EEGForceFusion: Joint Tokenised-Continuous Representation Learning for Subject-Independent Grasp Force Decoding
2026-07-27 • Human-Computer Interaction
Human-Computer InteractionArtificial IntelligenceEmerging Technologies
AI summaryⓘ
The authors developed a new method to better understand brain signals from EEG for controlling devices that mimic hand grip strength. They combined detailed and simplified ways of reading the brain activity using special AI models to capture both quick and longer-term brain patterns. Testing on a public dataset showed their method works well across different people and is fast enough for real-time use. This approach could help improve assistive robots and rehabilitation tools that rely on brain control.
Brain-machine interfaceEEGContinuous grasp force decodingConvolutional neural networksRecurrent neural networksTransformer modelsTokenisationCross-subject generalisationReal-time decodingNeuro-rehabilitation
Authors
Sankalp Sunil Turankar, Yogesh Kumar Meena
Abstract
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.