Default Mode Network Detection using EEG in Real-time

A machine-learning model using a Hidden Markov Model identified the brain's Default Mode Network (DMN) from electroencephalogram (EEG) data with 95% accuracy. The model also achieved a correlation of 0.617 between baseline and calculated DMN fractional occupancy. This suggests that real-time EEG analysis can effectively detect the DMN, offering a more scalable and economical alternative to functional magnetic resonance imaging for monitoring and treating mental health disorders such as depression.

Study at a glance

Abstract

Abstract Mental health disorders affect countless people worldwide and present a major challenge for mental health services, which are struggling with the demand on a global scale. Recent studies have indicated that activity of the brain’s Default Mode Network (DMN) could prove insightful in monitoring patient recovery from depression and has been used as a therapeutic target itself. An opportunity exists to replicate recent therapeutic protocols targeting DMN connectivity via functional magnetic resonance imaging using the more economically scalable modality of electroencephalogram (EEG). The aim of this work was to validate the accuracy of real-time DMN detection methods applied to EEG data, using a publicly available dataset. Using a Hidden Markov Model to identify a 12-state resting-state network, this work achieved an overall DMN detection accuracy of 95%. Furthermore, the model was able to achieve a correlation of 0.617 between the baseline and calculated DMN fractional occupancy. These results demonstrate the ability of real-time analysis to effectively identify the DMN through EEG data providing an avenue for further applications that monitor and treat mental health disorders.