Copilot-Assisted Second-Thought Framework for Brain-to-Robot Hand Motion Decoding

• URL: http://arxiv.org/abs/2603.27492v1
• Date: Sun, 29 Mar 2026 03:12:08 GMT
• Title: Copilot-Assisted Second-Thought Framework for Brain-to-Robot Hand Motion Decoding
• Abstract summary: Motor kinematics prediction from electroencephalography (EEG) is an important research area for developing movement-related brain-computer interfaces (BCIs)<n>We propose a CNN-attention hybrid model for decoding hand kinematics from EEG during grasp-and-lift tasks.<n>We extend this approach to EEG-EMG multimodal decoding, which yields substantially improved results.
• Score: 11.483725773928379
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motor kinematics prediction (MKP) from electroencephalography (EEG) is an important research area for developing movement-related brain-computer interfaces (BCIs). While traditional methods often rely on convolutional neural networks (CNNs) or recurrent neural networks (RNNs), Transformer-based models have shown strong ability in modeling long sequential EEG data. In this study, we propose a CNN-attention hybrid model for decoding hand kinematics from EEG during grasp-and-lift tasks, achieving strong performance in within-subject experiments. We further extend this approach to EEG-EMG multimodal decoding, which yields substantially improved results. Within-subject tests achieve PCC values of 0.9854, 0.9946, and 0.9065 for the X, Y, and Z axes, respectively, computed on the midpoint trajectory between the thumb and index finger, while cross-subject tests result in 0.9643, 0.9795, and 0.5852. The decoded trajectories from both modalities are then used to control a Franka Panda robotic arm in a MuJoCo simulation. To enhance trajectory fidelity, we introduce a copilot framework that filters low-confidence decoded points using a motion-state-aware critic within a finite-state machine. This post-processing step improves the overall within-subject PCC of EEG-only decoding to 0.93 while excluding fewer than 20% of the data points.

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