EEG decoding with conditional identification information
This addresses the problem of inter-person variability in EEG decoding for brain-computer interfaces, representing an incremental improvement over existing deep learning methods.
The paper tackled the challenge of decoding EEG signals for unseen individuals by incorporating conditional identification information into a neural network, resulting in substantially boosted accuracy on the WithMe dataset for both seen and unseen subjects.
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.