@inproceedings{5e7609bec7bd4d08a571460408de7b8f,
title = "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.",
keywords = "Auditory attention decoding, Causal-Split, Deep neural networks, EEG, Online decoding",
author = "Gabriel Ivucic and Saurav Pahuja and Felix Putze and Siqi Cai and Haizhou Li and Tanja Schultz",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 ; Conference date: 15-07-2024 Through 19-07-2024",
year = "2024",
doi = "10.1109/EMBC53108.2024.10782636",
language = "英语",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings",
address = "美国",
}