The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform From EEG Signals
Abstract
Human emotion is a physical or psychological process which is triggered either consciously or unconsciously due to perception of any object or situation. The electroencephalogram (EEG) signals can be used to record ongoing neuronal activities in the brain to get the information about the human emotional state. These complicated neuronal activities in the brain cause non-stationary behavior of the EEG signals. Thus, emotion recognition using EEG signals is a challenging study and it requires advanced signal processing techniques to extract the hidden information of emotions from EEG signals. Due to poor generalizability of features from EEG signals across subjects, recognizing cross-subject emotion has been difficult. Thus, our aim is to comprehensively investigate the channel specific nature of EEG signals and to provide an effective method based on flexible analytic wavelet transform (FAWT) for recognition of emotion. FAWT decomposes the EEG signal into different sub-band signals. Furthermore, we applied information potential to extract the features from the decomposed sub-band signals of EEG signal. The extracted feature values were smoothed and fed to the random forest and support vector machine classifiers that classified the emotions. The proposed method is applied to two different publicly available databases which are SJTU emotion EEG dataset and database for emotion analysis using physiological signal. The proposed method has shown better performance for human emotion classification as compared to the existing method. Moreover, it yields channel specific subject classification of emotion EEG signals when exposed to the same stimuli.
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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
Time–Frequency Representation and Convolutional Neural Network-Based Emotion Recognition
Khare, S. K., & Bajaj, V. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3008938 · Cited by 299
Real-Time Video Emotion Recognition Based on Reinforcement Learning and Domain Knowledge
Zhang, K., Li, Y., Wang, J., Cambria, E., & Li, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3072412 · Cited by 184
Automated emotion recognition: Current trends and future perspectives
Maithri, M., Raghavendra, U., Gudigar, A., Samanth, J., Barua, P. D., Murugappan, M., Chakole, Y., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106646 · Cited by 172
Multimodal emotion recognition based on manifold learning and convolution neural network
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Metadata
- Authors
- Gupta, V., Chopda, M. D., & Pachori, R. B.
- Venue
- IEEE Sensors Journal
- Published
- 2019-03-15
- Volume
- 19
- Issue
- 6
- Pages
- 2266-2274
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W2903462437
- Cited by
- 293
- References
- 44
- FWCI
- 28.38
- Type
- journal article
- Language
- en
- Open access
- closed
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