Machine Learning-Based Alexithymia Assessment Using Resting-State Default Mode Network Functional Connectivity
Sensors September 12, 2025 Kei Suzuki, Midori Sugaya
A machine-learning model using resting-state electroencephalogram (EEG) signals can assess alexithymia severity with moderate accuracy. Functional connectivity in brain regions related to the default mode network, estimated via source localization, served as explanatory variables. The model classified individuals as having low or high alexithymia severity, achieving a maximum ROC-AUC score of 0.70. Analysis using explainable artificial intelligence revealed that functional connectivity in the theta and gamma frequency bands, particularly in the Left Hippocampus, was most effective for assessment. The findings suggest EEG and machine learning offer a potential objective method for evaluating alexithymia, a risk factor for several mental disorders.