Authors - Yash Dargude, Jui Ambekar, Yash Gadakh, S.T Gandhe Abstract - Mental health disorders, such as depression, anxiety, and stress, are global challenges that significantly affect individuals’ well-being and productivity. Early detection and diagnosis are crucial for effective intervention, yet traditional methods often rely on subjective assessments, leading to potential delays. Electroencephalography (EEG) has emerged as a promising non-invasive tool for objectively monitoring brain activity, offering valuable insights into mental health conditions. This survey paper explores the current state-of-the-art in mental health detection using EEG signals. We provide an overview of EEG-based systems, highlighting key signal processing techniques such as filtering, artifact removal, and noise reduction. Feature extraction methods, including time-domain, frequency-domain, and time-frequency domain techniques, are reviewed to emphasize how patterns in brainwave activity correlate with mental health states. Additionally, we examine various machine learning and deep learning algorithms, such as Support Vector Machines (SVM), Random Forest, and Convolutional Neural Networks (CNNs), which have been applied to classify mental health conditions based on EEG data. The paper also presents a comprehensive analysis of the effectiveness of these models in detecting specific mental health conditions like depression, anxiety, and stress. We discuss the challenges faced in using EEG for mental health detection, such as signal variability and the need for large datasets, and propose future directions for enhancing the accuracy and generalizability of these models. This survey aims to contribute to the development of more reliable, EEG-based diagnostic tools for mental health assessment.
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