Decoding Stimulus Reconstruction-Based Auditory Attention Robustly in Unbalanced EEG Datasets
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. We hypothesize and demonstrate that stimulus reconstruction-based DNN decoders tend to produce overestimated decoding performance on unbalanced datasets. To address this issue, we propose a leave-one-paired-envelope-out (LOPE...
| Authors | Yuanming Zhang, Yayun Liang, Zhibin Lin et al. |
|---|---|
| Primary category | eess.AS |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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