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Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface.

Alexander Craik1,2, Juan José González-España1,2, Ayman Alamir2,3,4

  • 1Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA.

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Summary

This study presents a new wireless, low-cost headset for electroencephalography (EEG) recordings, enabling closed-loop brain-computer interfaces (BCIs) and Internet of Things (IoT) applications with enhanced usability and portability.

Keywords:
brain–computer interfaceselectroencephalographymobile EEGmotor intent detectionneurodiagnosticsrehabilitation

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Existing electroencephalography (EEG) systems can be costly, cumbersome, and difficult to use, limiting their application in real-time brain-computer interfaces (BCIs) and Internet of Things (IoT) devices.
  • There is a need for a portable, user-friendly, and affordable EEG solution for both clinical and home-based neurorehabilitation and research.

Purpose of the Study:

  • To design and validate a wireless, low-cost, mobile, dry-electrode headset for EEG recordings.
  • To enable closed-loop brain-computer interface (BCI) and Internet of Things (IoT) applications.
  • To ensure the device is user-friendly, portable, reliable, and cost-effective.

Main Methods:

  • Designed a headset using commercial off-the-shelf (COTS) components, balancing key performance metrics.
  • Incorporated a patent-pending self-positioning dry electrode bracket for optimal scalp contact.
  • Integrated five EEG electrodes, three skin sensors for eye movement/blinks, and an inertial measurement unit (IMU) for head movement monitoring. Utilized a 24-bit, 500 Hz EEG amplifier with WiFi connectivity, real-time adaptive noise cancellation, and support vector machine (SVM) classifiers.

Main Results:

  • The adjustable headset accommodates 90% of the population.
  • The system demonstrated high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively).
  • Pilot testing validated the closed-loop BCI application for upper-limb rehabilitation, proving concept for clinical and home use.

Conclusions:

  • The developed wireless closed-loop BCI system offers a low-cost, usable, interoperable, portable, and reliable solution.
  • The headset is suitable for BCI and neurorehabilitation research.
  • The system's programmability and features make it a viable option for Internet of Things (IoT) applications.