Measurement and Analysis of System Parameter Effects on Noise in EEG Systems
Meghna Roy Chowdhury, Shreyas Sen · 2025
Electroencephalography (EEG) is a widely used method for monitoring brain activity. Traditionally, wet electrodes have been the preferred choice due to their superior signal-to-noise ratio (SNR). However, as the demand for wearable and long-term EEG systems grows, researchers are increasingly exploring dry electrodes, which offer greater practicality despite their lower SNR. In this paper, we analyze how system parameters affect the noise characteristics that contribute to the lower SNR in dry EEG systems. Through experimental measurements and theoretical insights, we examine key factors influencing signal quality, particularly contact impedance and uncorrelated pickup. Specifically, we demonstrate that increasing pressure and electrode contact area significantly reduces noise levels, by approximately 50 dB and 30 dB, respectively, by lowering contact impedance. Furthermore, we observe that while dry electrodes maintain stable noise levels over time, wet electrodes experience a significant noise increase of approximately 38 dB after just two hours. Additionally, we highlight the presence of flicker noise in dry EEG systems, a phenomenon previously overlooked in this context. Our findings provide critical insights into the noise behavior of dry electrodes, paving the way for more reliable and practical wearable EEG systems. By addressing key challenges in signal quality, we contribute to advancing long-term neurological monitoring technologies for real-world applications