Development and Evaluation of Dry-Contact Electroencephalography (EEG) Sensors - Green, Jardin (Auckland University of Technology, 2020)
The driving force behind ongoing advancements in dry-contact electroencephalography (EEG) sensor development, is the user-comfort, reduced set-up time and sustained period of use that can potentially be exhibited in wearable devices. However, competing with the signal quality of traditional wet-contact biopotential sensors can be a challenging task. Investigation into problem areas when practically replacing wet-contact sensors with dry-contact sensors indicates the unknown electrode-tissue impedance (ETI) is arguably the most difficult to deal with. While there are many modern techniques that aim to reduce the effects of the unknown ETI, none are regarded as extremely successful methods. Thus, researchers at the Institute of Biomedical Technologies (IBTec), located within Auckland University of Technology (AUT), have conceptualised a novel method to accurately acquire these EEG potentials by mathematically accounting for and removing the unknown ETI as a variable from the system. This thesis describes the development process in designing sensor electronics compatible with the novel EEG dry-contact method. It then continues by elaborating on the full analysis and evaluation process of the developed method. This is first achieved through computer-aided simulation and then followed by practical testing in a controlled laboratory set-up. The system was simulated and tested with a fixed ETI and then a varied ETI. During the varied ETI simulations and testing, a non-windowed and windowed signal processing algorithm was applied to the acquired output signals. Once processed, the systems signals were compared with the input signal both visually and numerically using the multitaper power spectral density (PSD) analysis. It was found that an exceptionally accurate representation of the known input EEG signal could be reproduced while using the novel system. Most importantly, this reproduced signal also remained highly accurate regardless of the electrode-tissue interface present in the system. With the results presented, it was concluded that continuation in the development and evaluation of the novel system may well offer a solution that provides an efficient and reliable dry-contact EEG acquisition system.
Keywords: Electroencephalography; EEG; Development; Sensors