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Development of a Modular Board for EEG Signal Acquisition
Tomas Uktveris1, Vacius Jusas2
1Department of Software Engineering, Kaunas University of Technology, Studentu St. 50, LT-51368 Kaunas, Lithuania. tomas.uktveris@ktu.lt.
Sensors (Basel, Switzerland)
|July 5, 2018
Summary
This study introduces a compact, modular electroencephalogram (EEG) acquisition board for brain-computer interfaces (BCIs). The low-cost, scalable device supports 16-64 channels and wireless data transfer, enabling wider BCI applications.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Wearable Technology
Background:
- Rising popularity of brain-computer interfaces (BCIs) necessitates affordable, miniaturized electroencephalogram (EEG) acquisition systems.
- Current limitations in scientific analysis, modularity, and validation hinder the development and availability of low-cost EEG devices.
Purpose of the Study:
- To design and evaluate a compact, modular, battery-powered EEG acquisition board for BCI applications.
- To address the EEG scaling problem through a vertically stackable hardware design.
- To provide a unified validation approach for low-cost EEG devices.
Main Methods:
- Development of a modular EEG acquisition board utilizing the ADS1298 analog front-end chip.
- Implementation of a vertically stackable design for scalable channel configurations (16-64 channels).
- Integration of Bluetooth and Wi-Fi for wireless raw EEG signal transfer at 250-1000 Hz sample rates.
Main Results:
- The proposed board offers a scalable solution for EEG acquisition, supporting a wide range of channels and sample rates.
- Wireless data transfer capabilities were successfully implemented via Bluetooth and Wi-Fi.
- System evaluation confirmed validity against datasheet specifications and real-world applications, achieving 6.59 µVpp input-referred noise and -97 dB CMRR (0-70 Hz).
Conclusions:
- The developed compact and modular EEG acquisition board meets the demand for low-cost, high-performance BCI hardware.
- The proposed design and validation methods offer a scalable and cost-effective solution for precision cEEG research.
- This work facilitates progress in BCI technology by improving device availability and enabling further scientific analysis.
Keywords:
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