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Updated: Jun 26, 2026
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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
Published on: July 26, 2013
Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes.
R Matthews1, P J Turner, N J McDonald
1QUASAR, San Diego, CA 92121 USA. robm@quasarusa.com
Summary
This study presents a compact, wireless electroencephalogram (EEG) system using novel noninvasive sensors that record brain activity through hair. The system enables real-time workload classification during ambulation without skin preparation.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Wearable Technology
Background:
- Traditional electroencephalogram (EEG) systems often require skin preparation and conductive gels, limiting their use in ambulatory settings.
- Existing ambulatory EEG systems can be bulky, power-hungry, and susceptible to motion artifacts.
- Noninvasive sensing technologies are needed to overcome the limitations of conventional EEG for real-world applications.
Purpose of the Study:
- To describe a compact, lightweight, and ultra-low power ambulatory wireless EEG system.
- To evaluate the performance of innovative noninvasive bioelectric sensors for EEG acquisition during ambulation.
- To demonstrate real-time workload classification using EEG data recorded during subject motion.
Main Methods:
- Development of a compact, wireless EEG system incorporating QUASAR's noninvasive bioelectric sensors.
- Utilizing sensors that operate through hair, eliminating the need for skin preparation or conductive gels.
- Implementing mechanical isolation within a harness to mitigate motion artifacts during ambulation.
- Employing advanced algorithms for real-time classification of workload based on ambulatory EEG data.
Main Results:
- The system successfully recorded high-quality EEG data during ambulation.
- The noninvasive sensors operated effectively through hair without conductive gels.
- Real-time classification of subject workload was achieved during motion.
- Demonstrated the feasibility of using the developed EEG system for ambulatory workload assessment.
Conclusions:
- The described ambulatory wireless EEG system offers a compact, lightweight, and low-power solution for brain activity monitoring.
- The innovative noninvasive sensors and mechanical isolation enable high-quality EEG recording during ambulation.
- The system's capability for real-time workload classification during motion has significant implications for various applications.
