Lemniscate of Bernoulli’s map quantifiers: innovative measures for EEG emotion recognition
Author:
Publisher
Springer Science and Business Media LLC
Subject
Cognitive Neuroscience
Link
https://link.springer.com/content/pdf/10.1007/s11571-023-09968-6.pdf
Reference73 articles.
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2. Al-Nafjan A, Hosny M, Al-Ohali Y, Al-Wabil A (2017) Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review. Appl Sci 7(12):1239. https://doi.org/10.3390/app7121239
3. Bhattacharyya A, Tripathy RK, Garg L, Pachori RB (2021) A novel multivariate-multiscale approach for computing EEG spectral and temporal complexity for human emotion recognition. IEEE Sens J 21(3):3579–3591. https://doi.org/10.1109/JSEN.2020.3027181
4. Bulagang AF, Weng Ng G, Mountstephens J, Teo J (2020) A review of recent approaches for emotion classification using electrocardiography and electrodermography signals. Inform Med Unlocked 20:100363. https://doi.org/10.1016/j.imu.2020.100363
5. Cizmeci H, Ozcan C (2022) Enhanced deep capsule network for EEG-based emotion recognition. Signal Image Video Process. https://doi.org/10.1007/s11760-022-02251-x
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