A Novel Adversarial Approach for EEG Dataset Refinement: Enhancing Generalization Through Proximity-to-Boundary Scoring.

As deep learning (DL) performs remarkably in pattern recognition from complex data, it is used to interpret user intentions from electroencephalography (EEG) signals. However, the DL models trained on EEG datasets have low generalization ability owing to numerous noisy samples in datasets. Therefore, prior research has focused on distinguishing and eliminating noisy samples from datasets. One intuitive solution is based on the property of noisy samples during the training phase. Noisy samples are located near the decision boundary after model training. Therefore, they can be detected using a gradient-based adversarial attack. However, the limitations of usability exist because the intuitive solution requires additional hyperparameter optimizations, resulting in a tradeoff between accurateness and efficiency. In this article, we proposed a novel training framework that enhances the generalization ability of the model by reducing the influence of noisy samples during training, without additional hyperparameter optimizations. We designed the proximity-to-boundary score (PBS) to continuously measure the data closeness to the decision boundary. As a result, the proposed framework improved the generalization ability of the model across two motor imagery datasets and one sleep stage dataset. Specifically, training the model with the proposed framework resulted in performance improvements ranging from a minimum of 1.43% to a maximum of 6.66% on two motor imagery datasets and from a minimum of 0.72% to a maximum of 2.85% on a sleep stage classification dataset. We qualitatively confirmed that data with low PBS are indeed noisy samples and degrade the model training. Hence, we demonstrated that employing the proposed framework accurately and efficiently mitigates the influence of noisy samples, enhancing the model's generalization capabilities.