Preprint: Effects of EEG Preprocessing on Channel-Wise Attention in Visual EEG Decoding
A preprint uses the Adaptive Thinking Mapper (ATM) model to empirically assess how EEG preprocessing strategies — a baseline MVNN-only pipeline versus a full cleaning pipeline integrating ICA and notch filtering — affect channel-attention representations in visual EEG decoding, using cross-generalization, noise-robustness, and spectro-temporal ablation analyses and examining the structural correspondence between data-driven attention weights and neurophysiological reference networks (GPDC, PDC, DTF). The researchers report the full pipeline suppressed non-neural artifacts such as frontal noise while maintaining comparable decoding accuracy and baseline robustness, and the learned attention spatial organization stayed stable across pipelines. The work has not been peer reviewed.