[2604.01889] LI-DSN: A Layer-wise Interactive Dual-Stream Network for EEG Decoding

Computer Science > Machine Learning arXiv:2604.01889 (cs) [Submitted on 2 Apr 2026] Title:LI-DSN: A Layer-wise Interactive Dual-Stream Network for EEG Decoding Authors:Chenghao Yue, Zhiyuan Ma, Zhongye Xia, Xinche Zhang, Yisi Zhang, Xinke Shen, Sen Song View a PDF of the paper titled LI-DSN: A Layer-wise Interactive Dual-Stream Network for EEG Decoding, by Chenghao Yue and 6 other authors View PDF HTML (experimental) Abstract:Electroencephalography (EEG) provides a non-invasive window into brain activity, offering high temporal resolution crucial for understanding and interacting with neural processes through brain-computer interfaces (BCIs). Current dual-stream neural networks for EEG often process temporal and spatial features independently through parallel branches, delaying their integration until a final, late-stage fusion. This design inherently leads to an "information silo" problem, precluding intermediate cross-stream refinement and hindering spatial-temporal decompositions essential for full feature utilization. We propose LI-DSN, a layer-wise interactive dual-stream network that facilitates progressive, cross-stream communication at each layer, thereby overcoming the limitations of late-fusion paradigms. LI-DSN introduces a novel Temporal-Spatial Integration Attention (TSIA) mechanism, which constructs a Spatial Affinity Correlation Matrix (SACM) to capture inter-electrode spatial structural relationships and a Temporal Channel Aggregation Matrix (TCAM) to integ...