EEG Data Quality in Large-Scale Field Studies in India and Tanzania

Area of Science:

• Neuroscience
• Environmental Health
• Public Health

Background:

• Understanding the neurophysiological effects of diverse environments requires large-scale data collection.
• Field-based electroencephalography (EEG) offers portability and cost-effectiveness but faces challenges in data quality and researcher efficiency.
• Existing methods struggle with data quality and researcher idle time in distributed field research.

Purpose of the Study:

• To demonstrate a scalable and cost-effective method for collecting high-quality field-based EEG data.
• To validate the reliability of non-specialist data collection in diverse global settings.
• To enable large-scale neurophysiological research, particularly in low- and middle-income countries.

Main Methods:

• Implemented structured training, dedicated teams, and daily automated data quality analysis and feedback.
• Conducted data collection over 30 weeks in India and Tanzania with non-specialist research teams.
• Collected EEG and survey data from 7,933 diverse participants, including hunter-gatherers and office workers.

Main Results:

• Achieved an average of 25.6 participants per week per team, maintaining high throughput.
• EEG data quality, assessed using PREP and FASTER methods, was comparable to controlled lab conditions.
• Reduced cost per participant to under $50, significantly lower than typical data collection expenses.

Conclusions:

• Non-specialist teams can reliably collect high-quality EEG data in diverse field settings with appropriate training and automated feedback.
• The demonstrated methodology significantly lowers the cost and improves the efficiency of large-scale neurophysiological data collection.
• This approach facilitates expansive research programs, especially in resource-limited regions, to study environmental impacts on neurophysiology.