and Medicine. 134. https://doi.org/10.1016/j.compbiomed.2021.104428
| Article Title | Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals |
|---|---|
| ERA Journal ID | 5040 |
| Article Category | Article |
| Authors | Maheshwari, Daksh, Ghosh, S.K., Tripathy, R.K., Sharma, Manish and Acharya, U. Rajendra |
| Journal Title | Computers in Biology and Medicine |
| Journal Citation | 134 |
| Article Number | 104428 |
| Number of Pages | 15 |
| Year | 2021 |
| Publisher | Elsevier |
| ISSN | 0010-4825 |
| 1879-0534 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2021.104428 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482521002225 |
| Abstract | Emotion is interpreted as a psycho-physiological process, and it is associated with personality, behavior, motivation, and character of a person. The objective of affective computing is to recognize different types of emotions for human-computer interaction (HCI) applications. The spatiotemporal brain electrical activity is measured using multi-channel electroencephalogram (EEG) signals. Automated emotion recognition using multi-channel EEG signals is an exciting research topic in cognitive neuroscience and affective computing. This paper proposes the rhythm-specific multi-channel convolutional neural network (CNN) based approach for automated emotion recognition using multi-channel EEG signals. The delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) rhythms of EEG signal for each channel are evaluated using band-pass filters. The EEG rhythms from the selected channels coupled with deep CNN are used for emotion classification tasks such as low-valence (LV) vs. high valence (HV), low-arousal (LA) vs. high-arousal (HA), and low-dominance (LD) vs. high dominance (HD) respectively. The deep CNN architecture considered in the proposed work has eight convolutions, three average pooling, four batch-normalization, three spatial drop-outs, two drop-outs, one global average pooling and, three dense layers. We have validated our developed model using three publicly available databases: DEAP, DREAMER, and DASPS. The results reveal that the proposed multivariate deep CNN approach coupled with β-rhythm has obtained the accuracy values of |
| Keywords | Channel selection; Multi-channel EEG; Rhythms; Deep CNN; Classification; Emotion recognition |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | BITS Pilani, India |
| Institute of Infrastructure, Technology, Research and Management (IITRAM), India | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Kumamoto University, Japan |
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