EEG decoding with conditional identification information

TL;DR

This paper proposes a neural network approach that incorporates individual identification information to improve EEG decoding accuracy, especially for unseen subjects, advancing brain-computer interface capabilities.

Contribution

It introduces a novel method that integrates personal traits into EEG decoding models, enhancing performance on both known and new subjects.

Findings

Significant accuracy improvement with personal trait integration

Enhanced decoding performance for unseen individuals

Potential for better EEG interpretability and identification

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 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.