Design Principles and Dynamic Front End Reconfiguration for Low Noise EEG Acquisition With Finger Based Dry Electrodes

Abstract

Dry electrodes are a convenient alternative to wet electrodes for electroencephalography (EEG) acquisition systems. Dry electrodes are subject to a higher amount of noise at the electrode scalp interface and these effects can be worsened due to non-ideal design or improper placement on the head. In this work, we investigate a popular dry electrode design based on a number of resistive `finger' shaped contacts. We conduct experiments comparing designs with varying numbers of fingers using two impedance measurement methods and show that sparser arrangements of fingers are more robust to varying use cases and are more effective at penetrating through hair on the scalp. We then show that these impedance measurement metrics could be used to sort individual fingers within one electrode according to quality of electrical contact. We show that the signals from individual fingers can differ from each other significantly due to differing local effects of impedance and noise, and demonstrate through experimental results that dynamically selecting only a subset of fingers with good contact impedance can improve the overall signal-to-noise ratio of the EEG signal from that electrode.