Quantifying cognitive aging and performance with at-home gamified mobile EEG
Abstract
Cognitive and clinical neuroscience often rely on small datasets, gathered from restricted populations in lab settings only. Dry-sensor EEG is a technology that can be made affordable and easy to use. When combined with... [ view full abstract ]
Cognitive and clinical neuroscience often rely on small datasets, gathered from restricted populations in lab settings only. Dry-sensor EEG is a technology that can be made affordable and easy to use. When combined with gamification of experimental tasks, this could provide a new way to gather data on a large scale. Signal quality is a challenge, but the ease of gathering repeated samples can compensate for this. In this paper we present a new system that uses a novel wearable EEG headset, a tablet-based suite of cognitive games, and a cloud-based data analysis system. A large 12-week at-home feasibility study is described, yielding high adherence and user acceptability, even among older users. Data from that trial replicates well-established EEG and behavioural patterns from the cognitive neuroscience literature. Some initial machine learning analyses show that user age and cognitive performance level can be discriminated from single daily sessions, with much higher classification performance when aggregating over multiple days.
Authors
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Brian Murphy
(Queen's University Belfast, BrainWaveBank Ltd Belfast)
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Andrea Aleni
(BrainWaveBank Ltd)
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Brahim Belaoucha
(BrainWaveBank Ltd)
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John Dyer
(BrainWaveBank Ltd)
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Hugh Nolan
(BrainWaveBank Ltd)
Topic Area
Digital Signal Processing
Session
Poster » Poster Session (14:50 - Thursday, 21st June, Ashby Building Foyer)
Presentation Files
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