AI RESEARCH
EEG-MFTNet: An Enhanced EEGNet Architecture with Multi-Scale Temporal Convolutions and Transformer Fusion for Cross-Session Motor Imagery Decoding
arXiv CS.AI
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ArXi:2604.05843v1 Announce Type: cross Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices, providing critical for individuals with motor impairments. However, accurate motor imagery (MI) decoding from electroencephalography (EEG) remains challenging due to noise and cross-session variability. This study