Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method

• URL: http://arxiv.org/abs/2503.13475v1
• Date: Tue, 04 Mar 2025 13:16:11 GMT
• Title: Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method
• Authors: ZhongYi Zhang, ChenYang Xu, LiXuan Zhao, HuiRang Hou, QingHao Meng,
• Abstract summary: Depression Level classification based on GCN model.<n>Inspired by the model learning patterns, we introduced two novel modules.<n> DepL-GCN achieved accuracies of 81.13% and 81.36% for multi-class severity recognition.
• Score: 8.832488069619947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring,widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/normal), lacking granularity for severity assessment. To address this, we proposed the DepL-GCN, i.e., Depression Level classification based on GCN model. This model tackles two key challenges: (1) subjectivity in depres-sion-level labeling due to patient self-report biases, and (2) class imbalance across severity categories. Inspired by the model learning patterns, we introduced two novel modules: the sample confidence module and the minority sample penalty module. The former leverages the L2-norm of prediction errors to progressively filter EEG samples with weak label alignment during training, thereby reducing the impact of subjectivity; the latter automatically upweights misclassified minority-class samples to address imbalance issues. After testing on two public EEG datasets, DepL-GCN achieved accuracies of 81.13% and 81.36% for multi-class severity recognition, outperforming baseline models.Ablation studies confirmed both modules' contributions. We further discussed the strengths and limitations of regression-based models for depression-level recognition.

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