Craik, A.; González-España, J.J.; Alamir, A.; Edquilang, D.; Wong, S.; Sánchez Rodríguez, L.; Feng, J.; Francisco, G.E.; Contreras-Vidal, J.L. Design and Validation of a Low-Cost Mobile EEG-Based Brain–Computer Interface. Sensors2023, 23, 5930.
Craik, A.; González-España, J.J.; Alamir, A.; Edquilang, D.; Wong, S.; Sánchez Rodríguez, L.; Feng, J.; Francisco, G.E.; Contreras-Vidal, J.L. Design and Validation of a Low-Cost Mobile EEG-Based Brain–Computer Interface. Sensors 2023, 23, 5930.
Craik, A.; González-España, J.J.; Alamir, A.; Edquilang, D.; Wong, S.; Sánchez Rodríguez, L.; Feng, J.; Francisco, G.E.; Contreras-Vidal, J.L. Design and Validation of a Low-Cost Mobile EEG-Based Brain–Computer Interface. Sensors2023, 23, 5930.
Craik, A.; González-España, J.J.; Alamir, A.; Edquilang, D.; Wong, S.; Sánchez Rodríguez, L.; Feng, J.; Francisco, G.E.; Contreras-Vidal, J.L. Design and Validation of a Low-Cost Mobile EEG-Based Brain–Computer Interface. Sensors 2023, 23, 5930.
Abstract
Objective: Design and validate a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications.
Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation.
Main Results: The adjustable headset was designed to accommodate 90\% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An accelerometer provides monitoring of head movements. The EEG amplifier operates with 24 bits resolution up to 500 Hz sampling frequency, and can communicate with other devices using 802.11 {b/g/n/WiFi}. It has high SNR and CMRR (121 dB and 110 dB, respectively), and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as backend coding language and JS, CSS, HTML as front-end coding languages, and includes training and optimization of Support Vector Machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation provides proof-of-concept validation for device use at both the clinic and at home.
Significance: The low-cost, accessibility, usability, interoperability, and programmability of the proposed closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.
Keywords
Brain Computer Interfaces; Electroencephalography; Mobile EEG; Rehabilitation; Neurodiagnostics; Motor Intent Detection
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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