Feasibility of Repeated Assessment of Cognitive Function in Older Adults Using a Wireless, Mobile, Dry-EEG Headset and Tablet-Based Games

Esther C McWilliams, Florentine M Barbey, John F Dyer, Md Nurul Islam, Bernadette McGuinness, Brian Murphy, Hugh Nolan, Peter Passmore, Laura M Rueda-Delgado, Alison R Buick, Esther C McWilliams, Florentine M Barbey, John F Dyer, Md Nurul Islam, Bernadette McGuinness, Brian Murphy, Hugh Nolan, Peter Passmore, Laura M Rueda-Delgado, Alison R Buick

Abstract

Access to affordable, objective and scalable biomarkers of brain function is needed to transform the healthcare burden of neuropsychiatric and neurodegenerative disease. Electroencephalography (EEG) recordings, both resting and in combination with targeted cognitive tasks, have demonstrated utility in tracking disease state and therapy response in a range of conditions from schizophrenia to Alzheimer's disease. But conventional methods of recording this data involve burdensome clinic visits, and behavioural tasks that are not effective in frequent repeated use. This paper aims to evaluate the technical and human-factors feasibility of gathering large-scale EEG using novel technology in the home environment with healthy adult users. In a large field study, 89 healthy adults aged 40-79 years volunteered to use the system at home for 12 weeks, 5 times/week, for 30 min/session. A 16-channel, dry-sensor, portable wireless headset recorded EEG while users played gamified cognitive and passive tasks through a tablet application, including tests of decision making, executive function and memory. Data was uploaded to cloud servers and remotely monitored via web-based dashboards. Seventy-eight participants completed the study, and high levels of adherence were maintained throughout across all age groups, with mean compliance over the 12-week period of 82% (4.1 sessions per week). Reported ease of use was also high with mean System Usability Scale scores of 78.7. Behavioural response measures (reaction time and accuracy) and EEG components elicited by gamified stimuli (P300, ERN, Pe and changes in power spectral density) were extracted from the data collected in home, across a wide range of ages, including older adult participants. Findings replicated well-known patterns of age-related change and demonstrated the feasibility of using low-burden, large-scale, longitudinal EEG measurement in community-based cohorts. This technology enables clinically relevant data to be recorded outside the lab/clinic, from which metrics underlying cognitive ageing could be extracted, opening the door to potential new ways of developing digital cognitive biomarkers for disorders affecting the brain.

Keywords: EEG; EEG biomarker; cognition; gamification; mobile EEG.

Conflict of interest statement

LR-D, AB, EM, JD, HN, MI, FB, and BMu are employees of the company Cumulus Neuroscience Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 McWilliams, Barbey, Dyer, Islam, McGuinness, Murphy, Nolan, Passmore, Rueda-Delgado and Buick.

Figures

Figure 1
Figure 1
Flow of EEG and behavioural data.
Figure 2
Figure 2
Sixteen-channel wireless headset designed with pliable sensors and the sensor signal quality check.
Figure 3
Figure 3
Images of 2-stimulus visual oddball, flanker, n-back, and delayed match-to-sample gamified tasks.
Figure 4
Figure 4
Weekly adherence. Mean number of sessions per week across all participants and by age group. Error bars show upper and lower 95% confidence intervals.
Figure 5
Figure 5
Percentage of time each of 16 channels recorded non-saturated data, shown across age groups.
Figure 6
Figure 6
Behavioural responses to gamified cognitive tasks over 12 weeks across age group. Shading indicates 95% confidence interval. (A) median correct RTs to targets in 2-stimulus oddball task; (B) median correct RTs to congruent trials in flanker task; (C) percentage accuracy, all trials, n-back task; (D) percentage accuracy, all trials, delayed match-to-sample task.
Figure 7
Figure 7
Resting state task. (A) power spectral density (PSD) in decibels (dB) at O1 and O2 by age group in eyes-open and eyes-closed conditions, and the difference condition; (B) relative power at O1 and O2 by age group in eyes-open and eyes-closed conditions with logarithmic scaling for display only.
Figure 8
Figure 8
P300. (A) single-session median; (B) single-participant mean; (C) grand mean; (D) grand mean topographies selected at ERP peak timepoints; (E) examples of single-session median ERPs successfully recorded from game plays from 6 different participants (2 participants per age group).
Figure 9
Figure 9
ERN. (A) single-session median; (B) single-participant mean; (C) grand mean; (D) grand mean topographies selected at ERP peak timepoints.
Figure 10
Figure 10
Showing age-related differences in event-related components recorded using the platform. (A) P300; (B) ERN.

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