CLASSIFYING ELECTROENCEPHALOGRAM (EEG) SIGNALS FOR ACCURATE MENTAL DISORDER ANALYSIS

Electroencephalogram (EEG) is a powerful tool for analysis of brain signals. The variation in pattern of EEG signals are useful in computing the mental disorders. In this presented work, the main aim is to study and analyze the techniques available for EEG signal classification. Additionally, contribute a deep learning based technique for accurately predict the mental health. This document discusses the over need of the proposed work, the problem domain and the proposed solution strategy. Additionally, the expected outcome and required tools and techniques have also been discussed.