EEG Signal Acquisition and Analysis for Detecting and Classifying Mental Disorders

¹ Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India 603 203
² Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India 603 203

^* Corresponding author: harisudk@srmist.edu.in

Abstract

Electroencephalography (EEG) is a critical tool in the diagnosis and monitoring of mental disorders, providing valuable insights into the brain’s electrical activity. However, traditional EEG electrodes face challenges in terms of signal quality, comfort, and stability, which can affect the accuracy of diagnosing conditions such as depression, schizophrenia, anxiety, and bipolar disorder. This project proposes a novel high-sensitivity, multi-layered electrode design tailored specifically for EEG applications. By combining advanced materials such as graphene-enhanced conductive polymers, silver nanowires, and integrated microfluidic channels on a flexible silicone substrate, this electrode offers improved signal-to-noise ratio (SNR), reduced contact impedance, and better comfort for long-term wear. The goal of this project is to enhance the accuracy of EEG signal acquisition for mental health diagnostics and to enable the differentiation of multiple mental disorders through machine learning-based signal analysis. The proposed electrode design significantly improves the quality of EEG signals, which are often subtle and prone to interference. By implementing microneedles for better skin contact and microfluidic channels for maintaining a stable interface, the electrode minimizes common artifacts and noise, enabling the capture of high-fidelity brainwave data. These features are essential for differentiating complex mental disorders, as even minor signal variations can be clinically relevant. This electrode design, with its enhanced sensitivity and stability, presents a significant advancement in EEG technology for mental health diagnostics. It not only improves the comfort and accuracy of EEG monitoring but also holds potential for personalized and early diagnosis of mental health conditions.