Open Access
Issue |
E3S Web Conf.
Volume 351, 2022
10th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|
|
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Article Number | 01026 | |
Number of page(s) | 5 | |
DOI | https://doi.org/10.1051/e3sconf/202235101026 | |
Published online | 24 May 2022 |
- Said Abenna, Mohammed Nahid, and Abdelmounaim Kochairi Belbachir. Brain-computer interface: Rhythm alpha analysis for eyes signals. In The Fourth International Conference On Intelligent Computing in Data Sciences, IEEE, (2020) [Google Scholar]
- Said Abenna, Mohammed Nahid, and Abderrahim Bajit. BCI: Classifiers Optimization for EEG Signals Acquiring in RealTime. In 2020 6th IEEE Congress on Information Science and Technology (CiSt), (2021). [Google Scholar]
- Said Abenna, Mohammed Nahid, and Abderrahim Bajit. Brain-computer interface: A novel EEG classification for baseline eye states using LGBM algorithm, chapter 18, pages 189–198. Springer Science and Business Media LLC, (2021). [Google Scholar]
- L. F. Nicolas Alonso and J. Gomez Gil. Brain-computer interfaces, a review sensors. 2012. [Google Scholar]
- C.J. Bell, P. Shenoy, R. Chalodhorn, and R.P. Rao. Control of a humanoid robot by a noninvasive brain-computer interface in humans. J. Neural Eng., (2008). [Google Scholar]
- B. Blankertz, G. Dornhege, M. Krauledat, K. R. Müller, and G. Curio. The non-invasive berlin braincomputer interface: fast acquisition of effective performance in untrained subjects. NeuroImage, (2007). [Google Scholar]
- B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. R. Müller. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag, (2008). [Google Scholar]
- A.A. Hossain, M.W. Rahman, M.A. Riheen. Left and right hand movements eeg signals classification using wavelet transform and probabilistic neural network. International Journal of Electrical and Computer Engineering, (2015). [Google Scholar]
- Chih Hsu Huang, Ming Shaung Ju, and Chou Ching K. Lin. A robust algorithm for removing artifacts in EEG recorded during fMRI/EEG study. Computers in Biology and Medicine, (2012). [PubMed] [Google Scholar]
- J. Lerga, N. Saulig, L. Stankovi. Rule-Based EEG Classifier Utilizing Local Entropy of TimeFrequency Distributions, (2021). [Google Scholar]
- W. Long, J.J. Jiao, X.M. Liang, and M.Z. Tang. An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization. Eng. Appl. Artif. Intell., (2018). [Google Scholar]
- D.J. McFarland, D.J. Krusienski, W.A. Sarnacki, and J.R. Wolpaw. Emulation of computer mouse control with a noninvasive brain-computer interface. J. Neural Eng., (2008). [Google Scholar]
- S. Mirjalili, S.M. Mirjalili, and A. Lewis. Grey wolf optimizer. Adv. Eng. Software, (2014) [Google Scholar]
- S. R. Sreeja and Debasis Samanta. Classification of multiclass motor imagery EEG signal using sparsity approach. Neurocomputing, 368:133–145, (2019). [CrossRef] [Google Scholar]
- Yousef Rezaei Tabar and Ugur Halici. A novel deep learning approach for classification of EEG motor imagery signals. Journal of Neural Engineering, page 16003, (2017). [Google Scholar]
- S. Z. Zahid, M. Aqil, M. Tufail, and M. S. Nazir. Online Classification of Multiple Motor Imagery Tasks Using Filter Bank Based Maximum-a-Posteriori Common Spatial Pattern Filters. IRBM, 1, (2019). [Google Scholar]
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