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Review of Machine Technique for Prediction of Depression Using EEG Signal

Pratibha Pandole, Pankaj Dubey


Depression is a prevalent mental health disorder that affects millions worldwide, often leading to significant personal and societal burdens. Traditional diagnostic methods for depression rely heavily on subjective assessments, which can be prone to bias and inconsistency. In recent years, there has been a growing interest in utilizing machine learning techniques to predict depression based on
electroencephalogram (EEG) signals, offering a promising avenue for more objective and reliable diagnosis. This review explore various machine learning methods employed for the prediction of depression using EEG signals, highlighting their strengths, limitations, and potential applications. Through a comprehensive analysis of existing literature, this review aims to provide insights into the current state-of-the-art techniques, challenges, and future directions in utilizing EEG-based machine
learning approaches for depression prediction.


Machine learning, EEG, Emotion, Stress, E-healthcare

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