AI RESEARCH
Decoding Stimulus Reconstruction-Based Auditory Attention Robustly in Unbalanced EEG Datasets
arXiv CS.LG
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ArXi:2605.25605v1 Announce Type: cross In the past decade, numerous studies have applied deep neural networks (DNNs) to decode auditory attention (AAD) from Electroencephalogram (EEG) signals via stimulus reconstruction. However, the influence of dataset balance on the decoding performance of stimulus reconstruction-based AAD remains unexplored. In this study, three publicly available EEG-AAD datasets - KUL, DTU, and NJU cEEGrid - are used to construct both balanced and unbalanced experimental conditions.