Beyond Augmentation: Score-Guided Pathological Prior for EEG-based Depression Detection

• URL: http://arxiv.org/abs/2606.00180v1
• Date: Fri, 29 May 2026 14:45:33 GMT
• Title: Beyond Augmentation: Score-Guided Pathological Prior for EEG-based Depression Detection
• Abstract summary: Deep learning-based Major Depressive Disorder (MDD) detection using Electroencephalography (EEG) is constrained by the "small-sample dilemma"<n>We propose a novel framework "Beyond Augmentation": Score-Guided Classification (SGC)<n>SGC does not synthesize pseudo-samples; instead, it utilizes an unsupervised generative network architecture to model the structural and statistical anomaly degrees of samples.
• Score: 7.12129270303641
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning-based Major Depressive Disorder (MDD) detection using Electroencephalography (EEG) is fundamentally constrained by the "small-sample dilemma." Prevailing generative data augmentation methods not only incur heavy computational overhead but also risk introducing synthetic noise, thereby blurring classification boundaries. To challenge the traditional "data quantity first" convention, we propose a novel framework "Beyond Augmentation": Score-Guided Classification (SGC). SGC does not synthesize pseudo-samples; instead, it utilizes an unsupervised generative network architecture to model the structural and statistical anomaly degrees of samples, serving as the core "Pathological Prior". This prior, after robust normalization, is explicitly fused with deep feature representations, thereby precisely guiding the classifier's decision boundary. Furthermore, to dynamically adapt to varying channel configurations, we propose a Cross-Channel Spatial Adaptation module, utilizing a spatial mapping mechanism to effectively resolve the hardware heterogeneity of mismatched channels in multi-center datasets. Extensive experiments on the Mumtaz2016 and high-density MODMA datasets demonstrate the effectiveness and exceptional generalizability of our method under the challenging "zero data augmentation" setting and at "zero sample synthesis cost". Keywords: Electroencephalography (EEG), Depression Detection, Anomaly Score, Diffusion Models, Few-Shot Learning

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