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Recent advances in neuroscience and engineering have resulted in brain-computer interface (BCI) devices that enhance the quality of life for people with movement limitations. BCI enables external devices to perform tasks using brain signals that are received, processed, and converted into commands by the brain. A widely used BCI paradigm based on electroencephalograms (EEGs) is motor imagery (MI), which has demonstrated potential as a tool for neurorehabilitation. In recent years, deep learning architectures have gained considerable attention for their ability to analyze EEG signals. This review paper focuses on applying deep learning for MI EEG classification in controlling lower-limb rehabilitation exoskeletons. Finally, current issues and potential directions will be discussed.

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Classification of MI EEG Signal Using Deep Learning Architectures for a Lower-Limb Rehabilitation Exoskeleton

  • 会议论文

  • 发表日期：

    2025年

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  • Maryam Khoshkhooy Titkanlou,
  • Roman Mouček

摘要

Recent advances in neuroscience and engineering have resulted in brain-computer interface (BCI) devices that enhance the quality of life for people with movement limitations. BCI enables external devices to perform tasks using brain signals that are received, processed, and converted into commands by the brain. A widely used BCI paradigm based on electroencephalograms (EEGs) is motor imagery (MI), which has demonstrated potential as a tool for neurorehabilitation. In recent years, deep learning architectures have gained considerable attention for their ability to analyze EEG signals. This review paper focuses on applying deep learning for MI EEG classification in controlling lower-limb rehabilitation exoskeletons. Finally, current issues and potential directions will be discussed.