  Depression is a common psychiatric disorder, which causes significant patient
distress. Bipolar disorder is characterized by mood fluctuations between
depression and mania. Unipolar and bipolar depression can be easily confused
because of similar symptom profiles, but their adequate treatment plans are
different. Therefore, a precise data-driven diagnosis is essential for
successful treatment. In order to aid diagnosis, research applied machine
learning to brain imaging data, in particular to electroencephalography (EEG),
with accuracies reaching 99.5% (unipolar vs. healthy) or 85% (bipolar vs.
healthy). However, these results arise from small training sets, without
validation on independent data, and thus have a high risk of inflated
accuracies due to data over-fitting. We propose to use a bigger corpus of
realistic clinical data for training and testing and improve classification
with microstates features, which can assess the function of large-scale brain
networks.
