A Deep Convolution Neural Network Based Model for Identification Depression Using EEG

An upsurge in suicide instances throughout the world is frequently caused by depression. Therefore, a precise evaluation and counselling are essential to alleviate the consequences of depression. The electrical activity of the brain is monitored and documented using an electroencephalogram (EEG). It is capable of producing a reliable evaluation of the degree of depression. Prior investigation established the viability of using deep learning (DL) models with EEG data to diagnose mental disorder. The patient's behaviour exhibits the signs of depression. As a result, doctors employ questionnaires and talking sessions as screening methods to determine the severity of depression. DeprNet is a DL-based Convolutional Neural Network that this research suggests be used to categorise the EEG data of healthy and depressed patients. The suggested system uses a convolutional neural network as a deep learning technique. The system was created using an EEG dataset for depression, and it analyses if a subject is positive, negative, or neutral. The confusion matrix, accuracy, precision, recall, and f1-score are among the experimental findings.