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Objective: We designed and validated 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 inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal–to–noise ratio (SNR) and common–mode rejection ratio (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 a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (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 and provides proof-of-concept validation for the device’s use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.
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Design Principles for Mobile Brain-Body Imaging Devices with Optimized Ergonomics
Mobile brain-body imaging (MoBI) technology allows the study of the brain in action and the context of complex natural settings. MoBI devices are wearable devices that typically record the scalp electroencephalogram (EEG) and head motion of the user. MoBI systems have applications in neuroscience, rehabilitation, design, and other applications. Here, we propose design principles for MoBI systems for use in brain-machine interfaces for rehabilitation by individuals with movement disabilities. This design study discusses the validity of the process of utilizing 3D anthropometric data as a basis to design a MoBI headset for an optimized fit and ergonomics. The study also discusses the need for ensuring that EEG sensors keep constant contact with the scalp and face for the best scan quality. Moreover, the need for singlehanded correct positioning of the headset is discussed to address disabilities in the older populations and clinical populations with motor impairments.
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- PAR ID:
- 10279568
- Editor(s):
- Ahram, Tareq Z; Falcão, Christianne S.
- Date Published:
- Journal Name:
- Advances in Usability, User Experience, Wearable and Assistive Technology
- Volume:
- 275
- ISSN:
- 2367-3370
- Page Range / eLocation ID:
- 3-10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-80091-8_1
