About this document
EEG-Based Depression Detection Methods by haripriyasaraf03 is a document available to read on EtoBox.
This research investigates the detection of depression through electroencephalographic (EEG) signals using various machine learning classifiers. The study compares the accuracy of different classifiers trained on both linear and nonlinear EEG features, achieving classification accuracies between 80% and 95%. The findings suggest that EEG features can effectively classify ongoing and long-lasting effects of depression.
- Author
- haripriyasaraf03
- Language
- EN