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Neurological diseases are a significant health threat, often presenting through abnormalities in electroencephalogram (EEG) signals during seizures. In recent years, machine learning (ML) technologies have been explored as a means of automated EEG pathology diagnosis. However, existing ML-based EEG binary classification methods largely focus on extracting EEG-related features, which may lead to poor performance in classifying EEG signals by overlooking potentially redundant information. In this paper, we propose a novel Kruskal–Wallis (KW) test-based framework for EEG pathology detection. Our framework first divides EEG data into frequency sub-bands using wavelet packet decomposition and then extracts statistical characteristics from each selected coefficient. Next, the piecewise aggregation approximation technique is used to obtain the aggregated feature vectors, followed by the KW statistical test methodology to select significant features. Finally, three ensemble learning classifiers, random forest, categorical boosting (CatBoost), and light gradient boosting machine, are used to classify the extracted significant features into normal or abnormal classes. Our proposed framework achieves an accuracy of 89.13%, F1-score of 87.60%, and G-mean of 88.60%, respectively, outperforming other competing techniques on the same dataset, which shows the great promise in EEG pathology detection. [ABSTRACT FROM AUTHOR]

Deep learning models, especially convolutional neural networks (CNNs), have shown remarkable achievements in clinical electroencephalography (EEG) pathology detection, due to their prominent capability in automatic feature learning. However, most existing CNN-based models ignore the spatial correlations of the EEG signals and the latent complementary information provided by different convolutional layers, which is essentially an important clue for pathology detection. To this end, we propose SCNet, a S patial Feature Fused C onvolutional Net work, for multi-channel EEG pathology detection. SCNet first designs a spatial information learning mechanism to capture the channel-wise spatial correlations via global pooling strategies. A multi-level feature fusion module, which combines feature maps learned by different convolutional layers, is then devised to fully leverage the complementarity of multi-level features. The efficacy of the proposed SCNet is experimentally evaluated on both datasets, and the results obtained outperform current representatives in the literature. Ablation studies on the spatial information learning and multi-level fusion modules further confirm their great potential in EEG-based diagnostic applications. • A novel solution is proposed to learn subtle spatial information from EEG signals. • The proposed multi-feature fusion module enhances the capability of feature expression. • The designed network can extract differentiated features and perform classification directly. • Promising results show that our method outperforms state-of-the-art baselines. [ABSTRACT FROM AUTHOR]