CNN-attention model decodes EEG to hand kinematics with within-subject PCCs above 0.98 on two axes, improved to 0.93 overall by a motion-state FSM copilot that drops under 20% of points, enabling simulated Franka Panda control.
Detection and classification of movement-related cortical potentials associated with task force and speed
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Copilot-Assisted Second-Thought Framework for Brain-to-Robot Hand Motion Decoding
CNN-attention model decodes EEG to hand kinematics with within-subject PCCs above 0.98 on two axes, improved to 0.93 overall by a motion-state FSM copilot that drops under 20% of points, enabling simulated Franka Panda control.