A Method of Selecting Negative Examples from Clinical EEG by Combining Ensemble Learning and Uncertainty Estimation

Electroencephalogram (EEG) analysis is a critical tool for diagnosing various neurological disorders. Intelligent EEG models facilitate the analysis and diagnosis of these conditions. However, the development of such models relies on expert labeling of EEG signals. Due to limited time and resources, experts typically only label positive examples, leaving the selection of negative examples to be handled by algorithms. This paper proposes a novel method for selecting EEG negative samples by integrating ensemble learning with uncertainty estimation. The method employs a Stacking ensemble strategy, with AtcNet, USleep, and EEG Transformer as base learners, and Support Vector Machines (SVM) as meta-learners. By integrating two uncertainty metrics, the Variance Metric (VAR) and the Kullback-Leibler Divergence Metric (KL), this method selects high-quality EEG negative samples. Experimental results demonstrate that this approach significantly improves the model's performance. the quality of negative example samples selected by this method is better than that selected by any one of the models of AtcNet, USleep, and EEG Transformer alone. Compared to the baseline method, the proposed method enhances the macro-average F1 score by up to 5.67%. This study confirms the superiority of the proposed negative sample selection method and the effectiveness of the constructed dataset.