Depression level prediction using EEG signal processing

Depression is one of the most common mental disorders that at its worst can lead to suicide. Diagnosing depression in the early curable stage is very important. It may also lead to various disorders like sleep disorders and alcoholism. Here in this project the Electroencephalogram Gram (EEG) signals are obtained from publicly available database are processed in MATLAB. This can be useful in classifying subjects with the disorders using classifier tools present in it. For this aim, the features are extracted from frequency bands (alpha, delta and theta).