EEG decoding with conditional identification information
Pengfei Sun Jorg De Winne Paul Devos Dick Botteldooren
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 information of each individual into the neural network, thereby
enhancing model representation through the synergistic interaction of EEG and
personal traits. We test our model on the WithMe dataset and demonstrated that
the inclusion of these identifiers substantially boosts accuracy for both
subjects in the training set and unseen subjects. This enhancement suggests
promising potential for improving for EEG interpretability and understanding of
relevant identification features.