The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Time–Frequency Representation and Convolutional Neural Network-Based Emotion Recognition
Abstract
Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral changes. Electroencephalogram (EEG) signals provide a noninvasive and nonradioactive solution for emotion identification. Accurate and automatic classification of emotions can boost the development of human-computer interface. This article proposes automatic extraction and classification of features through the use of different convolutional neural networks (CNNs). At first, the proposed method converts the filtered EEG signals into an image using a time-frequency representation. Smoothed pseudo-Wigner-Ville distribution is used to transform time-domain EEG signals into images. These images are fed to pretrained AlexNet, ResNet50, and VGG16 along with configurable CNN. The performance of four CNNs is evaluated by measuring the accuracy, precision, Mathew's correlation coefficient, F1-score, and false-positive rate. The results obtained by evaluating four CNNs show that configurable CNN requires very less learning parameters with better accuracy. Accuracy scores of 90.98%, 91.91%, 92.71%, and 93.01% obtained by AlexNet, ResNet50, VGG16, and configurable CNN show that the proposed method is best among other existing methods.
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Cites
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EEG-Based Emotion Recognition in Music Listening
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Emotion Recognition From EEG Using Higher Order Crossings
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Cited by
Khare, S. K., Blanes-Vidal, V., Nadimi, E. S., & Acharya, U. R. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102019 · Cited by 469
Self-citationNour, M., Cömert, Z., & Polat, K. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106580 · Cited by 316
Emotion recognition in EEG signals using deep learning methods: A review
Jafari, M., Shoeibi, A., Khodatars, M., Bagherzadeh, S., Shalbaf, A., García, D. L., Gorriz, J. M., & Acharya, U. R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107450 · Cited by 240
Pan, B., Hirota, K., Jia, Z., & Dai, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126866 · Cited by 142
Long Dialogue Emotion Detection Based on Commonsense Knowledge Graph Guidance
Nie, W., Bao, Y., Zhao, Y., & Liu, A. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3267295 · Cited by 96
Metadata
- Authors
- Khare, S. K., & Bajaj, V.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2021-07
- Volume
- 32
- Issue
- 7
- Pages
- 2901-2909
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W3046236234
- Cited by
- 299
- References
- 38
- FWCI
- 37.71
- Type
- journal article
- Language
- en
- Open access
- closed
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