The Impact of Cross-Validation Schemes for EEG-Based Auditory Attention Detection with Deep Neural Networks.

Abstract

This study assesses the performance of different cross-validation splits for brain-signal-based Auditory Attention Decoding (AAD) using deep neural networks on three publicly available Electroencephalography datasets. We investigate the effect of trial-specific knowledge during training and assess adaptability to diverse scenarios with a trial-independent split. Introducing a causal time-series split, and simulating online decoding, our results demonstrate a consistent performance increase for auditory attention classification. These positive outcomes provide valuable insights for the development of future brain-signal-based AAD systems, emphasizing the potential for practical, person-dependent AAD applications. The results highlight the importance of diverse evaluation methodologies for enhancing generalizability in developing effective neurofeedback systems and assistive technologies for auditory processing disorders under more real-life conditions.