{"ID":2880626,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.03521","arxiv_id":"2509.03521","title":"BiND: A Neural Discriminator-Decoder for Accurate Bimanual Trajectory Prediction in Brain-Computer Interfaces","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 $R^2$ of 0.76 ($\\pm$0.01) for unimanual and 0.69 ($\\pm$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.","short_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...","url_abs":"https://arxiv.org/abs/2509.03521","url_pdf":"https://arxiv.org/pdf/2509.03521v1","authors":"[\"Timothee Robert\",\"MohammadAli Shaeri\",\"Mahsa Shoaran\"]","published":"2025-08-19T10:18:41Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"cs.AI\",\"eess.SP\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}