Design Principles for Mobile Brain-Body Imaging Devices with Optimized Ergonomics

Gorman, Niell; Louw, Antoinette; Craik, Alexander; González, José A.; Feng, Jeff; Contreras-Vidal, José L.

doi:10.1007/978-3-030-80091-8_1

Cited by 2 publications

(1 citation statement)

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“…This is particularly important when using dry electrodes that cannot benefit from the viscous gel typically employed in wet-electrode systems. For the dry electrodes that are placed over the user's hair, it is common to experience unstable and noisy signals due to poor or intermittent contact between the electrode and the head scalp [55,56]. To meet this challenge, a unique self-positioning dry-hair electrode holder was developed, as shown in Figure 2B.…”

Section: Eeg Electrode-holder Designmentioning

confidence: 99%

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

Craik,

González-España,

Alamir

et al. 2023

Sensors

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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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Section: Eeg Electrode-holder Designmentioning

confidence: 99%