Decoding Stimulus Reconstruction-Based Auditory Attention Robustly in Unbalanced EEG Datasets
arXiv:2605.25605v1 Announce Type: cross Abstract: 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
The proliferation of deep learning in brain-computer interfaces necessitates robust performance across varied, real-world data, making the study of dataset balance critical now.
Improving the robustness of brain-computer interfaces, particularly those relying on EEG, could accelerate human-computer interaction and mental control applications.
Research is advancing our understanding of how data imbalance affects the reliability of deep learning models in EEG-based auditory attention decoding, leading to more practical and dependable systems.
- · Brain-Computer Interface researchers
- · Medical technology developers
- · Neuroscience community
- · AI algorithm developers
- · Developers of non-robust BCI systems
- · Research reliant on perfectly balanced datasets
Improved reliability and broader applicability of EEG-based auditory attention decoding systems.
Accelerated development of assistive technologies controlled via brain signals, such as hearing aids or communication devices.
Enhanced understanding of neural processing under noisy or incomplete data conditions, informing general AI resilience.
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