用分阶段方法提升脑机接口双手运动预测精度
BiND: A Neural Discriminator-Decoder for Accurate Bimanual Trajectory Prediction in Brain-Computer Interfaces
- 先分类动作类型，再用时序模型精准预测双手轨迹
- 预测准确率均超现有模型2%，跨会话表现更稳定
- 适合需要高精度双手控制的瘫痪患者脑机接口研究
从皮层内记录中解码双手运动仍是脑机接口的关键挑战，源于神经信号重叠和双肢间的非线性交互。本文提出BiND（双手神经判别-解码器），一种两阶段模型：首先分类运动类型（单手左、单手右或双手），随后使用带试验相对时间索引的GRU解码器预测连续2D手部速度。在一名四肢瘫痪患者公开的13会话皮层内数据集上，与六种先进模型（SVR、XGBoost、FNN、CNN、Transformer、GRU）对比，BiND在单手任务上达到平均R²为0.76（±0.01），双手任务为0.69（±0.03），均优于次优模型GRU 2%。在跨会话分析中，其性能比GRU最高提升4%，表现出更强鲁棒性。结果表明，任务感知判别与时序建模对提升双手解码有效。
原文摘要 · Abstract (English)
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.
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