EEGForceFusion：用于独立于主体的抓握力解码的联合令牌化-连续表示学习
EEGForceFusion: Joint Tokenised-Continuous Representation Learning for Subject-Independent Grasp Force Decoding
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- IIT Gandhinagar（印度理工学院甘地纳格尔分校）
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中文总结 AI 辅助
研究针对连续抓握力解码难题，提出混合EEG解码框架，联合建模连续和令牌化表示，集成多种技术于统一架构，实验表明该方法在WAY-EEG-GAL数据集上跨主体泛化能力强，适用于实时力解码。
中文摘要 AI 辅助
脑机接口在神经活动和外部设备之间建立联系，利用非侵入性脑电图（EEG）恢复运动功能并推动人机交互。然而，由于复杂的时间动态、高个体间变异性以及现有方法的有限泛化性，连续抓握力解码仍然具有挑战性。为解决此问题，我们提出了一种混合EEG解码框架，该框架联合对连续和令牌化表示进行建模，能够捕捉细粒度神经结构和长程时间依赖性。所提出的方法在基于融合的统一回归架构中集成了卷积循环表示学习、基于量化的令牌化和基于Transformer的时间建模。在WAY-EEG-GAL数据集上进行的严格留一主体出条件下的实验评估，在离线设置中实现了\(R^2 = 0.817\)，在模拟实时评估中实现了\(R^2 = 0.793\)，延迟适合实时部署。这些结果证明了强大的跨主体泛化能力，并突出了混合连续-令牌化表示在辅助机器人、神经康复和人机交互中基于EEG的实时力解码的实用性。
英文摘要
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.
