融合连续与分词表示，实现跨被试抓握力实时解码
EEGForceFusion: Joint Tokenised-Continuous Representation Learning for Subject-Independent Grasp Force Decoding
- 结合卷积循环与量化分词，统一建模神经信号的细粒度与长时依赖
- 在离线与模拟实时下分别达到R²=0.817与R²=0.793，性能稳定
- 适合助老机器人、神经康复等需低延迟脑机交互场景
脑机接口通过非侵入式脑电图（EEG）连接神经活动与外部设备，助力运动功能恢复并推动人机交互发展。然而，连续抓握力解码仍面临复杂时间动态、高个体差异及现有方法泛化性差等挑战。为此，我们提出一种混合解码框架，联合建模连续与分词表示，以捕捉神经信号的精细结构与长程时序依赖。该方法整合卷积-循环表征学习、基于量化分词技术及变压器时序建模，构建统一的融合回归架构。在WAY-EEG-GAL数据集上，严格采用留一被试交叉验证，离线评估获得R²=0.817，模拟实时评估达R²=0.793，且延迟满足实时部署需求。结果表明该方法具备强跨被试泛化能力，证实混合连续-分词表示在助残机器人、神经康复与人机交互中实现实时脑控抓握力的可行性。
原文摘要 · Abstract (English)
Brain-machine interfaces provide a link between neural activity and external devices, enabling restoration of motor function and advancing human-machine interaction using non-invasive electroencephalography (EEG). However, continuous grasp force decoding remains challenging due to complex temporal dynamics, high inter-subject variability, and limited generalisation of existing approaches. To address this, we propose a hybrid EEG decoding framework that jointly models continuous and tokenised representations, enabling capture of both fine-grained neural structure and long-range temporal dependencies. The proposed approach integrates convolutional-recurrent representation learning, quantisation-based tokenisation, and transformer-based temporal modelling within a unified fusion-based regression architecture. Experimental evaluation on the WAY-EEG-GAL dataset under strict leave-one-subject-out conditions achieves $R^2$ = 0.817 in offline settings and $R^2$ = 0.793 in simulated real-time evaluation, with latency suitable for real-time deployment. These results demonstrate strong cross-subject generalisation and highlight the practicality of hybrid continuous-tokenised representations for real-time EEG-based force decoding in assistive robotics, neuro-rehabilitation, and human-machine interaction.
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