F3-03-03: REPLICATING LAB ELECTROPHYSIOLOGY WITH OLDER USERS IN THE HOME, USING GAMIFIED DRY EEG

Neuroscience from the comfort of your home: Repeated, self-administered wireless dry EEG measures brain function with high fidelity

Recent advances have enabled the creation of wireless, “dry” electroencephalography (EEG) recording systems, and easy-to-use engaging tasks, that can be operated repeatedly by naïve users, unsupervised in the home. Here, we evaluated the validity of dry-EEG, cognitive task gamification, and unsupervised home-based recordings used in combination. Two separate cohorts of participants—older and younger adults—collected data at home over several weeks using a wireless dry EEG system interfaced with a tablet for task presentation. Older adults (n = 50; 25 females; mean age = 67.8 years) collected data over a 6-week period. Younger male adults (n = 30; mean age = 25.6 years) collected data over a 4-week period. All participants were asked to complete gamified versions of a visual Oddball task and Flanker task 5–7 days per week. Usability of the EEG system was evaluated via participant adherence, percentage of sessions successfully completed, and quantitative feedback using the System Usability Scale. In total, 1,449 EEG sessions from older adults (mean = 28.9; SD = 6.64) and 684 sessions from younger adults (mean = 22.87; SD = 1.92) were collected. Older adults successfully completed 93% of sessions requested and reported a mean usability score of 84.5. Younger adults successfully completed 96% of sessions and reported a mean usability score of 88.3. Characteristic event-related potential (ERP) components—the P300 and error-related negativity—were observed in the Oddball and Flanker tasks, respectively. Using a conservative threshold for inclusion of artifact-free data, 50% of trials were rejected per at-home session. Aggregation of ERPs across sessions (2–4, depending on task) resulted in grand average signal quality with similar Standard Measurement Error values to those of single-session wet EEG data collected by experts in a laboratory setting from a young adult sample. Our results indicate that easy-to-use task-driven EEG can enable large-scale investigations in cognitive neuroscience. In future, this approach may be useful in clinical applications such as screening and tracking of treatment response.

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.

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

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.