NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals

Motor Imagery (MI) is an emerging Brain–Computer Interface (BCI) paradigm in which a person imagines a body movement without any physical action. By decoding the scalp-recorded electroencephalography (EEG) signals, BCIs can establish direct communication pathways to control external devices, offering significant potential in prosthetics, rehabilitation, and human–computer interaction. However, existing solutions remain difficult to deploy in practice. (i) Most employ independent, opaque models for each MI task. This fragmented methodology lacks a unified architectural foundation. Consequently, these models are trained in isolation and fail to learn robust representations from diverse datasets, which often results in modest performance. (ii) They primarily adopt fixed sensor deployment, whereas real-world setups vary in electrode number and placement, causing models trained on one configuration to fail under another. (iii) Performance degrades sharply under low-SNR conditions typical of consumer-grade EEG. Together, these limitations hinder the practical adoption of MI-based BCIs.