The Landscape of Machine Learning for EEG Hand Motor Imagery Decoding: A Scoping Review (2020-2025)
Resumo
EEG-based Brain-Computer Interfaces (BCIs) show promise for motor rehabilitation, with hand motor imagery (MI) being a key paradigm. This scoping review analyzed 128 studies, mapping machine learning and deep learning architectures, training strategies, and performance metrics. Results reveal predominance of deep learning, and methodological diversity, but most studies are offline and subject-dependent, limiting reproducibility and real-time application. The review aims to highlight gaps and standardized evaluation protocols to advance practical BCIs.Referências
Bhalerao, S. V. and Pachori, R. B. (2024). Essdm: An enhanced sparse swarm decomposition method and its application in multi-class motor imagery–based eeg-bci system. TechRxiv.
Das, A., Singh, S., Kim, J., et al. (2025). Enhanced eeg signal classification in brain computer interfaces using hybrid deep learning models. Scientific Reports, 15:27161.
Finnis, R., Mehmood, A., Henning, H., and Iqbal, J. (2025). Exploring imagined movement for brain–computer interface control: An fnirs and eeg review. Brain Sciences, 15(9):1013.
Firoz, K. F., Seong, Y., and Yi, S. (2024). A preliminary study of neural signals of motor imagery task of arm movements through electroencephalography data classification with machine learning. In 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS), pages 1–6.
Li, M., Zuo, H., Zhou, H., Xu, G., and Qi, E. (2023). A study of action difference on motor imagery based on delayed matching posture task. Journal of Neural Engineering, 20(1).
Li, W., Shi, P., and Yu, H. (2021). Gesture recognition using surface electromyography and deep learning for prostheses hand: State-of-the-art, challenges, and future. Frontiers in Neuroscience, 15:621885.
Liu, Y., Jiang, L., Liu, H., and Ming, D. (2021). A systematic analysis of hand movement functionality: Qualitative classification and quantitative investigation of hand grasp behavior. Frontiers in Neurorobotics, 15:658075.
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., and Yger, F. (2018). A review of classification algorithms for eeg-based brain–computer interfaces: A 10-year update. Journal of Neural Engineering, 15(3):031005.
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., Lewin, S., Godfrey, C. M., Macdonald, M. T., Langlois, E. V., Soares-Weiser, K., Moriarty, J., Clifford, T., Tunçalp, , and Straus, S. E. (2018). Prisma extension for scoping reviews (prisma-scr): Checklist and explanation. Annals of Internal Medicine, 169(7):467–473.
Vafaei, E. and Hosseini, M. (2025). Transformers in eeg analysis: A review of architectures and applications in motor imagery, seizure, and emotion classification. Sensors, 25(5):1293.
Das, A., Singh, S., Kim, J., et al. (2025). Enhanced eeg signal classification in brain computer interfaces using hybrid deep learning models. Scientific Reports, 15:27161.
Finnis, R., Mehmood, A., Henning, H., and Iqbal, J. (2025). Exploring imagined movement for brain–computer interface control: An fnirs and eeg review. Brain Sciences, 15(9):1013.
Firoz, K. F., Seong, Y., and Yi, S. (2024). A preliminary study of neural signals of motor imagery task of arm movements through electroencephalography data classification with machine learning. In 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS), pages 1–6.
Li, M., Zuo, H., Zhou, H., Xu, G., and Qi, E. (2023). A study of action difference on motor imagery based on delayed matching posture task. Journal of Neural Engineering, 20(1).
Li, W., Shi, P., and Yu, H. (2021). Gesture recognition using surface electromyography and deep learning for prostheses hand: State-of-the-art, challenges, and future. Frontiers in Neuroscience, 15:621885.
Liu, Y., Jiang, L., Liu, H., and Ming, D. (2021). A systematic analysis of hand movement functionality: Qualitative classification and quantitative investigation of hand grasp behavior. Frontiers in Neurorobotics, 15:658075.
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., and Yger, F. (2018). A review of classification algorithms for eeg-based brain–computer interfaces: A 10-year update. Journal of Neural Engineering, 15(3):031005.
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., Lewin, S., Godfrey, C. M., Macdonald, M. T., Langlois, E. V., Soares-Weiser, K., Moriarty, J., Clifford, T., Tunçalp, , and Straus, S. E. (2018). Prisma extension for scoping reviews (prisma-scr): Checklist and explanation. Annals of Internal Medicine, 169(7):467–473.
Vafaei, E. and Hosseini, M. (2025). Transformers in eeg analysis: A review of architectures and applications in motor imagery, seizure, and emotion classification. Sensors, 25(5):1293.
Publicado
12/11/2025
Como Citar
BITENCOURT, Felipe Correa; PEREIRA, Eduarda Tessari; SALVALAIO, Claudio; BORGES JUNIOR, Voltaire Augusto; LACERDA, Marcelo Scarparo; LIMA, Alexandre; OLIVEIRA JR, Alcyr Alves de.
The Landscape of Machine Learning for EEG Hand Motor Imagery Decoding: A Scoping Review (2020-2025). In: ESCOLA REGIONAL DE APRENDIZADO DE MÁQUINA E INTELIGÊNCIA ARTIFICIAL DA REGIÃO SUL (ERAMIA-RS), 1. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 408-411.
DOI: https://doi.org/10.5753/eramiars.2025.16769.
