基于时空多级交互编码的自适应开放词汇脑电解码OA

Decoding electroencephalography(EEG)signals into natural language text is a fundamental task in brain-computer interface(BCI)research.Existing approaches often suffer from insufficient modeling of spatiotemporal EEG characteristics,limited capability in capturing hierarchical semantic relationships,and poor generalization across subjects.To address these issues,this study proposes a spatio-temporal multi-level adaptive cross-subject interactive attention network(ST-MSIAN).First,the spatiotemporal feature enhancemant module(SFEM)employs parallel temporal and spatial branches to jointly model temporal dynamics and channel dependencies of EEG signals.Multi-scale dilated convolutions with dilation rates of 1,2,and 4 are adopted to capture both local and long-range temporal dependencies,while spatial convolutions and attention mechanisms enhance inter-channel representations.Subsequently,subject-specific tokens are introduced to explicitly characterize individual neural patterns and reduce the influence of inter-subject variability.Based on these representations,the multi-level interactive attention mechanism(MIAM)progressively extracts hierarchical semantic information through channel-level,subject-level,and global-level attention interactions.The resulting features are further processed by a six-layer Transformer encoder to model contextual dependencies.Finally,a KAN-based projection head with learnable activation functions is employed to establish a more effective nonlinear mapping between high-dimensional EEG features and the language embedding space of a pretrained BART decoder,enabling accurate text generation.Experiments are conducted on the publicly available Zurich cognitive language processing corpus(ZuCo),including both ZuCo v1.0 and ZuCo v2.0 datasets.The proposed ST-MSIAN achieves a BLEU-1 score of 44.10%,a BLEU-3 score of 16.21%,a ROUGE-F score of 37.62%,and a BERTScore-F score of 60.08%.Compared with the strongest competing method,the proposed approach improves BLEU-1,ROUGE-F,and BERTScore-F by 2.23,4.87,and 4.66 percentage points,respectively.The proposed ST-MSIAN provides an effective and robust solution for cross-subject neural language decoding and offers promising potential for future applications in assistive communication systems and intelligent brain-computer interfaces.