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• W4316468747 abstract "In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research." @default.
• W4316468747 created "2023-01-16" @default.
• W4316468747 creator A5040814039 @default.
• W4316468747 creator A5049700514 @default.
• W4316468747 creator A5059897353 @default.
• W4316468747 creator A5089178693 @default.
• W4316468747 creator A5091392919 @default.
• W4316468747 date "2023-01-16" @default.
• W4316468747 modified "2023-09-29" @default.
• W4316468747 title "Status of deep learning for EEG-based brain–computer interface applications" @default.
• W4316468747 cites W1526657239 @default.
• W4316468747 cites W1971029950 @default.
• W4316468747 cites W1975717934 @default.
• W4316468747 cites W1990477627 @default.
• W4316468747 cites W2002055708 @default.
• W4316468747 cites W2043729845 @default.
• W4316468747 cites W2082367612 @default.
• W4316468747 cites W2087564647 @default.
• W4316468747 cites W2112561550 @default.
• W4316468747 cites W2117263102 @default.
• W4316468747 cites W2138257909 @default.
• W4316468747 cites W2149407814 @default.
• W4316468747 cites W2150590430 @default.
• W4316468747 cites W2151669316 @default.
• W4316468747 cites W2154848553 @default.
• W4316468747 cites W2171872429 @default.
• W4316468747 cites W2266999259 @default.
• W4316468747 cites W2485686941 @default.
• W4316468747 cites W2505869405 @default.
• W4316468747 cites W2533322963 @default.
• W4316468747 cites W2551178936 @default.
• W4316468747 cites W2551429043 @default.
• W4316468747 cites W2557301950 @default.
• W4316468747 cites W2565944610 @default.
• W4316468747 cites W2590420622 @default.
• W4316468747 cites W2623701936 @default.
• W4316468747 cites W2744802016 @default.
• W4316468747 cites W2768578923 @default.
• W4316468747 cites W2783178867 @default.
• W4316468747 cites W2790407631 @default.
• W4316468747 cites W2793397193 @default.
• W4316468747 cites W2794345050 @default.
• W4316468747 cites W2794747368 @default.
• W4316468747 cites W2883597459 @default.
• W4316468747 cites W2888355470 @default.
• W4316468747 cites W2889245000 @default.
• W4316468747 cites W2900786131 @default.
• W4316468747 cites W2900802277 @default.
• W4316468747 cites W2901730235 @default.
• W4316468747 cites W2902034646 @default.
• W4316468747 cites W2905523904 @default.
• W4316468747 cites W2908578648 @default.
• W4316468747 cites W2909739370 @default.
• W4316468747 cites W2909905004 @default.
• W4316468747 cites W2911969890 @default.
• W4316468747 cites W2913246999 @default.
• W4316468747 cites W2915893085 @default.
• W4316468747 cites W2930452824 @default.
• W4316468747 cites W2934123712 @default.
• W4316468747 cites W2940209306 @default.
• W4316468747 cites W2950097344 @default.
• W4316468747 cites W2954214015 @default.
• W4316468747 cites W2962777715 @default.
• W4316468747 cites W2962954310 @default.
• W4316468747 cites W2963822470 @default.
• W4316468747 cites W2963977071 @default.
• W4316468747 cites W2964065019 @default.
• W4316468747 cites W2966385318 @default.
• W4316468747 cites W2967205117 @default.
• W4316468747 cites W2971075653 @default.
• W4316468747 cites W2972694220 @default.
• W4316468747 cites W2983249795 @default.
• W4316468747 cites W2990886099 @default.
• W4316468747 cites W2991053967 @default.
• W4316468747 cites W2993348073 @default.
• W4316468747 cites W2994811811 @default.
• W4316468747 cites W3006715241 @default.
• W4316468747 cites W3006715270 @default.
• W4316468747 cites W3009764464 @default.
• W4316468747 cites W3012375271 @default.
• W4316468747 cites W3015271415 @default.
• W4316468747 cites W3015861963 @default.
• W4316468747 cites W3021025613 @default.
• W4316468747 cites W3027500411 @default.
• W4316468747 cites W3030231317 @default.
• W4316468747 cites W3035442710 @default.
• W4316468747 cites W3035471470 @default.
• W4316468747 cites W3044186523 @default.
• W4316468747 cites W3044699854 @default.
• W4316468747 cites W3045676091 @default.
• W4316468747 cites W3080551539 @default.
• W4316468747 cites W3081599307 @default.
• W4316468747 cites W3092342532 @default.
• W4316468747 cites W3094399329 @default.
• W4316468747 cites W3097417006 @default.
• W4316468747 cites W3101684563 @default.
• W4316468747 cites W3103608651 @default.
• W4316468747 cites W3106553357 @default.
• W4316468747 cites W3116731165 @default.
• W4316468747 cites W3117253464 @default.

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