Default Mode Network Detection using EEG in Real-time
Navin Cooray, Chetan Gohil, Brendan Harris et al. preprint
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.