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About this paper
Mohammad Hossein Koohi Ghamsari, Seyede Fatemeh Ghamkhari, Siavash Bayat, Ahmed Hemani
The authors review motor imagery brain-computer interface research, connecting EEG signal processing, hardware implementation, and enabling technologies to assess routes toward usable assistive systems. They describe decoding motor imagery from scalp EEG and map methods reported across studies. CNNs appear in 35.70% of classification studies, versus SVMs in 28.10%. These figures measure method prevalence, not comparative accuracy; the proposed wearable system-on-chip remains conceptual and unevaluated.
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