{"ID":2883105,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08681","arxiv_id":"2508.08681","title":"Multi-dimensional Neural Decoding with Orthogonal Representations for Brain-Computer Interfaces","abstract":"Current brain-computer interfaces primarily decode single motor variables, limiting their ability to support natural, high-bandwidth neural control that requires simultaneous extraction of multiple correlated motor dimensions. We introduce Multi-dimensional Neural Decoding (MND), a task formulation that simultaneously extracts multiple motor variables (direction, position, velocity, acceleration) from single neural population recordings. MND faces two key challenges: cross-task interference when decoding correlated motor dimensions from shared cortical representations, and generalization issues across sessions, subjects, and paradigms. To address these challenges, we propose OrthoSchema, a multi-task framework inspired by cortical orthogonal subspace organization and cognitive schema reuse. OrthoSchema enforces representation orthogonality to eliminate cross-task interference and employs selective feature reuse transfer for few-shot cross-session, subject and paradigm adaptation. Experiments on macaque motor cortex datasets demonstrate that OrthoSchema significantly improves decoding accuracy in cross-session, cross-subject and challenging cross-paradigm generalization tasks, with larger performance improvements when fine-tuning samples are limited. Ablation studies confirm the synergistic effects of all components are crucial, with OrthoSchema effectively modeling cross-task features and capturing session relationships for robust transfer. Our results provide new insights into scalable and robust neural decoding for real-world BCI applications.","short_abstract":"Current brain-computer interfaces primarily decode single motor variables, limiting their ability to support natural, high-bandwidth neural control that requires simultaneous extraction of multiple correlated motor dimensions. We introduce Multi-dimensional Neural Decoding (MND), a task formulation that simultaneously...","url_abs":"https://arxiv.org/abs/2508.08681","url_pdf":"https://arxiv.org/pdf/2508.08681v1","authors":"[\"Kaixi Tian\",\"Shengjia Zhao\",\"Yuhan Zhang\",\"Shan Yu\"]","published":"2025-08-12T06:59:30Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}