EEG-Based Diagnosis of Major Depressive Disorder Using Functional ICA Classification

M. Vidal, A. M. Aguilera

Depression manifests in various forms, with typical symptoms including persistent sadness or emptiness, reduced energy levels, and a diminished ability to experience joy. Electroencephalography (EEG) offers a cost-effective and scalable method for diagnosing and predicting treatment response in major depressive disorder. However, diagnosis using such neurophysiological tools requires advanced pre-processing and dimensionality reduction techniques, as well as careful interpretation of the results. We introduce a functional independent component analysis based on smoothed estimators that allows for robust discrimination of cortical regions potentially involved in depressive disorder. We exemplify our methods and results using the MPI-Leipzig Mind-Brain-Body dataset.

Keywords: Depression disorder, Functional ICA, Kurtosis,

Functional data analysis II
June 12, 2025  11:30 AM
Sala VIP Jaume Morera i Galícia

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