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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. 

Millions of concussions happen each year in the US alone. A proportionally large number of these concussions are due to high impact sports injury. Currently, there exists no solution to quickly monitor brain functions and test the oculomotor functions of individuals who have suffered a traumatic brain injury in order to diagnose them as having suffered a concussion. What is presently done to diagnose concussions is a CT scan or MRI, which are lengthy procedures to schedule, set up, and conduct; and furthermore, takes additional time to analyze the results in order to arrive at a diagnosis. This prolongation of the diagnosing process is inherently problematic since the longer time it takes between time of injury and time of diagnosis, there is greater risk of decisions and actions which can worsen damage to the brain. The sooner a concussion can be diagnosed, the sooner and better the treatment can be performed for recovery. In order to ameliorate this issue, we seek to develop a device to perform the function of diagnosis and monitoring of brain activity in a more rapid and timely manner. Literature review into the anatomy of vestibular and ocular brain functions was performed; as well as research into various testing and monitoring methodologies of these vestibular and ocular functions. One such method that has proven to be a reliable method for diagnosis is Vestibular Ocular Motor Screening (VOMS), which is a visual and balance test performed by a doctor with a patient. Further research was also done into existing technologies whose functionalities would allow the device in order to perform brain monitoring, visual testing, and ultimately diagnosis; namely EEG, VR, and infrared eye tracking. Currently, very few devices on the market take advantage of these technologies together for medical uses. A device incorporating these technologies together allows would allow for more consistent administering of visual tests and real-time monitoring of brain activity. With a functional prototype, user testing is to be performed in order to assess the function and viability of the device.