maryjis/eeg_depression

machine learning models for predicting depression based on EEG data

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Experimental

This project offers machine learning tools to help clinical researchers and mental health professionals identify Major Depression Disorder (MDD) patients using Electroencephalography (EEG) data. It takes raw or pre-processed EEG brain activity readings as input and provides a classification that indicates whether an individual is likely to have MDD or is a healthy control. This can aid in research for diagnostic support or understanding neural markers of depression.

No commits in the last 6 months.

Use this if you are a neuroscience researcher or clinician working with EEG data and want to apply machine learning to differentiate between depressed patients and healthy individuals.

Not ideal if you are looking for a tool for real-time clinical diagnosis without expert interpretation, or if you do not have access to EEG data.

clinical-neuroscience psychiatry-research EEG-analysis depression-diagnosis biomedical-signal-processing

No License Stale 6m No Package No Dependents

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