EEG decoding with conditional identification information

arXiv:2403.15489v1 Announce Type: cross
Abstract: Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces. Traditional machine learning algorithms have been hindered by the high noise levels and inherent inter-person variations in EEG signals. Recent advances in deep neural networks (DNNs) have shown promise, owing to their advanced nonlinear modeling capabilities. However, DNN still faces challenge in decoding EEG samples of unseen individuals. To address this, this paper introduces a novel approach by incorporating the conditional identification …

abstract advanced advances algorithms arxiv brain capabilities computer cs.ai cs.hc cs.lg decoding eeg eess.sp human identification information interfaces machine machine learning machine learning algorithms modeling networks neural networks noise person traditional machine learning type