aceNonlinear Feature Modeling Using Kolmogorov-Arnold Networks in Electroencephalography for Depression Classification: Deep Learning Study
Background Depression is a common mental disorder that seriously affects mental health and quality of life. Traditional depression diagnosis mainly relies on clinical interviews and subjective assessments, which may lead to variability in diagnostic outcomes. Electroencephalogram (EEG)-based computer-aided diagnosis has emerged as a promising, objective approach for depression recognition. However, effectively modeling the complex nonlinear characteristics of EEG signals remains a major challenge for existing de…
A ConvKAN Efficient model classified depression from EEG signals with accuracy, precision, recall, and F1-score all above 94% on the EDRA dataset, outperforming conventional CNN and machine learning baselines.
Evidence
- Peer-reviewedJMIR AI2026-10-19
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Truvace Impact Record TRV-2026-2083, v1: “Nonlinear Feature Modeling Using Kolmogorov-Arnold Networks in Electroencephalography for Depression Classification: Deep Learning Study.” Truvace, 2026-10-21. /record/TRV-2026-2083 (accessed at citation time). sha256 087d80bcc2cb44f1…
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