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A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
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Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors.
Guilherme Correia1, Michael J Crosse2,3, Alejandro Lopez Valdes3,4,5,6
1Department of Physics, NOVA School of Science and Technology, 2829-516 Caparica, Portugal.
Sensors (Basel, Switzerland)
|February 24, 2024
Summary
A new toolkit validates EEG-enabled earbuds for brain activity monitoring. This system uses a phantom ear model and software to assess device performance, paving the way for consumer and health applications.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Ear-EEG earbuds offer a discrete approach to brain activity monitoring.
- Widespread adoption requires thorough characterization of this emerging technology.
- Current validation methods are often limited to laboratory settings.
Purpose of the Study:
- To develop and present a comprehensive validation toolkit for ear-EEG devices.
- To facilitate the systematic assessment of novel ear-EEG hardware and neural signal acquisition.
- To enable reliable performance evaluation for consumer and health applications.
Main Methods:
- Developed a desktop application (EaR-P Lab) using the Lab Streaming Layer (LSL) protocol for EEG validation paradigms.
- Adapted the phantom evaluation concept using 3D ear scans to simulate ear-EEG activity.
- Validated paradigms with wet-electrode ear-EEG and benchmarked against scalp-EEG.
- Utilized the ear-EEG phantom for hardware characterization and electrode configuration optimization.
Main Results:
- The ear-EEG phantom successfully acquired performance metrics, identifying differences based on electrode location.
- Optimized electrode reference configuration led to increased auditory steady-state response (ASSR) power.
- The toolkit demonstrated effectiveness in assessing ear-EEG device performance.
Conclusions:
- The developed toolkit provides a standardized method for evaluating ear-EEG devices.
- This facilitates the advancement of discrete, non-invasive brain-computer interfaces (BCIs).
- The findings support the potential for ear-EEG earbuds in consumer and clinical settings.
