Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements
Author:
Affiliation:
1. Department of AI Engineering, Graduate School of Sciences , 232990 Uskudar University , Istanbul , Türkiye
2. Department of Mechanical Engineering, Faculty of Engineering and Architecture , 226844 İzmir Katip Çelebi University , İzmir , Türkiye
Abstract
Publisher
Walter de Gruyter GmbH
Link
https://www.degruyter.com/document/doi/10.1515/bmt-2023-0356/pdf
Reference43 articles.
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2. Wei, L, Yue, H, Jiang, X, Xi, C, Xiaojun, W. The assessment of EEG in patients with spinal cord injury to movements. In: Third International Symposium on Intelligent Information Technology Application Workshops. NanChang, China: IEEE; 2009.
3. Karakaya, S, Kucukyildiz, G, Ocak, H. Classification of motor imaginary in EEG using random. Global J Comput Sci Theor Res 2017;7:129–35. https://doi.org/10.18844/gjcs.v7i3.2792.
4. Xu, F, Li, J, Dong, G, Li, J, Chen, X, Zhu, J, et al.. EEG decoding method based on multi-feature information fusion for spinal cord injury. Neural Network 2022;156:135–51. https://doi.org/10.1016/j.neunet.2022.09.016.
5. Agarwal, T, Raturi, S, Vybhav, TK, Singh, M. Classification of EEG signal using lstms under audiovisual stimuli. In: 2020 International Conference on Communication and Signal Processing (ICCSP). Chennai, India: IEEE; 2020.
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