A Novel Approach to Early Depression Prediction and Estimation with EEG Signals

Depression is a widespread issue in today's society. WHO considers depression to be the leading cause of global disability, and it endangers nearly every aspect of human life, particularly public and private health. Analyzing EEG signals are useful in depression prediction. It reflects the functioning of the human brain and is regarded as the most appropriate tool for diagnosing depression. To improve design portability, effective diagnostics, and advanced technology we use Deep learning algorithms to recognize patterns and extract features from the raw data supplied to them. The Predictor model is based on advanced machine learning algorithms based on supervised learning techniques. Due to the simplicity in the use of the proposed model, this technology provides mental health to professionals with visible tools for detecting the symptoms of depression, enabling faster prevention.