BiND: A Neural Discriminator-Decoder for Accurate Bimanual Trajectory Prediction in Brain-Computer Interfaces

2025/08/19 by Robert, Timothee, Shaeri, MohammadAli, Shoaran, Mahsa
#Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Neurons and Cognition (q-bio.NC) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2509.03521

Abstract

Decoding bimanual hand movements from intracortical recordings remains a critical challenge for brain-computer interfaces (BCIs), due to overlapping neural representations and nonlinear interlimb interactions. We introduce BiND (Bimanual Neural Discriminator-Decoder), a two-stage model that first classifies motion type (unimanual left, unimanual right, or bimanual) and then uses specialized GRU-based decoders, augmented with a trial-relative time index, to predict continuous 2D hand velocities. We benchmark BiND against six state-of-the-art models (SVR, XGBoost, FNN, CNN, Transformer, GRU) on a publicly available 13-session intracortical dataset from a tetraplegic patient. BiND achieves a mean R² of 0.76 (±0.01) for unimanual and 0.69 (±0.03) for bimanual trajectory prediction, surpassing the next-best model (GRU) by 2% in both tasks. It also demonstrates greater robustness to session variability than all other benchmarked models, with accuracy improvements of up to 4% compared to GRU in cross-session analyses. This highlights the effectiveness of task-aware discrimination and temporal modeling in enhancing bimanual decoding.

Citations

• Walking naturally after spinal cord injury using a brain–spine interface
• Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs
• A brain-computer interface that evokes tactile sensations improves robotic arm control
• High-performance brain-to-text communication via handwriting
• Non-local Neural Networks
• Machine learning for neural decoding
• Attention Is All You Need
• Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency

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