Comparing conventional wet EEG vs dry‐sensor wireless EEG to probe sensory processing and neuronal function in the aging brain: Biomarkers (non‐neuroimaging) / novel biomarkers. (7th December 2020)

• Record Type: Journal Article
• Authors: Nolan, Hugh; Barbey, Florentine; Buick, Alison R; Dyer, John; Murphy, Brian
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.045666
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Neurodegenerative conditions such as AD, PD and MS can develop slowly, making early detection and on‐going characterisation a challenge. Current methods rely primarily on clinical opinion/self‐reports, or on burdensome and expensive clinic‐based methods. Visual and other sensory evoked potential tasks, particularly with steady‐state stimulation, have been used to probe neuronal function in healthy aging (Sridhar & Manian 2019), and neurodegeneration (Jacob et al 2002, Viallate et al 2010), in pre‐clinical and symptomatic stages (Shahmiri et al 2017) – but using conventional lab/clinic based wired wet EEG systems. In this proof‐of‐concept study, we evaluate wireless dry EEG as a less burdensome alternative. If feasible this could provide an easy‐to‐use, scalable and objective measure of neuronal function, for use in larger longitudinal studies of these conditions. Methods: We compare signals recorded from a wireless dry‐EEG headset (BrainWaveBank Ltd – Murphy 2018, 2019), and state‐of‐the‐art conventional wet‐EEG hardware (Biosemi ActiveTwo). Informed consent was obtained from 8 healthy adult males for this methodological study. Each attended 2 separate in‐lab sessions, one week apart, in which both dry and wet recordings were made. Static and flickering steady state (14Hz) visual stimulus conditions were presented. Grand average VEPs were calculated for each recording and stimulus condition. Signal stability was quantified using a Monte Carlo process to estimate 95% confidence intervals. From the steady state VEP, scalp topographies of spectral power were also computed. Results: After band‐pass and notch filtering, a grand average of a simple visual‐evoked potential (Figure 1) recorded at posterior recording sites with the wireless dry‐EEG headset has similar waveform morphology and noise levels to that from conventional wet‐EEG equipment – as does the steady state VEP (Figure 2). Topographies of spectral power at 14 and 28Hz (Figure 3) show a similar central/posterior scalp distribution. Conclusions: In controlled environments, a wireless dry‐sensor EEG headset can yield aggregate data of comparable quality to a state‐of‐the‐art wet‐EEG, making much larger scale studies of sensory processing and broader neuronal function possible for the first time.