Using Muse: Rapid Mobile Assessment of Brain Performance.
Olave E Krigolson1, Mathew R Hammerstrom1, Wande Abimbola1
1Centre for Biomedical Research, University of Victoria, Victoria, BC, Canada.
Frontiers in Neuroscience
|February 15, 2021
Summary
Mobile electroencephalography (mEEG) effectively measures cognitive fatigue by analyzing brain potentials and oscillations. This technology offers a faster, scalable approach to understanding neural states linked to errors and accidents.
Area of Science:
- Neuroscience
- Cognitive Science
- Biomedical Engineering
Background:
- Cognitive fatigue, a state linked to increased errors, poses significant safety risks.
- Mobile electroencephalography (mEEG) offers potential for large-scale neural data collection.
- Understanding the neural signatures of cognitive fatigue is crucial for safety and performance.
Purpose of the Study:
- To investigate the neural correlates of cognitive fatigue using mEEG.
- To examine the relationship between perceived fatigue, event-related potentials (ERPs), and EEG oscillations.
- To validate mEEG as a tool for accurate ERP and EEG data acquisition.
Main Methods:
- A cohort of 1,000 participants underwent mEEG recording during a visual oddball task on an iPad.
- EEG data were collected using a Muse EEG headband, with rapid setup and data collection (under 7 minutes).
- Analysis focused on N200 and P300 ERP components and delta, theta, alpha, and beta band oscillations.
Main Results:
- Robust N200 and P300 ERP components and neural oscillations were identified.
- Correlations were observed between ERP components, EEG power, and perceived cognitive fatigue.
- A combination of ERP and EEG features predicted cognitive fatigue better than individual features.
Conclusions:
- mEEG is validated as a viable and efficient tool for neuroscience research.
- The study provides insights into the neural impact of cognitive fatigue.
- Findings support the use of mEEG for large-scale studies on cognitive states and performance.
