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Updated: Feb 7, 2026
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Form factor meets function: anatomy-dependent electrode-skin coupling and signal content in consumer eyewear EEG
Andrea Costanzo Palmisciano1, Andrea Farabbi1, Matteo Rossi2
1Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.
Journal of Neural Engineering
|February 5, 2026
Summary
Wearable EEG integrated into eyeglasses can reliably detect brain activity, like alpha rhythms and P300 responses. Head shape influences electrode performance, highlighting the need for personalized design in consumer EEG devices.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Wearable Technology
Background:
- Consumer-level wearable EEG devices require robust performance across diverse users.
- Eyewear integration offers a discreet and practical platform for electroencephalography (EEG).
- Head morphology can significantly impact the efficacy of wearable sensor systems.
Purpose of the Study:
- To assess the influence of head anatomy on an eyewear-based EEG system.
- To characterize electrode-skin impedance for two dry-electrode types.
- To evaluate the system's ability to detect alpha-rhythm modulation and P300 potentials.
Main Methods:
- A prototype eyewear-EEG device with gold-plated retractile pin (GPR) and conductive elastomer (CoE) electrodes was developed.
- EEG signals were recorded using an OpenBCI Cyton board at 256 Hz.
- Participants performed eyes-open/eyes-closed tasks and an auditory oddball paradigm.
Main Results:
- GPR electrodes showed higher median impedance but narrower confidence intervals than CoE.
- Head breadth significantly impacted GPR impedance but not CoE impedance.
- Alpha-band power modulation and P300 responses were reliably detected, with GPR electrodes showing tighter latency.
Conclusions:
- Eyeglass-integrated EEG systems can effectively capture spontaneous and evoked neural activity.
- Inter-subject variability in head anatomy necessitates careful design considerations for wearable EEG.
- This technology shows promise for consumer-level brain-computer interfaces.
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