NVC-DSDNet: neurovascular coupling-aware delay alignment and subspace disentanglement network for hybrid EEG–fNIRS brain–computer interfaces
Year: 2026
Type: article
Abstract: Hybrid EEG–fNIRS brain–computer interfaces combine rapid electrophysiological activity with hemodynamic information, but neurovascular latency and entangled multimodal features hinder effective fusion. We propose the Neurovascular Coupling-Aware Delay Alignment and Subspace Disentanglement Network (NVC-DSDNet), which couples delay-aware attention with shared–private neural subspaces. EEG responses serve as temporal anchors that retrieve task-relevant fNIRS features over candidate delays, while Neural Feature Subspace Decomposition separates shared neural intent from modality-specific information. An adaptive multi-head decision module then weights unimodal and fused predictions at the sample level. In subject-independent five-fold cross-validation, NVC-DSDNet achieved accuracies of 72.67% for motor imagery, 82.79% for mental arithmetic, and 76.17% for word generation. Learned delay weights peaked at 4.5–5.5 s for motor imagery and shifted to 6.0–9.0 s for mental arithmetic, consistent with task-dependent hemodynamic time courses. These results show that coupling-aware temporal alignment and subspace disentanglement can improve multimodal neural decoding while retaining neurophysiological interpretability.
Source: Complex & Intelligent Systems
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W7225652864
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