A hybrid ensemble voting-based residual attention network for motor imagery EEG Classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Surfaces, Coatings and Films,Hardware and Architecture,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s10470-023-02240-1.pdf
Reference48 articles.
1. Khorshidtalab, A., Salami, M. J. E., & Akmeliawati, R. (2017). Motor imagery task classification using transformation based features. Biomedical Signal Processing and Control, 33, 213–219. https://doi.org/10.1016/j.bspc.2016.12.006
2. Jindal, K., & Upadhyay, R. (2017, July). Epileptic seizure detection from EEG signal using Flexible Analytical Wavelet Transform. In 2017 International Conference on Computer, Communications and Electronics (Comptelix) (pp. 67-72). https://doi.org/10.1109/COMPTELIX.2017.8003940
3. Blanco, J. A., Vanleer, A. C., Calibo, T. K., & Firebaugh, S. L. (2019). Single-trial cognitive stress classification using portable wireless electroencephalography. Sensors. https://doi.org/10.3390/s19030499
4. Khare, S., & Bajaj, V. (2020). Adaptive Tunable Q Wavelet Transform based Emotion Identification. IEEE Transactions on Instrumentation and Measurement. Vol. 9456(c), (pp.1–1). https://doi.org/10.1109/TIM.2020.3006611
5. He, L., Liu, B., Hu, D., Wen, Y., Wan, M., & Long, J. (2016). Motor imagery EEG signals analysis based on Bayesian network with Gaussian distribution. Neurocomputing, 188, 217–224. https://doi.org/10.1016/j.neucom.2015.05.133
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