DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding
In real-world applications of noninvasive electroencephalography (EEG), specialized decoders often show limited generalizability across diverse tasks under subject-independent settings. One central challenge is that task-relevant EEG signals often follow different temporal organization patterns across tasks, while many existing methods rely on task-tailored architectural designs that introduce task-specific temporal inductive biases. This mismatch makes it difficult to adapt temporal modeling across tasks without changing the model configuration. To address these challenges, we propose DSAINet, an efficient dual-scale attentive interaction network for general EEG decoding. Specifically, D...
| Authors | Zhiyuan Ma, Zeyuan Li, Zihao Qiu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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