A Portable Real-time Multi-channel Frontal EEG Acquisition System Integrated with an Ultra-thin Biocompliant Anti-oxidation Electrode Patch
Electroencephalogram (EEG) is the most commonly used tool for exploring dynamic brain activity. However, the commonly-used electrodes for EEG measurement are mostly rigid and require coating of conductive gels that is not user-friendly and makes the devices difficult for long-lasting recording. Furthermore, the typical EEG acquisition system is large in size and not economic, limiting the convenience in personalized application. Herein, a portable multi-channel EEG recorder with new designs of both electrodes and signal acquisition system is proposed. An ultra-thin stretchable electrode array patch, called USAE, composed of synthesized dopamine-modified silver-platinum nanowires (Dopa-Ag@Pt NWs) was created with strain-tolerated high conductivity, anti-oxidation ability, and adhesive ability, thus providing good reliability and high signal quality during long-term use. A dopamine-inspired transfer printing process permitted the integration of NWs into an ultrathin silicone substrate with thickness less than 100 μm. After connected with a miniaturized module composed of multi-channel pre-amplifier and a low-power bio-signal microcontroller, the system successfully conducts real-time 8-channel EEG measurements for monitoring of dynamic brain activity. Compared to conventional EEG measurement systems, this proposed system can not only provide stable and high-quality signals but can also significantly reduce the overall operation cost and power consumption, offering a promising platform in the application of brain-machine interfaces.
Authors
- Po-Hsueh Chang
- Herming Chiueh (ORCID: https://orcid.org/0000-0002-4828-3316)
- Wei‐Chen Huang (ORCID: https://orcid.org/0000-0002-2772-0468)
- Ru-Zheng Lin
- Weihan Huang
- Jianyu Chen (ORCID: https://orcid.org/0000-0001-7211-7927)
- Chun-Chang Lin
Institutions
- ITRI International (US)
- National Yang Ming Chiao Tung University (TW)
- Industrial Technology Research Institute (TW)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/jsen.2025.3607407
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00