Topology Aware EEG Classification Using Persistent Images and Color Equivariant CNNs

Abstract

Electroencephalography owing to its high temporal resolution and non-invasive nature, holds significant potential for auxiliary diagnosis in neuropsychiatric disorders such as schizophrenia. However, conventional EEG analysis methods often fall short in capturing high-order structural features of complex brain functional connectivity and in achieving robust generalization across subjects. To address these limitations, this study proposes a novel image classification framework, CEResNet, which integrates topological data analysis with color-equivariant convolutional neural networks. Specifically, the proposed method first employs persistent homology to extract multi-scale high-order topological features from multi-band EEG signals. These features are then transformed into persistence images via Gaussian kernel mapping, serving as inputs for the deep learning model. A color-equivariant convolutional module is subsequently embedded into the ResNet-18 architecture to enhance the model’s robustness against color distribution shifts. Experimental results demonstrate that the proposed approach achieves a classification accuracy of 0.9231 in distinguishing schizophrenia patients from healthy controls, significantly outperforming various traditional machine learning baselines and existing comparative models. These findings validate the effectiveness of encoding topological structures into images combined with color-equivariant modeling strategies, offering a novel perspective for intelligent identification of neuropsychiatric disorders.