Dataset Refinement for Improving the Generalization Ability of the EEG Decoding Model
This addresses the issue of noisy data in EEG decoding for brain-computer interfaces, which is an incremental improvement over assuming well-curated datasets.
The paper tackled the problem of noisy EEG datasets by designing a dataset refinement algorithm that removes noisy data based on training influence metrics, applied to two motor imagery EEG datasets and three models, resulting in consistently better generalization performance compared to using the original dataset.
Electroencephalography (EEG) is a generally used neuroimaging approach in brain-computer interfaces due to its non-invasive characteristics and convenience, making it an effective tool for understanding human intentions. Therefore, recent research has focused on decoding human intentions from EEG signals utilizing deep learning methods. However, since EEG signals are highly susceptible to noise during acquisition, there is a high possibility of the existence of noisy data in the dataset. Although pioneer studies have generally assumed that the dataset is well-curated, this assumption is not always met in the EEG dataset. In this paper, we addressed this issue by designing a dataset refinement algorithm that can eliminate noisy data based on metrics evaluating data influence during the training process. We applied the proposed algorithm to two motor imagery EEG public datasets and three different models to perform dataset refinement. The results indicated that retraining the model with the refined dataset consistently led to better generalization performance compared to using the original dataset. Hence, we demonstrated that removing noisy data from the training dataset alone can effectively improve the generalization performance of deep learning models in the EEG domain.