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

Abstract

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.

Keywords: brain–computer interfaces; electroencephalography; mobile EEG; motor intent detection; neurodiagnostics; rehabilitation.

Figure A1

The headset exploded view showing all components. This figure was adapted from US provisional patent #62857263.

Figure 1

Design criteria adopted in this research to maximize the translational impact of noninvasive (non-surgical) closed-loop BCI technology (adapted with permission from [23]).

Figure 2

(A) Fully assembled one-size-fit-all (patent pending) headset design. (B) Dry-electrode bracket. (C) The skin sensor holder. This figure was adapted from US provisional patent #62857263.

Figure 3

Block diagram of the EEG amplifier board.

Figure 4

A custom EEG-based BCI headset with wireless tablet-based (Fire 8, Amazon, Seattle, WA, USA) graphical user interface (GUI) and an IoT-enabled powered upper-limb exoskeleton robotic device (Rebless, H Robotics, Austin, TX, USA) deployed in a sample neurorehabilitation application.

Figure 5

Channel Impedance: Impedance values from the open-loop sessions for five participants. The values were taken before (blue) and after (orange) the session. The values are in k$\mathsf{\Omega }$.

Figure 6

(A). Eye blinks: A participant (S4) was instructed to blink three times during a session. The plot shows the signal detected by the vertical EOG sensor. (B). Eye Movements: The same participant was instructed to move her eyes left–to–right and right–to–left over a period of 15 s. The resulting plot shows the oscillating EEG due to these repetitive eye movements.

Figure 7

Characterization of EEG in three task conditions. (A). Eyes Closed (EC): A participant was instructed to maintain eyes closed for a period of 8 s during the session. (B). Eyes Open (EO): The participant maintained eyes open for a period of time. (C). Head Movement (HM): The participant was asked to move the head towards the front, back, left, and right for a period of time. The resulting plot demonstrates correct synchronization of EEG and IMU data based on the resulting movement artifacts in the EEG signal. (D). Spectral Comparison between EO, EC, and HM conditions.

Figure 8

Spectrogram and relative power: (A–D). Plots show the average spectrogram for participants S1–S4 from 0.5 s before movement onset (MO) to 2 s after MO. (E). shows average relative power in the $\delta$ and $\mu$ frequency bands among participants. The average is based on two blocks with twenty trials each.

Figure 9

(A): User-friendly interface that presents real-time impedance measurements. (B): Easy-to-use survey functionality for direct user feedback. (C): Debugging interface that can be used for troubleshooting of the system by the user, including a real-time metric for the communication rate between the system and the selected tablet.

Figure 10

Movement–related cortical potential, MRCP: Following the protocol proposed by [17], we obtained the MRCP for participant S005. For each channel, the MRCPs were obtained from averaging 20 trials. The spatial average of those averages is the plot labeled “Average”. Channel FC${}_3$ was excluded due to its high impedance value for this participant. The vertical broken line represents the movement onset (MO).

Figure 11

The closed-loop BCI–robot neurorehabilitation system in use at the home of the participant with chronic stroke.

Figure 12

Average MRCP amplitudes at start and end of therapy: The subplots present MRCPs across each of the five EEG electrodes recorded for participant S005 at start (block 1) and end (block 105) after six weeks of the at-home BCI therapy. Each MRCP is the result of averaging each of the 20 trials in each block. The vertical dotted line represents the moment movement intent (MI) was detected by the trained SVM machine learning model.