Multi-dimensional Neural Decoding with Orthogonal Representations for Brain-Computer Interfaces

TL;DR

This paper introduces Multi-dimensional Neural Decoding (MND) and OrthoSchema, a framework that enhances decoding of multiple motor variables from neural data, improving generalization and robustness for brain-computer interfaces.

Contribution

The paper proposes OrthoSchema, a novel multi-task framework inspired by cortical orthogonal organization, to improve multi-dimensional neural decoding and transfer learning in BCIs.

Findings

Significant accuracy improvements in cross-session, cross-subject, and cross-paradigm decoding.

OrthoSchema effectively models cross-task features and session relationships.

Ablation studies confirm the importance of all components for robust transfer.

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.