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.

Full text

Citation: 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.
https://doi.org/10.3390/s23135930
Academic Editors: Mario Ortiz
García, José M. Azorín and Eduardo
Iáñez Martínez
Received: 9 May 2023
Revised: 9 June 2023
Accepted: 20 June 2023
Published: 26 June 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Article
Design and Validation of a Low-Cost Mobile EEG-Based
Brain–Computer Interface
Alexander Craik 1,2,* , Juan José González-España 1,2 , Ayman Alamir 2,3,4 , David Edquilang 5, Sarah Wong 2,5 ,
Lianne Sánchez Rodríguez 1,2 , Jeff Feng 2,5 , Gerard E. Francisco 6,7 and Jose L. Contreras-Vidal 1,2,*
1Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA
2Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry–University Cooperative Research
Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center,
University of Houston, Houston, TX 77004, USA
3Department of Biomedical Engineering, University of Houston, Houston, TX 77004, USA
4Department of Electrical Engineering, Jazan University, Jazan 45142, Saudi Arabia
5Department of Industrial Design, University of Houston, Houston, TX 77004, USA
6Department of Physical Medicine & Rehabilitation, University of Texas Health McGovern Medical School,
Houston, TX 77030, USA
7The Institute for Rehabilitation and Research (TIRR) Memorial Hermann Hospital, Houston, TX 77030, USA
*Correspondence: arcraik@uh.edu (A.C.); jlcontreras-vidal@uh.edu (J.L.C.-V.)
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; rehabilitation;
neurodiagnostics; motor intent detection
1. Introduction
Since the early 1960s, when electroencephalography (EEG) data were first digitized
and processed with a computer to today, much progress has been made in harnessing the
potential of brain–computer interface (BCI) applications [
1
,
2
]. While EEG measurements
Sensors 2023,23, 5930. https://doi.org/10.3390/s23135930 https://www.mdpi.com/journal/sensors

Sensors 2023,23, 5930 2 of 26
are affected by many factors, including physiological and non-physiological artifacts [
3
]
resulting in low signal-to-noise ratios [
4
], recent advancements in de-noising (
e.g., [5,6])
and
deep learning [
7
,
8
] techniques have driven the emergence of viable clinical and non-clinical
BCI applications based on scalp EEG [
1
,
2
,
9
]. These applications include, but are not
limited to, seizure state prediction [
10
,
11
], sleep stage analysis [
12
], cognitive workload
assessment [
13
], motor-imagery-based brain–computer interface (BCI) systems [
14
,
15
],
neurorehabilitation [
16
,
17
], multi-modal and multi-brain–computer interfaces [
18
], brain-
controlled vehicles [
19
], EEG-based home control [
20
], virtual reality [
21
], and interactive
virtual environments [
22
]. While the future of these proof-of-concept BCI-enabled applications
is promising, there are a number of technical challenges that remain before the widespread
translation and adoption of these systems is realized.
Prior efforts from the scientific, engineering, medical, regulatory, industrial, and
patient-advocate communities [
23
–
26
] have addressed the challenges and opportunities
for accelerating the translation of closed-loop BCI systems for medical applications. Some
of the key challenges identified in deploying these technologies to end-users include
usability, interoperability, accessibility, and mobility, as well as the lack of standards
(device, performance, clinical, and end-user metrics). For example, current commercial
EEG amplifiers and BCI headsets are prohibitively expensive, lack interoperability, or
fail to provide a high signal quality or closed-loop operation, which are vital for BCI
applications [
23
]. To address these challenges and facilitate the translation of BCI systems,
we adopted criteria derived from the above stakeholder meetings for the design of
closed-loop BCI systems (Figure 1). Next, we briefly review these criteria. The reader
is referred to the source publications from these stakeholder meetings for additional details.
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]).
Portability [
27
] and interoperability both affect the type of BCI applications that can be
considered. Most commercial EEG systems are tethered to immobile processing hardware,
making them difficult to deploy outside of the clinic or laboratory. A portable and wireless
EEG system is highly preferred so it can be used outside lab and clinical settings in clinical
and non-clinical mobile applications at home, work, or play. Additionally, a system design
that only provides control of a single device or the analysis of a single protocol significantly
limits the potential for BCI systems, so a generalized control or analysis framework is
preferred over a device-, task- or protocol-specific system to maximize interoperability in
the widest sense.
Usability [
28
,
29
], form factor [
30
], and reliability [
31
] all significantly affect the user’s
experience. The current commercial EEG systems are generally difficult to set up and use,

Sensors 2023,23, 5930 3 of 26
particularly in medical applications by users with disabilities. This is a critical challenge for
applications that will be used by the public as a complex system setup may be too difficult
or take too long for an untrained user to operate without technical or expert assistance. A
difficult challenge in the design of an EEG headset is accommodating the many different
head sizes and shapes, hair types and styling, and user preferences, but designing many
different variations may not be economically feasible nor desirable for a commercial system.
While a one-size-fits-all design is preferable, the ability for the system to be adaptable
must be emphasized early in the design process and heavily tested in ecological settings.
Moving this technology to low-cost hardware will increase accessibility, but, if the system
is not reliable, the resulting user frustration may lead to product abandonment. Therefore,
extensive software and hardware bench testing must be performed to ensure reliability.
Outside of factors that affect the design considerations and the user’s experience, the
ability for the system to process EEGs quickly and effectively is a necessary condition for
complex closed-loop BCI applications. This necessity is due to the fact that EEG suffers
from a low signal-to-noise ratio, low spatial resolution, and high prevalence of artifacts,
such as eye movements, eye blinks, and motion artifacts [
32
], to name a few. Many of
the commonly used signal de-noising methods are not suitable for real-time or mobile
applications [
5
,
6
], so the selection of on-chip real-time signal-de-noising methods is a
crucial decision that should be considered early on in the development process. Once the
EEG signals are de-noised, a neural decoder or neural classifier is commonly employed to
extract valuable information, e.g., motor intent, emotional state, or other classes of internal
states, from the brain signals acquired with EEG [
7
,
33
]. However, most current EEG systems
do not provide the decoding functionality necessary for implementing closed-loop BCI
applications without additional hardware and software. The above challenges provided
the motivation for the development of the proposed EEG-based closed-loop BCI headset.
While there are low-cost commercial dry EEG amplifier systems available on the
market, none meet the criteria outlined above in Figure 1. For example, the Ultracortex
Mark IV EEG headset from OpenBCI [
34
] is a popular open-source EEG headset design
and is sold for a relatively low cost ($399.99 for the user to 3D print the headset, $899.99 for
the 3D-printed and assembled version at the time of publication). However, each headset
electrode holder must be manually manipulated for each user, which is not as user-friendly
as a design that employs a single manipulator for headset adjustments. Additionally, the
OpenBCI headset does not provide processing onboard with the amplifier. Instead, it
requires a separate computational unit for signal processing. The Muse 2 system [
35
] is
one of the lowest-cost commercial amplifiers available ($249.99) and includes a software
application that provides standard biofeedback. A major drawback with the Muse 2
system is that an annual subscription must be purchased to use many of the available
software features. Additionally, the Muse 2 system only has two forehead sensors and
two sensors located behind each ear, which limits the potential applications for systems
based on this system. Like the OpenBCI Mark IV headset, the Muse 2 system does not have
onboard processing capabilities, meaning a separate computing unit must be employed. In
another example BCI system [
36
], the researchers designed specialized dry EEG electrodes
for a low-channel-count EEG system for steady-state visual evoked potential (SSVEP)
applications. The main focus was to validate the dry-electrode design, so the authors
used a relatively expensive commercial amplifier (NeuroScan Synamps, CompuMedics
Neuroscan, Victoria, Australia). In another study [
37
], a low-cost system integrating
EEG and augmented reality (AR) capabilities was deployed for SSVEP-based applications.
Instead of creating a custom amplifier, the authors opted for a low-cost two-channel EEG
system for signal acquisition (EEG-SMT, Olimex, Plovdiv, Bulgaria). In [
38
], the authors
developed an inexpensive BCI system for upper-limb stroke rehabilitation. This system
relied on a higher-cost Emotiv (Emotiv Epoc+, Emotiv, San Francisco, CA, USA [
39
])
commercial amplifier and utilized open-source functionality from BCI2000 [
40
], without
a dedicated user-friendly interface. While the market for commercial EEG amplifiers is
expanding, there are no suitable commercial systems that meet the specifications required

Sensors 2023,23, 5930 4 of 26
for more closed-loop BCI applications. For a recent review of portable EEG devices with
wireless capability, see [41].
The rest of the paper is organized as follows: Section 2will describe the methods,
including hardware and software selection and development, as well as the methodology
for system validation using bench testing and human-subject experiments in the laboratory,
clinic, and home. Section 3presents the results of the system validation tests, including
first-in-human validation in an ecological setting. Section 4provides a discussion on crucial
design decisions and the development of the system generally. We conclude with some
lessons learned and next steps.
2. Methods
The design criteria were based on the recommendations from stakeholder
meetings [
23
–
26
,
42
]. The design choices based on the design factors shown in Figure 1will
be discussed in detail through the following sections.
To define the product and the engineering specifications for the system, we parcelled
these target specifications into four key areas: the headset specifications for a universally fitting
design, the desired characteristics for the EEG amplifier and sensors for artifact detection,
and the specifications for the brain–computer interface itself. These specific engineering
requirements are detailed in Table 1. The following section will detail the user-centered design
of the headset, the development of the software, and the approach followed for bench testing
and experimental validation with human participants for the system.
Table 1. Engineering specifications for the proposed closed-loop BCI device.
Headset Specifications
Circumference Adjustment Range (cm) 52.3–61.2
Head Breadth Adjustment Range (cm) 13.8–16.6
Head Length Adjustment Range (cm) 17.3–21.4
Electroencephalography (EEG) Electrode
Locations
Frontocentral (FC) 3, FC1, FCz, FC2, FC4
EEG Electrode Type Dry Comb Electrodes
Electrooculography (EOG) Electrode
Locations
Both Temples, Above Left Eye
Reference Electrode Locations Mastoids
EOG and Reference Electrode Type Dry Flat Electrodes
Amplifier Specifications
Number of Channels 8
Signal–to–Noise Ratio (SNR) (dB) 121
Input Noise (µVPP) 1.39
Common–Mode Rejection Ratio (CMRR)
(dB)
110
Analog–to–Digital Converter (ADC)
Resolution (bits)
24
Impedance (MΩ) 1000
Maximum Sampling Rate (Hz) 500

Sensors 2023,23, 5930 5 of 26
Table 1. Cont.
Amplifier Specifications
Bandwidth (Hz) DC-131
Input range (mV) ±104
Resolution (µV) 0.012
Inertial Measurement Unit Specifications
ADC 16
Gyro Full-Scale Range (dps) 250–2000
Acc Full-Scale Range (g) 2–16
Zero offset error (for 250 dps) 5
Zero-g Offset (mg) ±50
Power Consumption Acc+Mgn (mW) 0.58
Power Consumption Gyro (mW) 4.43
Brain–Computer Interface Specifications
Processor Speed (GHz) 1
Processor Memory (MB) 512
Processor Storage (GB) 4
Open-Loop Sampling Frequency (Hz) 80
Closed-Loop Sampling Frequency (Hz) 40
Communication 802.11 b/g/n WiFi
Backend Coding Language LabVIEW
Frontend Coding Language JavaScript (JS), Cascading Style Sheets (CSS),
HyperText Markup Language (HTML)
Machine Learning Capability Support Vector Machine
De-noising Capabilities Low- and High-Pass Filters; Adaptive
Noise Cancellation
Battery Capacity (kWh) 2.96
2.1. Headset Design
Proper headset fit for the users is a critical factor affecting the system’s performance,
usability, and comfort, but most headsets on the market do not fit as well as research-grade
soft EEG caps [
43
,
44
]. Traditional soft EEG caps are still the most widely available option
in terms of accommodating both head size and shape variations [
45
,
46
]; however, they
have some disadvantages compared to a headset: (1) Disinfection: Headsets can be
disinfected by surface cleaning while EEG caps need to be immersed, after removing the
electrodes, into a disinfection solution for several minutes; (2) Donning/doffing: Headsets
are usually faster to set up than EEG caps, which may require assistance, particularly
if based on wet electrodes; (3) Electrode localization: Headsets can help to maintain
correct electrode positioning while EEG caps may result in electrode displacements from
session to session; (4) Fitting: Headsets typically have a mechanism for fitting head shape
and size, whereas EEG caps need to be selected in some discrete ranges varying from
small to extra-large, which may lead to poor electrode set-up in some cases as head size
variations are continuous; (5) Form factor: Headsets may be more desirable in terms of
the aesthetics than EEG caps; (6) Single-hand use: Headsets may allow single-hand use
for donning/doffing, which may be critical for users with hemiparesis or other hand
disabilities. Overall, the wide range of variations in human body biometrics demands
flexibility and adjustability in designing a more accommodating headset. Anthropometry

Sensors 2023,23, 5930 6 of 26
data are widely used as a reference of variations to design products with optimized fit,
comfort, functionality, and safety [
47
]. In terms of size management, there are two different
approaches. One approach is to offer the headset in different sizes to fit a wide range of
users. Another approach is to offer a single size with adequate adjustments in multiple
degrees to fit all users. Previous research in the development of a one-size-fits-all headset
has found success, providing support for this approach [48,49].
One important requirement in the design of mobile devices is the need for single-handed
device interaction as the headset will likely be used by people with a limited attention span
and upper-limb and/or hand impairments, including reduced mobility and hand dexterity
(e.g., older individuals and persons with chronic stroke [
17
]). These physical limitations
significantly influence the details of the design, the mechanical controls, and the overall
form factor. As indicated in other studies [
50
], the hardware design influences the user’s
interaction with the device. For this reason, the design process should include a detailed
ergonomic evaluation to ensure all controls are intuitive for one-hand use.
As a device to be used directly by consumers, general usability factors should be
considered and optimized, including the overall weight, adjustability, operational clarity
and accuracy, user comfort, and aesthetics [
51
]. Additionally, a good fixation of the
scalp and skin electrodes should be provided for reducing the contact impedance at the
electrode–scalp/skin interface, which enhances the signal-to-noise ratio [46].
2.1.1. Electrodes
The headset design process began by selecting the locations of five EEG channels. Five
electrode locations (Frontocentral locations: FC3, FC1, FCz, FC2, and FC4) were selected
with a reference to the international 10–20 system provided by the American Clinical
Neurophysiology Society guidelines [
52
]. These were selected based on the proximity to
the primary motor cortex and the effectiveness of using these electrodes for motor-related
BCI paradigms, including motor imagery classification [
53
] and movement-related cortical
potential (MRCP) identification [
17
]. The authors note that the electrode locations can be
modified within the 3D headset model for paradigms that require EEG collection from
other areas of the scalp.
Dry EEG comb electrodes with 5 mm extended prongs (Florida Research Instruments, Inc.,
Cocoa Beach, FL, USA) were selected for this device to maximize the usability and shorten the
set-up time. Comb electrodes [
54
] are recognized as an effective solution for collecting EEGs
through longer-hair conditions and the selected electrodes end in blunt tips for long-term
wearing comfort. While these dry electrodes alone will likely go through users’ different
hairstyles and/or hair types to reach the scalp, without a specific mechanism to secure and
maintain a constant steady contact during use, it is still likely to fail the needs of most users
and needed to be addressed during the design of the EEG electrode holders.
An additional functionality of the headset is the capability to measure eye movements
and eye blinks using electrooculography (EOG) sensors, whose outputs could be used for
real-time de-noising of the EEG signals or even as additional control signals. Ancillary
experiments (to be reported elsewhere) provided support that three EOG sensors can be
used to effectively extract information about eye blinks and eye movements in the vertical,
horizontal, and oblique axes. These EOG sensor locations are located at the right temple,
the left temple, and directly above the participant’s left eye.
Two electrodes, one behind each ear, complete the set of electrodes/sensors available
in the headset. The skin sensors are adjustable in position and orientation to adapt to and fit
a wide range of face profiles and contours while maintaining a constant and steady contact.
2.1.2. EEG Electrode-Holder Design
One challenge for mobile EEG systems is to secure the electrodes and obtain good
impedance for recordings. 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

Sensors 2023,23, 5930 7 of 26
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. The holder is a proprietary (patent-pending) design for
holding the designated electrode while providing a self-positioning rotational mechanical
linkage that helps facilitate hair penetration of the electrode tips. The holder is 1.7 cm in
diameter and 1.9 cm in height and is composed of three parts: the slider, the housing, and
the cap. A screw-and-nut pair is used to fasten the electrode tip to the lower end of the
slider. The fully shielded electrical wire is oriented between the screw and the electrode’s
inner wall. The wire is routed through the center open space and the center hole on the cap.
The slider is spring-loaded with a vertical travel of up to 10 mm. The electrode will move
up and down along three spiral tracks, which allows for rotation of up to 120 degrees, to
accommodate the regionally changing head shape. This rotation will assist the electrode
tip in moving through the user’s hair for improved contact with the scalp. The spring will
help to maintain a constant pressure between the electrode and the contact surface. The
headset and electrode tip design are covered by US provisional patent #62857263.
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.
2.1.3. EOG Electrode-Holder Design
The headset system includes three electrooculography (EOG) sensors to track the
users’ eye movements. Two sensors are positioned at the temple area along the side of each
eye and a third is positioned directly above the user’s left eye. Typically, EOG skin sensors
require the application of a conductive gel medium or tape to achieve steady constant
contact with the skin. This headset is designed with accessibility for individuals with
limited dexterity, so it is undesirable to use sensors that require gel or tape. For that reason,
the headset uses dry skin sensors.
A proprietary EOG sensor-holder arm was developed to maintain a constant contact
with the skin. The holder is composed of two parts: an arm and an EOG sensor plug
(Figure 2C). The EOG sensor sits in the socket of the plug and is wired through the hollow
arm, which is connected to the main board. The arm is printed in a medical-grade skin-safe
flexible resin and is designed with a unique structure and form that makes it flexible while
maintaining a constant pressure at the tip. The EOG plug is formed similarly to accordion
pleats, which makes the plug compressible and can be flexed in any direction. The plug
sits in an opening at the tip of the arm with an interference fit. The arm is rotatable around
the connection on the structure to handle variations in face contours between users. The

Sensors 2023,23, 5930 8 of 26
sensor plug’s spring motion applies a constant pressure to the skin surface to maintain a
steady contact.
2.1.4. Headset Size and Adjustment Mechanism Design
Anthropometric data [
57
] were used to determine the overall device size in relation
to the range of head size variations. The sizing parameters are referenced from the
measurements of the smallest (5th percentile female) to the largest (95th percentile male)
head sizes. The key dimensions in design consideration are the head breadth, circumference,
and length. The size range in three dimensions provides a guide for the design of the
adjustment mechanisms. The differences in head breadth, circumference, and length
between the 5th percentile female and the 95th percentile male are 2.7 cm, 8.9 cm, and 4.1 cm,
respectively. A digital mannequin corresponding to the 5th percentile female was developed
and then scaled up to the 95th percentile male. These two digital mannequin models served
as the basis to build the headset model in a 3D digital SolidWorks environment.
Traditional anthropometry calculation is based on a uniform variation in several
dimensions. For instance, if the head length increases, the head breadth is expected to also
increase by a consistent ratio. In some cases, the head breadth and the head length do not
follow the common ratio due to unique head forms. This characteristic was confirmed with
the real-world data collection for this study, which helped to determine a more realistic
range of deviation. Due to this discrepancy, the head-breadth-adjustment mechanism
was designed to be independent of the head-length-adjustment mechanism. Based on
the electrode mapping and the general mechanical adjustment concept, an initial headset
structure was developed, which includes 3-degree of freedom adjustments with a sufficient
range to fit 90% of all users.
The final design (Figure 2A) utilizes a large dial (6.5 cm in diameter and 0.4 cm in
thickness) in the back to adjust the overall circumference. The end of the ear-hub band is
designed with gear teeth in a slot along the center line. The left and right band overlap
in the electrical box where they connect to the dial through the gear. The outer perimeter
of the dial is shaped with fine convex diamond textures. The dial protrudes 0.6 cm out of
the box and is designed to be turned easily in both directions with one finger. The dial’s
clockwise rotation will extend two ear-hub parts to increase the headset circumference,
whereas the counter-clockwise rotation will contract two parts to reduce the circumference.
The overall circumference adjustment range is up to 8.9 cm. With a unique semi-flexible
structure design, the headset is a one-size-fits-all solution.
2.1.5. Headset Fabrication
The 3D model for the headset was designed with SolidWorks (SolidWorks 2019,
Dassault Systemes, Vélizy-Villacoublay, France) and prototyped with a 3D printing process.
Two types of printers were used in producing the prototype. An Artillery Sidewinder
X1 FDM printer (manufactured by Shenzhen Yuntu Chuangzhi Technology Co., Ltd.,
Shenzhen, China) was used for the rigid-structure printing, while a Saturn resin printer
(manufactured by ELEGOO technology Co., Ltd., Shenzhen, China) was used to print the
flexible components. Two medical-grade thermoplastic resins were selected for the primary
headset components: Taulman Nylon 910 (produced by Taulman3D Material, Linton, IN,
USA) and Flexible 80A resin (produced by Formlabs in Somerville, MA, USA). The Taulman
Nylon 910 resin was used to build the rigid structural parts of the headset as it has similar
strength and stiffness to polypropulene (PP) and is FDA-approved for skin contact, and
the parts can be repeatedly bent while still returning to the original shape. The Flexible
80A resin was used to build all elastic parts and is also FDA-approved for skin contact. The
resulting flexible headset parts are stiff but soft with an 80A shore durometer. In addition
to these two primary resins, two additional resins were used for the internal components.
Esun PLA+ was used to fabricate the rear adjustment plate and dry EEG brackets while
Polymax PC resin (Polymaker, Shanghai, China) was used to fabricate the ratchet gear and

Sensors 2023,23, 5930 9 of 26
adjustment dial. The finalized design is presented in Figure 2. From an aesthetic standpoint,
an emphasis was placed on creating a headset with clean and smooth external surfaces.
2.2. Design of the BCI Module
The following subsections detail the hardware and software component selections and
development for the BCI module.
2.2.1. Hardware Selections and Development
The primary hardware considerations of the BCI module include the selection of
the processing unit, the design and manufacturing of the custom amplifier, and the
power system.
Processor Selection
The BeagleBone Black—Wireless (BBB-W) [
58
] was selected as the BCI processor for
its low cost, availability, compatibility, and WiFi capabilities. Moreover, the availability of
an open-source LabVIEW toolkit (LINX LabVIEW [
59
]) significantly reduced the software
redesign. The BBB-W has a 1 GHz ARM processor, 512 MB of DDR3 RAM, and 4 GB of
onboard storage, providing the computational power and storage space necessary for the
BCI headset.
Design of the Integrated Amplifier and Processing Board
In EEG systems, an instrumentation amplifier acts to increase the amplitude of the
detected signal to a level that can be further processed while an input buffer amplifier
eliminates the need for impedance matching. Recently, the term amplifier has been
broadened to also include the digitization of the analog signal through an analog-to-digital
conversion (ADC) chip, wireless communication, and motion-detection system. In the
proposed BCI system, there are three main components on the amplifier board: signal
amplification, analog-to-digital conversion, and motion sensing. Following the ADC step,
it is necessary to pre-process the signals before transmission to the processing unit. These
steps are summarized in Figure 3.
Figure 3. Block diagram of the EEG amplifier board.
With respect to the amplifier, there are some electrical characteristics that are expected
with any EEG amplifier [
60
]. The ADS1299 chip from Texas Instruments (Dallas, TX,
USA) [
61
] was selected as it best matched the intended functionality. Its characteristics are
summarized in Table 1—section Amplifier Characteristics. The minimum requirements for
the inertial measurement unit (IMU), which provides motion sensing, were low energy
consumption, a digital signal with more than 10 bits resolution, and the inclusion of a 3-axis
accelerometer and a 3-axis gyroscope. Table A1 in the Appendix Asection presents the
characteristics of the ICM-20948 [
62
], which was selected because of its low error, its low
power consumption, and the availability of a magnetometer.
For communication between the amplifier and the processing board, either an integrated
approach or a system that relies on Bluetooth for communication between these modules
must be selected. Rather than develop independent amplifier and processing board
hardware modules that would communicate over Bluetooth, the possibility of missing data

Sensors 2023,23, 5930 10 of 26
packets in this crucial stage, Bluetooth’s line-of-sight requirement, and the computational
capabilities of the BBB made an integrated amplifier and processing unit more desirable. For
this combined unit, the serial peripheral interface (SPI) communication protocol was employed
for communication between the processing unit and the directly connected amplifier.
Power System
The BBB amplifier is powered by a relatively small 3.7 V battery (BatterySpace p/n:
PL-383562-2C single cell Polymer Li-Ion 3.7 V/800 mAh/2.96 Wh,
64 mm ×36 mm ×4 mm
/
18 g, UL listed, UN approved battery) because portability was an important design
factor [
63
]. Based on the maximum expected power consumption of 1.48 kWh from
our system due to signal processing and constant communication with an external device
(e.g., smart phone or tablet), the battery guarantees at least two hours of use. For charging
of the battery, the procedure described in the “Battery Power Source/Charger” section of
the OSD3358 Application Guide [64] was implemented for the system.
2.2.2. Software
For the development of the device, LabVIEW (National Instruments Inc., Austin, TX,
USA) was selected as the primary coding language due to its extensive libraries and access
to National Instruments’ hardware and software in the early phases of the design. We note,
however, that any coding language could instead be used with the selected hardware, and, in
fact, a C++ version of the BCI firmware module has also been developed. This section details
the main considerations, modular design, and resulting open- and closed-loop characteristics
for the system software. The primary focus throughout the software development was
on maintaining real-time capability, modularity, and flexibility to implement different BCI
applications, thereby increasing the interoperability of the system.
Firmware
While LabVIEW real-time toolkits can sample at a constant frequency, this functionality
requires the National Instruments onboard hardware clock, so setting a constant sampling
frequency through LabVIEW is not possible on third-party processing boards. The firmware
designed for the system instead employs spline interpolation, so the system can sample
EEG and EOG at a rate set by the user, limited only by the computational power of the
processing board. We have also developed a faster C++ implementation that does not
require interpolation.
Communication
The BeagleBone Black—Wireless (BeagleBoard.org Foundation, Oakland Charter Township,
MI, USA) processing board has both WiFi and Bluetooth capabilities (802.11 b/g/n WiFi and
Bluetooth 4.1 plus BLE), which are important for the goal of creating a completely portable
system. This gives the BCI device the capability of communicating with any device that
can be controlled remotely. In addition to communicating with WiFi-enabled devices, to
remain completely portable, the device includes a user interface that communicates with
the system through the available LabVIEW web service. For the design of this interface,
HyperText Markup Language (HTML), Cascading Style Sheets (CSS), and Javascript (JS)
were selected as the base languages for the interface, since they can be used for the creation
of a cross-platform interface that can be accessed from any browser and display that can
handle the computational demands of the system.
Open-Loop Capabilities
The BCI device can be used to collect and save raw data from a user according to
an easily modifiable protocol. These data include five EEG channels, three EOG channels,
and accelerometer data from the IMU. Due to the design considerations, the maximum
sampling rate that can be achieved for raw data collection and saving is 80 Hz. To achieve this
sampling rate, the system utilizes LabVIEW’s point-by-point virtual instruments and channel

Sensors 2023,23, 5930 11 of 26
mechanisms. Sampling up to 80 Hz means future applications can be developed that require
a spectral analysis of the Delta, Theta, Alpha, Beta, and lower Gamma frequency bands.
EEG De-Noising Capabilities
We implemented various real-time de-noising capabilities, including spline interpolation,
low-pass filters, high-pass filters, and an H-Infinity adaptive noise cancellation filtering
framework. Spline interpolation provides a mechanism to handle any lost data packets as
well as the ability to maintain a constant sampling frequency, a requirement for accurate
filtering. The low- and high-pass filters allow for the isolation of frequency bands, a
method that can be used for the spectral analysis commonly found in EEG signal-processing
paradigms. The H-infinity filter employs data collected from the three EOG sensors in the
automatic real-time removal of eye movement and eye blink artifacts [
5
], which is one of
the most common biological artifacts affecting EEG. In addition, it can detect and remove
amplitude drifts and recording biases simultaneously [
5
]. A recent extension can identify
and remove motion artifacts as well [6].
Closed-Loop Capabilities
To test the closed-loop capabilities of the system, an example experimental protocol
was implemented. This experimental protocol includes a real-time signal processing
pipeline, training data collection, training of a machine learning model, testing of the
trained model in real time, a graphical user interface (GUI), and constant communication
with a third-party device. Due to design considerations, the system processes EEG and
EOG data at 40 Hz and can save data at 20 Hz while simultaneously processing the signal,
controlling a third-party WiFi device/object, and controlling a user interface over the web
server. Sampling at up to 40 Hz supports applications that require a spectral analysis
of the Delta, Theta, Alpha, and lower Beta bands. The authors note that further coding
optimization effort could be made on the firmware design, which would likely allow for
higher sampling frequencies.
Modular Software Design
While specific experimental protocols can influence the overall system software
design, there are several key modules that will appear in many BCI systems. These
common modules include an impedance check to assess the signal quality, a module
for implementing the data-collection parameters and machine learning model training, a
module to allow for user feedback through a survey mechanism, and a module for user
help and troubleshooting. Additionally, as the system is designed to be used both inside
and outside of a clinical setting, an extensive debugging user interface is necessary. The
authors emphasize that the current system software is designed to be modifiable for many
BCI applications.
Aesthetic Design of the User Interface—There were several aesthetic choices made
during the user interface development that helped to further enhance the usability of
the system. Colors and sizes were optimized to account for possible vision deficits by
end-users. This includes large font sizes and components for those with poor vision
and a color-blind-friendly design [
65
]. The development focused on hemianopia- and
nystagmus-friendly design features, such as the button and icon designs, the logo position
as a reference point, easing the cognitive workload, and creating a simple but appealing
design [66].
Impedance Check—Ensuring signal quality involves measuring and displaying impedance
values for the user so that, for electrodes that show high impedance values, the user can
adjust the electrodes accordingly. Real-time display of these impedance values is therefore
an essential module for BCI systems. Here, the module is designed to set up the amplifier,
interpolate at a constant sampling frequency, filter at the prescribed subband range (as
designated by the ADS1299 documentation), and send the resulting impedance values to
the user interface in real time.

Sensors 2023,23, 5930 12 of 26
Model Calibration—For applications that rely on machine learning model predictions
for the acquisition of a control signal, training data must first be collected to train the
machine learning model. The system allows for customization of the protocol for different
BCI paradigms. Functionality has been built to allow for the acquisition of multiple days of
data, which can then be used to train a machine learning model or monitor task performance
and progress. As an initial machine learning model selection, the system includes a support
vector machine (SVM) library (including hyperparameter optimization and n-fold cross
validation), which the user can initiate from the user interface. Once the SVM model
is trained, the user is then able to proceed with the model-testing stage. In addition
to collecting EEG data for each testing trial, this module also collects protocol-specific
characteristics, which can be analyzed later by a clinician or researcher to verify the progress
of a user through a specific protocol. The authors note that, while only an SVM library has
been developed, many types of machine learning model can be implemented in the device
within the limits of the available onboard memory.
Survey Collection—The proposed system includes a survey functionality that gives the
user a way to provide feedback, which can be completed at any time. These results are
stored onboard the processing unit for further analysis. This pop-up interface is presented
in Section 3.3.1 , which can be modified depending on the type of feedback desired for a
particular application.
Debugging Interface—For ease of use, significant effort was made in developing a
debugging user interface. The device’s debugging interface, presented in Section 3.3.1, includes
mechanisms to check whether the internal LabVIEW script is running, whether the web
server is correctly activated, a signal-impedance check with a channel-selection mechanism,
and a device-communication check. This provides the user with a series of simple steps
that can be performed without guidance to address potential system faults. The debugging
home screen provides the user with easy-to-understand descriptions of each debugging
page to make troubleshooting as painless as possible.
2.3. System Validation
To demonstrate the features and functionality of the system, assessments were designed
to validate three key areas: the headset design, the open-loop capability of the system, and
the closed-loop capability of the system (see Table 2).
Table 2. Bench testing and human-subject validation methodology.
Headset Design Validation
Test Name Description Assessment Tool/Specifications
System Comfort Evaluation of user’s
comfort level
Questionnaire/Likert scale
System Usability System Usability Scale
(SUS) [28]
SUS > 65 [67]
Open-Loop BCI Validation
Test Name Description Target Specifications
Signal Quality Assessment of electrode and
skin sensor impedance
Impedance < 100 kOhm
Eye Tracking EOG evaluation Detection of eye blinks and
eye movements

Sensors 2023,23, 5930 13 of 26
Table 2. Cont.
Open-Loop BCI Validation
Synchronized
EEG-EOG-IMU
Acquire multi-modality data
streams to confirm
synchronized streaming
of data
Synchronized EEG-EOG-IMU
recordings ≤4 ms
Open-loop BCI
Performance
Assessment of EEG power
modulations in delta and mu
bands during a
GO-NOGO task
Event-related
desynchronization/synchronization
(ERD/ERS)
Closed-Loop Brain–Computer Interface Validation
Test Name Description Target Specifications
IoT Functionality Assess communication rates
between the headset and
multiple types of devices
Communication rate < 50 ms for all
connected devices
SVM Model
Training
Evaluation of decoding
accuracy for motor intent
Model accuracy ≥80%; detection
of MRCPs
Closed-loop
Performance
Evaluation of trained SVM for
online prediction of
motor intent
≤50 ms closed-loop performance
All tests were performed either at the University of Houston (UH) under a human-subjects
protocol approved by the Institutional Review Board (IRB) at UH (IRB studies #3430 and
#2515) or at the University of Texas Health Science Center of Houston (under IRB study
HSC-MS-20-1287). Five neurologically intact adults (four males and one female) were recruited
and underwent a series of tests for validation of the headset design and open-loop BCI
functionality. One 66-year-old male participant with chronic stroke, with hemiparisis on the left
side of his body, participated in the validation of the closed-loop functionality during at-home
use. All recruited participants gave their written informed consent prior to testing.
2.3.1. Headset Design Validation
Usability testing was conducted to validate the headset design. The testing focused on
two key aspects: the overall participant comfort of the system during extended periods of
use and the overall usability of the system based on the System Usability Scale (SUS) [
28
,
68
].
These tests were carried out with a diverse set of participants with varied head sizes, shapes,
and hair types.
2.3.2. Open-Loop Brain–Computer Interface Validation
To evaluate the functionality of the BCI, a set of tests was performed that focused
on the performance of the BCI in open-loop operations, impedance measurement, EOG
measurement, and the synchronization of EOG, EEG, and head-movement data in real time.
2.3.3. Closed-Loop Brain–Computer Interface Validation
To assess the closed-loop capabilities of the system, an example deployment application
from the neurorehabilitation literature was selected [
17
]. Specifically, a BCI–robot system,
including an IoT-enabled robotic device and a tablet with a custom graphical user interface
(GUI), is presented as an example of deployment in a neurorehabilitation application;
see Figure 4. This specific implementation was chosen based on previous research on
a closed-loop BCI for rehabilitation [
17
]. In their study, the authors developed a BCI
system for upper-limb rehabilitation after stroke that focused on detecting motor intent to
control a motorized exoskeleton for the upper limb. They achieved this by identifying a
movement-related cortical potential (MRCP) that precedes voluntary movements of the
upper limb (e.g., readiness potential). This type of cortical potential has been extensively

Sensors 2023,23, 5930 14 of 26
studied [
69
–
74
] as a means of predicting motor intent. However, other brain features, such
as changes in EEG rhythms, could be used to detect motor intent.
In that study [
17
], the authors utilized an expensive high-density EEG system and a
custom motorized upper-limb exoskeleton supervised by a team of trained technicians and
physical therapists to conduct the clinical trial in stroke survivors. Encouraging clinical
results were observed, with all participants showing sustained improvements in motor
abilities following the cessation of the rehabilitation protocol. These positive outcomes,
along with the necessity for increasing accessibility, usability, interoperability, and mobile
deployment at home made this example application suitable for validation of the proposed
BCI headset. In addition to the development of the system itself, this example deployment
required the collection of data both in the clinic and at the participant’s home, which
allowed for an assessment of the system’s usability outside of the clinic.
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.
3. Results
3.1. Headset Design Validation Results
In this section, we report the results from the system comfort and system usability
scale assessments. These assessments were carried out to validate the final designs for the
overall headset and the electrode holders.
3.1.1. System Comfort Test
Table 3shows the system comfort results from five participants with a diverse range
of head shapes, sizes, and hair types. Participants responded to the following questions:
“Did the headset move during the study?”, “Did the headset cause the sensation of dents
on your head?”, “Did the headset feel too big on your head?”, and “Did the headset feel
too small on your head?”. The participants could choose from the following rating values:

Sensors 2023,23, 5930 15 of 26
“Strongly Agree”, “Agree”, “Neutral”, “Disagree”, and “Strongly Disagree”. Although
the overall level of comfort across the participants was high (e.g., 4.6/5 for three of the
questions), two reported reduced comfort in one item due to the occurrence of feeling of
dents on their scalp after two hours of use.
During this assessment, it was confirmed that when a female participant whose head
measurements matched the fifth percentile of female head sizes wore the headset, the
headset was in its fully contracted state with a comfortable and secure fit. When repeating
this assessment with a participant near the 95th percentile of male head circumference, the
headband’s vertical sizing mechanism expanded 1.9 cm on both sides to accommodate the
larger distance between the top of the head and the ears.
Table 3. Comfort Score: 1: “Strongly Agree” to 5: “Strongly Disagree”.
Participant # “Moving” “Dents” “Too Big” “Too Small”
S1 5 5 5 5
S2 5 2 5 5
S3 4 2 3 3
S4 4 2 5 5
S5 5 3 5 5
Mean 4.6 2.8 4.6 4.6
SD 0.548 1.304 0.894 0.894
3.1.2. System Usability Test
The SUS [
28
] was used to assess the usability of the system. This metric has been
employed previously in the assessment of usability for other BCI systems [
67
,
75
]. For
the proposed system, the average SUS score among the five participants was 90.5, which
is above the threshold (65) for an acceptable system [
67
]. All participants were able to
independently and intuitively don the headset with only one hand.
3.2. Open-Loop BCI Validation
In this section, we report the results from the signal quality, EOG collection, IMU
synchronization, and open-loop BCI assessments.
3.2.1. Signal-Quality Test
The impedance values from all electrodes were collected before and after the open-loop
BCI test. The beginning and final impedance values for each electrode are presented in
Figure 5. For all but two electrode impedance measurements, the electrode impedance
values remained under 100 kΩand for most electrodes they remained under 50 kΩ.

Sensors 2023,23, 5930 16 of 26
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 kΩ.
3.2.2. Eye-Tracking Test
In this test, we recorded eye blinks and horizontal and vertical eye movements from
a center position using the GUI. Examples of eye blinks and tracking of eye movements,
which were acquired at 80 Hz, are presented in Figure 6. Measurements of eye movements
and eye blinks are critical for the identification and removal of ocular artifacts from
EEGs in BCI systems or for use as additional signal sources for control. In our proposed
system, H-infinity adaptive noise cancellation, an adaptive filtering technique that requires
representations of the EOG signals, is implemented on board for real-time operation [5].
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.
3.2.3. Synchronized EEG–EOG–IMU Test
The synchronized acquisition of EEG, EOG, and IMU data from the user’s head
is important for characterizing head movement and the identification and removal of
potential motion artifacts from the EEG signals [
6
]. Figure 7depicts raster plots of
EEG measurements acquired during conditions with (A) eyes closed, (B) eyes open, and
(C) head movements collected at 80 Hz. A band-pass filter from 1 Hz to 50 Hz was applied
to the signals and no additional de-noising methods were employed. Figure 7C depicts
a raster plot showing synchronized EEG and IMU recordings during head movements
towards the front, back, left, and right for one participant. As expected, the head motion,

Sensors 2023,23, 5930 17 of 26
as displayed by the IMU channels (e.g., ACC and GYRO), coincides with motion artifact
contamination of the EEG data. Additionally, as compared to the eyes-open and eyes-closed
conditions, EEGs during head movement experience an increase in gamma activity due
to EMG contamination, which matches the prior literature on EMG contamination during
head movement (see Figure 7D) [76].
Figure 7D depicts the spectral characteristics of EEG during eyes-open, eyes-closed,
and head-movement conditions. These spectral characteristics demonstrate the 1/f spectrum
typical of EEG signals. Moreover, the EEG during the eyes-closed condition shows a modest
increase in alpha (8–12 Hz) power as compared to the eyes-open condition [
77
] as the
electrodes are positioned over motor areas rather than occipital areas where large alpha
waves would be expected.
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.
3.2.4. Open-Loop Performance
To further assess the spectral characteristics of EEG, four participants underwent two
blocks of 21 trials of a simple GO–NOGO paradigm. In this paradigm, the system’s user
interface first asked the participant to fix their attention on a cross (NOGO) for five seconds.
The user’s interface then presented a circle and indicated to the user to move their arm
from a horizontal to a vertical position (GO). The expected spectral trend for a paradigm of
this nature [
78
–
80
] would be that, when moving from NOGO to GO, the relative power in
the
δ
band should increase while the relative power in the
µ
band should decrease. Figure 8
shows that the relative power in these two bands for all participants follow our expectations.
The paired t-tests with Rest/Move factor for all electrodes, except
FC4
, were significant
(p< 0.0001) : t(167) = 11.8, p= 9.3 ×10−24 for δ, and t(167) = −9.3, p= 7.0 ×10−17 for µ.
3.3. Closed-Loop BCI Validation
In this section, we report the findings from closed-loop BCI assessments, including IoT
functionality and BCI decoder training and performance. The closed-loop BCI validation
was designed based on the BCI–robot neurorehabilitation study described in [
17
,
81
] and
tested on an individual diagnosed with chronic stroke. A significant difference is that testing
of the participant was carried out first at the clinic and then at his home, as described below.

Sensors 2023,23, 5930 18 of 26
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
δ
and
µ
frequency bands among participants. The average is based on two
blocks with twenty trials each.
3.3.1. IoT Functionality Test
A general BCI system must be able to interact with a wide range of IoT-enabled devices.
In this example deployment, the system’s communication rate via WiFi was verified in
two ways: communication with a robot rehabilitation device and with several different
WiFi-enabled tablets for the visual (GUI) display. In this test, communication with the
rehabilitation device was found to remain under 50 ms. The displays and browsers tested
include the iPhone (7+ or greater) and an Amazon Fire tablet with the Google Chrome,
Microsoft Edge, and Amazon Silk browsers, with all tested browsers and displays able to
maintain a communication rate under 25 ms. Figure 9presents the GUI developed for this
example deployment and the means to assess the real-time communication rate between
the tablet and the system.
3.3.2. Support Vector Machine Model Training
Figure 10 presents the movement-related cortical potentials (MRCPs) recorded through
the experimental protocol, which were then used to train the SVM neural decoding model.
Table 4presents the decoding accuracies on S005’s data for a model trained with the
hyperparameters displayed in Table 4, where the rejection rate refers to the amount of
outliers in the data to be rejected for the training and validation of the model. All models
were trained with four-fold cross-validation.

Sensors 2023,23, 5930 19 of 26
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).

Sensors 2023,23, 5930 20 of 26
Table 4. Hyperparameter optimization: closed-loop model hyperparameter optimization using
4-fold cross-validation on participant S005’s data.
Hyperparameter Optimization
Rejection Rate Channels Not Used Accuracy
0 - 85.5%
0.1 - 97.4%
0.323 - 100.0%
0.3 - 100.0%
0 FC3 96.3%
0.1 FC3 98.6%
0.2 FC3 99.3%
0.3 FC3 99.1%
3.3.3. Closed-Loop BCI Performance
To assess whether the trained SVM could correctly predict motor intent during
closed-loop BCI operation, the system was deployed during a series of GO (Move)–NOGO
(Fixate) trials at the participant’s home after initial calibration in a clinical setting (Figure 11).
For this test, the participant underwent two sessions per day, with each session consisting of
three blocks of 20 trials over a period of six weeks and an average of six sessions per week.
In Figure 12, we present signals classified by the trained model as representative of motor
intent, where “Movement Intent” indicates when the model detected the participant’s
motor intent using MRCPs. Each of these signals is the average of the 20 trials from the
first block at the start of the protocol (in blue) and the last block at the end of the protocol
(in orange). We can see here how the MRCP evolves across the six weeks of at-home BCI
therapy for four of the five EEG channels (FC
4
, FC
2
, FC
Z
, FC
1
). This evolution is not evident
in the case of
FC3
, which is the result of the relatively poor contact between that channel
and the scalp of the participant, which had impedance values of greater than 100 kΩ).
Figure 11.
The closed-loop BCI–robot neurorehabilitation system in use at the home of the participant
with chronic stroke.

Sensors 2023,23, 5930 21 of 26
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.
4. Discussion and Conclusions
The design and validation of a custom EEG-based closed-loop BCI headset with
onboard processing capabilities has been presented in this report. The design criteria
required the consideration of a number of factors. Here, we have developed a minimal
viable solution to this design task that is low-cost, portable, wireless, and easy to use and
has high interoperability. To ensure a comfortable user experience, the proposed solution
has a form factor that provides a one-size-fits-all approach and includes a user-friendly
graphical interface for use at home. Additionally, the system has real-time adaptive signal
de-noising and decoding capabilities built into the onboard processing board, making the
system fully contained within the headset, a feature not currently found in off-the-shelf
commercially available systems. All components of the system have been extensively bench
tested and also validated with healthy adults, including an individual with chronic stroke.
In the development of the proposed system, the importance of understanding the
cascading nature of single design decisions cannot be overstated. Early design decisions
can significantly impact the available options for hardware and software functionality and
overall system operation. For the current system, the most influential design choice was in
the selection of LabVIEW as the back-end coding language. While LabVIEW has a large
number of well-tested libraries available, many of these libraries require a processing board
developed by National Instruments. Due to the cost of those boards, the selection of the
processing board was limited by whether the board was capable of using an open-source
user-built LabVIEW library, which is not as well-tested as the libraries developed by
National Instruments. Many of the challenges faced in the development of the proposed
system were due to incompatibilities between LabVIEW and the low-cost processing board.
Careful selection of the high-level system components (such as the backend language,
port selections, wireless protocol, etc.) are critical for maximizing the performance and
flexibility. In this regard, and to show the flexibility of our proposed system, we have
recently programmed the board in C++ and achieved an open-loop sampling rate of 250 Hz.
In conclusion, the proposed system should provide an open test bed for developing
low-cost and portable yet effective custom EEG-based closed-loop BCI systems with
wireless capabilities, which will help expand the potential user base and application
domains and increase the feasibility for academic research and workforce development.

Sensors 2023,23, 5930 22 of 26
Author Contributions:
A.C.: Methodology, Software, Firmware, Validation, Writing—original
draft, review and editing. J.J.G.-E.: Methodology, Hardware, Pseudo-code of Amplifier and IMU
Firmware, Power System, Bench Testing, IRB protocol, Validation, Writing—review and editing.
A.A.: Methodology, Graphical User Interface, Validation, Writing—review and editing. D.E.: 3D
Model Design and Prototyping, Writing—review and editing. S.W.: 3D Model Prototyping and
Printing, Writing—review and editing. L.S.R.: Software, Validation, Writing—review and editing.
J.F.: Methodology, 3D Model Design and Prototyping, Supervision, Writing—review and editing.
G.E.F.: Conceptualization, Resources, Supervision, and Writing—review and editing.
J.L.C.-V.: Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Visualization,
Analysis Strategy, and Writing—review and editing. All authors have read and agreed to the
published version of the manuscript.
Funding:
This work was supported by the National Science Foundation (NSF) Partnership for
Innovations (PFI) and Research Experiences for Undergraduates (REU) Supplement Award #1827769
and the NSF Industry–University Cooperative Research Center for Building Reliable Advances and
Innovations in Neurotechnology (IUCRC BRAIN) center Award #2137255. A.A. is funded by a
doctoral fellowship from Jazan University, Saudi Arabia.
Institutional Review Board Statement:
The study was conducted in accordance with the Declaration
of Helsinki, and was performed either at the University of Houston (UH) under a human-subjects
protocol approved by the Institutional Review Board (IRB) at UH (IRB studies #3430, approved
12/17/2021 and #2515, approved 10/19/2020) or at the University of Texas Health Science Center of
Houston (under IRB study HSC-MS-20-1287, approved 2/22/2021)
Informed Consent Statement:
Informed consent was obtained from all participants involved in the
study.
Data Availability Statement:
Data is available upon reasonable request by contacting the corresponding
author.
Acknowledgments:
We would like to acknowledge H Robotics (Austin, TX, USA) for their development
and troubleshooting of the robotic exoskeleton device used for the proof-of-concept validation of our
closed-loop BCI for neurorehabilitation.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Appendix A.1. Inertial Measurement Unit Characteristics
Table A1. Inertial measurement unit: ICM-20948 [62].
Metric ICM-20948
ADC (bits) 16
Dynamic Range (dps) 250–2000
Zero offset error (dps) (at 250 dps) ±5
Zero-g Offset (mg) ±50
Power Acc + Mgn (mW) 0.58
Power Gyro (mW) 4.43

Sensors 2023,23, 5930 23 of 26
Appendix A.2. Exploded Headset Image
Figure A1.
The headset exploded view showing all components. This figure was adapted from
US provisional patent #62857263.
References
1.
Kübler, A. The history of BCI: From a vision for the future to real support for personhood in people with locked-in syndrome.
Neuroethics 2020,13, 163–180. [CrossRef]
2.
Nijholt, A.; Contreras-Vidal, J.; Jeunet, C.; Väljamäe, A. Brain-Computer Interfaces for Non-clinical (Home, Sports, Art,
Entertainment, Education, Well-Being) Applications. Front. Comput. Sci. 2022,4, 860619. [CrossRef]
3.
Urigüen, J.; Garcia-Zapirain, B. EEG artifact removal—State-of-the-art and guidelines. J. Neural Eng.
2015
,12, 031001. [CrossRef]
[PubMed]
4.
Goldenholz, D.; Ahlfors, S.; Hämäläinen, M.; Sharon, D.; Ishitobi, M.; Vaina, L.; Stufflebeam, S. Mapping the signal-to-noise-ratios
of cortical sources in magnetoencephalography and electroencephalography. Hum. Brain Mapp. 2009,30, 1077–1086. [CrossRef]
5.
Kilicarslan, A.; Grossman, R.G.; Contreras-Vidal, J.L. A robust adaptive de-noising framework for real-time artifact removal in
scalp eeg measurements. J. Neural Eng. 2016,13, 026013. [CrossRef]
6.
Kilicarslan, A.; Vidal, J. Characterization and real-time removal of motion artifacts from EEG signals. J. Neural Eng.
2019
,16,
056027. [CrossRef] [PubMed]
7.
Craik, A.; He, Y.; Contreras-Vidal, J.L. Deep learning for electroencephalogram (eeg) classification tasks: A review. J. Neural Eng.
2019,16, 031001. [CrossRef]
8.
Roy, Y.; Banville, H.; Albuquerque, I.; Gramfort, A.; Falk, T.; Faubert, J. Deep learning-based electroencephalography analysis: A
systematic review. J. Neural Eng. 2019,16, 051001. [CrossRef]
9.
Abiri, R.; Borhani, S.; Sellers, E.; Jiang, Y.; Zhao, X. A comprehensive review of EEG-based brain—Computer interface paradigms.
J. Neural Eng. 2019,16, 011001. [CrossRef]
10.
Steele, A.G.; Parekh, S.; Azgomi, H.F.; Ahmadi, M.B.; Craik, A.; Pati, S.; Francis, J.T.; Contreras-Vidal, J.L.; Faghih, R.T. A mixed
filtering approach for real-time seizure state tracking using multi-channel electroencephalography data. IEEE Trans. Neural Syst.
Rehabil. Eng. 2021,29, 2037–2045. [CrossRef] [PubMed]
11.
Ahmadi, M.B.; Craik, A.; Azgomi, H.F.; Francis, J.T.; Contreras-Vidal, J.L.; Faghih, R.T. Real-time seizure state tracking using two
channels: A mixed-filter approach. In Proceedings of the 2019 53rd Asilomar Conference on Signals, Systems, and Computers,
Pacific Grove, CA, USA, 3–6 November 2019; pp. 2033–2039.
12.
Aboalayon, K.A.I.; Faezipour, M.; Almuhammadi, W.S.; Moslehpour, S. Sleep stage classification using eeg signal analysis: A
comprehensive survey and new investigation. Entropy 2016,18, 272. [CrossRef]
13.
Zhou, Y.; Huang, S.; Xu, Z.; Wang, P.; Wu, X.; Zhang, D. Cognitive workload recognition using eeg signals and machine learning:
A review. IEEE Trans. Cogn. Dev. Syst. 2021,14, 799–818 . [CrossRef]

Sensors 2023,23, 5930 24 of 26
14.
Craik, A.; Kilicarslan, A.; Contreras-Vidal, J.L. Classification and transfer learning of eeg during a kinesthetic motor imagery
task using deep convolutional neural networks. In Proceedings of the 2019 41st Annual International Conference of the IEEE
Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 3046–3049.
15.
Ferrero, L.; Quiles, V.; Ortiz, M.; Iáñez, E.; Navarro-Arcas, A.; Flores-Yepes, J.; Contreras-Vidal, J.; Azorín, J. Comparison of
different brain–computer interfaces to assess motor imagery using a lower-limb exoskeleton. In Converging Clinical and Engineering
Research on Neurorehabilitation IV, Proceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), Online, 13–16
October 2020; Springer: Berlin/Heidelberg, Germany, 2022; pp. 53–58.
16.
Bundy, D.T.; Souders, L.; Baranyai, K.; Leonard, L.; Schalk, G.; Coker, R.; Moran, D.W.; Huskey, T.; Leuthardt, E.C. Contralesional
brain—Computer interface control of a powered exoskeleton for motor recovery in chronic stroke survivors. Stroke
2017
,48,
1908–1915. [CrossRef]
17.
Bhagat, N.A.; Yozbatiran, N.; Sullivan, J.L.; Paranjape, R.; Losey, C.; Hernandez, Z.; Keser, Z.; Grossman, R.; Francisco, G.;
O’Malley, M.K.; et al. A clinical trial to study changes in neural activity and motor recovery following brain-machine interface
enabled robot-assisted stroke rehabilitation. medRxiv 2020. [CrossRef]
18.
Nijholt, A. Multi-modal and multi-brain-computer interfaces: A review. In Proceedings of the 2015 10th International Conference
on Information, Communications and Signal Processing (ICICS), Singapore, 2–4 December 2015; pp. 1–5.
19.
Hekmatmanesh, A.; Nardelli, P.H.; Handroos, H. Review of the state-of-the-art of brain-controlled vehicles. IEEE Access
2021
,9,
110173–110193. [CrossRef]
20.
Shukla, P.K.; Chaurasiya, R.K. A review on classification methods used in eeg-based home control systems. In Proceedings of the
2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India,
22–25 November 2018; pp. 1–5.
21.
Zhang, R. Virtual reality games based on brain computer interface. In Proceedings of the 2020 International Conference on
Intelligent Computing and Human-Computer Interaction (ICHCI), Sanya, China, 4–6 December 2020; pp. 227–230.
22.
Kerous, B.; Liarokapis, F. Brain-computer interfaces-a survey on interactive virtual environments. In Proceedings of the 2016 8th
International Conference on Games and Virtual Worlds for Serious Applications (vs.-Games), Barcelona, Spain, 7–9 September
2016; pp. 1–4.
23.
Craik, A.; Kilicarslan, A.; Contreras-Vidal, J.L. A translational roadmap for a brain-machine-interface (bmi) system for
rehabilitation. In Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy,
6–9 October 2019; pp. 3613–3618.
24.
Merchak, T.; Goldberg, I. Concept to Clinic: Commercializing Innovation (C3i) Program; The National Institute of Biomedical Imaging
and Bioengineering (NIBIB): Bethesda, MD, USA, 2021.
25.
Paek, A.; Brantley, J.; Ravindran, A.; Nathan, K.; He, Y.; Eguren, D.; Cruz-Garza, J.; Nakagome, S.; Wickramasuriya, D.; Chang, J.;
et al. A roadmap towards standards for neurally controlled end effectors. IEEE Open J. Eng. Med. Biol.
2021
,2, 84–90. [CrossRef]
26.
Bowsher, K.; Civillico, E.; Coburn, J.; Collinger, J.; Contreras-Vidal, J.; Denison, T.; Donoghue, J.; French, J.; Getzoff, N.; Hochberg,
L.; et al. Others Brain–computer interface devices for patients with paralysis and amputation: A meeting report. J. Neural Eng.
2016,13, 023001. [CrossRef]
27. Mooney, J. Strategies for supporting application portability. Computer 1990,23, 59–70. [CrossRef]
28.
Brooke, J. SUS-A Quick and Dirty Usability Scale. Usability Evaluation in Industry; CRC Press: Boca Raton, FL, USA, 1996;
ISBN 9780748404605. Available online: https://www.crcpress.com (accessed on 15 October 2022).
29.
Bevana, N.; Kirakowskib, J.; Maissela, J. What is usability. In Proceedings of the 4th International Conference on HCI, Stuttgart,
Germany, 1–6 September 1991; pp. 1–6.
30.
Stavrakos, S.; Ahmed-Kristensen, S. Assessment of anthropometric methods in headset design. In Proceedings of the DESIGN
2012, The 12th International Design Conference (DS 70), Dubrovnik, Croatia, 21–24 May 2012.
31.
Chabin, T.; Gabriel, D.; Haffen, E.; Moulin, T.; Pazart, L. Are the new mobile wireless EEG headsets reliable for the evaluation of
musical pleasure? PLoS ONE 2020,15, e0244820. [CrossRef]
32. Jiang, X.; Bian, G.; Tian, Z. Removal of artifacts from EEG signals: A review. Sensors 2019,19, 987. [CrossRef]
33.
He, Y.; Eguren, D.; Azorín, J.M.; Grossman, R.G.; Luu, T.P.; Contreras-Vidal, J.L. Brain–machine interfaces for controlling
lower-limb powered robotic systems. J. Neural Eng. 2018,15, 021004. [CrossRef] [PubMed]
34. Open Source Tools for Neuroscience. Available online: https://openbci.com/ (accessed on 2 February 2023).
35. Meditation Made Easy. 2023. Available online: https://choosemuse.com/ (accessed on 2 February 2023).
36.
Xing, X.; Wang, Y.; Pei, W.; Guo, X.; Liu, Z.; Wang, F.; Ming, G.; Zhao, H.; Gui, Q.; Chen, H. A high-speed SSVEP-based BCI using
dry EEG electrodes. Sci. Rep. 2018,8, 14708. [CrossRef] [PubMed]
37.
Arpaia, P.; Callegaro, L.; Cultrera, A.; Esposito, A.; Ortolano, M. Metrological characterization of consumer-grade equipment for
wearable brain—Computer interfaces and extended reality. IEEE Trans. Instrum. Meas. 2021,71, 4002209. [CrossRef]
38.
Fok, S.; Schwartz, R.; Wronkiewicz, M.; Holmes, C.; Zhang, J.; Somers, T.; Bundy, D.; Leuthardt, E. An EEG-based brain computer
interface for rehabilitation and restoration of hand control following stroke using ipsilateral cortical physiology. In Proceedings
of the 2011 Annual International Conference of The IEEE Engineering In Medicine And Biology Society, Boston, MA, USA,
30 August–3 September 2011; pp. 6277–6280.
39.
EMOTIV Emotiv EPOC X: 14 Channel Mobile EEG Headset. EMOTIV. 2023. Available online: https://www.emotiv.com/epoc-x/
(accessed on 2 February 2023).

Sensors 2023,23, 5930 25 of 26
40.
Schalk, G.; McFarl, D.; Hinterberger, T.; Birbaumer, N.; Wolpaw, J. BCI2000: A general-purpose brain-computer interface (BCI)
system. IEEE Trans. Biomed. Eng. 2004,51, 1034–1043. [CrossRef] [PubMed]
41.
Niso, G.; Romero, E.; Moreau, J.T.; Araujo, A.; Krol, L.R. Wireless eeg: An survey of systems and studies. NeuroImage
2022
,269,
119774. [CrossRef]
42.
ISO 9241-210:2010; Ergonomics of Human-System Interaction—Part 210: Human-Centred Design for Interactive Systems. ISO:
Geneva, Switzerland, 2010.
43.
Feng, T.; Kuhn, D.; Ball, K.; Kerick, S.; McDowell, K. Evaluation of a Prototype Low-Cost, Modular, Wireless Electroencephalography
(Eeg) Headset Design for Widespread Application; Army Research Lab.: Adelphi, MD, USA, 2016.
44.
Hairston, W.D.; Whitaker, K.W.; Ries, A.J.; Vettel, J.M.; Bradford, J.C.; Kerick, S.E.; McDowell, K. Usability of four
commercially-oriented eeg systems. J. Neural Eng. 2014,11, 046018. [CrossRef]
45.
Verwulgen, S.; Lacko, D.; Vleugels, J.; Vaes, K.; Danckaers, F.; Bruyne, G.D.; Huysmans, T. A new data structure and workflow for
using 3d anthropometry in the design of wearable products. Int. J. Ind. Ergon. 2018,64, 108–117. [CrossRef]
46.
Li, G.; Wang, S.; Duan, Y.Y. Towards gel-free electrodes: A systematic study of electrode-skin impedance. Sens. Actuators Chem.
2017,241, 1244–1255. [CrossRef]
47. Pheasant, S.; Haslegrave, C.M. Bodyspace: Anthropometry, Ergonomics and the Design of Work; CRC Press: Boca Raton, FL, USA, 2018.
48.
Lacko, D.; Vleugels, J.; Fransen, E.; Huysmans, T.; Bruyne, G.D.; Hulle, M.M.V.; Sijbers, J.; Verwulgen, S. Ergonomic design of an
eeg headset using 3d anthropometry. Appl. Ergon. 2017,58, 128–136. [CrossRef]
49.
Ellena, T.; Subic, A.; Mustafa, H.; Pang, T.Y. The helmet fit index—An intelligent tool for fit assessment and design customisation.
Appl. Ergon. 2016,55, 194–207. [CrossRef]
50.
Karlson, A.K.; Bederson, B.B.; Contreras-Vidal, J. Understanding single-handed mobile device interaction. Handb. Res. User
Interface Des. Eval. Mob. Technol. 2006,1, 86–101.
51.
Yates, D.C.; Rodriguez-Villegas, E. A key power trade-off in wireless eeg headset design. In Proceedings of the 2007 3rd
International IEEE/EMBS Conference on Neural Engineering, Kohala Coast, HI, USA, 2–5 May 2007; pp. 453–456.
52.
Acharya, J.N.; Hani, A.J.; Thirumala, P.; Tsuchida, T.N. American clinical neurophysiology society guideline 3: A proposal for
standard montages to be used in clinical eeg. Neurodiagn. J. 2016,56, 253–260. [CrossRef] [PubMed]
53.
Wang, D.; Miao, D.; Blohm, G. Multi-class motor imagery EEG decoding for brain-computer interfaces. Front. Neurosci.
2012
,6, 151.
[CrossRef] [PubMed]
54.
Chen, Y.-H.; de Beeck, M.O.; Vanderheyden, L.; Mihajlovic, V.; Grundlehner, B.; Hoof, C.V. Comb-shaped polymer-based dry
electrodes for eeg/ecg measurements with high user comfort. In Proceedings of the 2013 35th Annual International Conference of
the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013; pp. 551–554.
55.
Kawana, T.; Yoshida, Y.; Kudo, Y.; Iwatani, C.; Miki, N. Design and characterization of an eeg-hat for reliable eeg measurements.
Micromachines 2020,11, 635. [CrossRef] [PubMed]
56.
Gorman, N.; Louw, A.; Craik, A.; Gonzalez, J.; Feng, J.; Contreras-Vidal, J.L. Design principles for mobile brain-body imaging
devices with optimized ergonomics. In Proceedings of the International Conference on Applied Human Factors and Ergonomics,
Virtually, 25–29 July 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 3–10.
57.
Gordon, C.C.; Blackwell, C.L.; Bradtmiller, B.; Parham, J.L.; Barrientos, P.; Paquette, S.P.; Corner, B.D.; Carson, J.M.; Venezia, J.C.;
Rockwell, B.M.; et al. 2012 Anthropometric Survey of US Army Personnel: Methods and Summary Statistics; Army Natick Soldier
Research Development and Engineering Center: Natick, MA, USA, 2014.
58. Meet Beagle™: Open Source Computing. Available online: https://beagleboard.org/ (accessed on 2 February 2023).
59.
Linx Labview Makerhub. Available online: https://www.labviewmakerhub.com/doku.php?id=learn:start (accessed on
2 February 2023).
60.
Nuwer, M.; Comi, G.; Emerson, R.; Fuglsang-Frederiksen, A.; Guerit, J.; Hinrichs, H.; Ikeda, A.; Luccas, F.; Rappelsburger, P. IFCN
standards for digital recording of clinical EEG. Electroencephalogr. Clin. Neurophysiol. 1998,106, 259–261. [CrossRef] [PubMed]
61.
Texas Instruments. ADS1299 Analog-to-Digital Converter for EEG and Biopotential Measurements; Texas Instruments: Dallas, TX,
USA, 2017.
62. TDK. ICM-20948 World’s Lowest Power 9-Axis MEMS MotionTracking™ Device; TDK InvenSense: San Jose, CA, USA, 2021.
63. Louis. Polymer Lithium-ion Battery Product Specification; AA Portable Power Corp: Richmond, CA, USA, 2010.
64. Octavo Systems LLC. OSD3358 Application Guide; Octavo Systems LLC: Austin, TX, USA, 2017.
65. Jenny, B.; Kelso, N.V. Color design for the color vision impaired. Cartogr. Perspect. 2007, 61–67. [CrossRef]
66.
Allan, J. Accessibility Requirements for People with Low Vision. 2016. Available online: https://www.w3.org/TR/low-vision-
needs/ (accessed on 2 February 2023).
67.
Zander, T.O.; Andreessen, L.M.; Berg, A.; Bleuel, M.; Pawlitzki, J.; Zawallich, L.; Krol, L.R.; Gramann, K. Evaluation of a dry
eeg system for application of passive brain-computer interfaces in autonomous driving. Front. Hum. Neurosci.
2017
,11, 78 .
[CrossRef]
68.
Bangor, A.; Kortum, P.; Miller, J. Determining what individual SUS scores mean: Adding an adjective rating scale. J. Usability
Stud. 2009,4, 114–123.
69. Hallett, M. Movement-related cortical potentials. Electromyogr. Clin. Neurophysiol. 1994,34, 5–13.

Sensors 2023,23, 5930 26 of 26
70.
Lu, M.; Arai, N.; Tsai, C.; Ziemann, U. Movement related cortical potentials of cued versus self-initiated movements: Double
dissociated modulation by dorsal premotor cortex versus supplementary motor area rTMS. Hum. Brain Mapp.
2012
,33, 824–839.
[CrossRef]
71.
Niazi, I.; Jiang, N.; Tiberghien, O.; Nielsen, J.; Dremstrup, K.; Farina, D. Detection of movement intention from single-trial
movement-related cortical potentials. J. Neural Eng. 2011,8, 066009. [CrossRef] [PubMed]
72.
Siemionow, V.; Yue, G.; Ranganathan, V.; Liu, J.; Sahgal, V. Relationship between motor activity-related cortical potential and
voluntary muscle activation. Exp. Brain Res. 2000,133, 303–311. [CrossRef] [PubMed]
73.
Shibasaki, H.; Barrett, G.; Halliday, E.; Halliday, A. Components of the movement-related cortical potential and their scalp
topography. Electroencephalogr. Clin. Neurophysiol. 1980,49, 213–226. [CrossRef] [PubMed]
74.
Shakeel, A.; Navid, M.; Anwar, M.; Mazhar, S.; Jochumsen, M.; Niazi, I. A review of techniques for detection of movement
intention using movement-related cortical potentials. Comput. Math. Methods Med. 2015,2015, 346217. [CrossRef]
75.
Duvinage, M.; Castermans, T.; Petieau, M.; Seetharaman, K.; Hoellinger, T.; Cheron, G.; Dutoit, T. A subjective assessment of a
p300 bci system for lower-limb rehabilitation purposes. In Proceedings of the 2012 Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, San Diego, CA, USA, 28 August–1 September 2012; pp. 3845–3849.
76.
Goncharova, I.; McFarl, ; D.; Vaughan, T.; Wolpaw, J. EMG contamination of EEG: Spectral and topographical characteristics. Clin.
Neurophysiol. 2003,114, 1580–1593. [CrossRef]
77.
Barry, R.; Clarke, A.; Johnstone, S.; Magee, C.; Rushby, J. EEG differences between eyes-closed and eyes-open resting conditions.
Clin. Neurophysiol. 2007,118, 2765–2773. [CrossRef]
78.
Kuhlman, W.N. Functional topography of the human mu rhythm. Electroencephalogr. Clin. Neurophysiol.
1978
,44, 83–93.
[CrossRef]
79. Müller, V.; Anokhin, A.P. Neural synchrony during response production and inhibition. PLoS ONE 2012,7, e38931. [CrossRef]
80.
Schoppenhorst, M.; Brauer, F.; Freund, G.; Kubicki, S. The significance of coherence estimates in determining central alpha and
mu activities. Electroencephalogr. Clin. Neurophysiol. 1980,48, 25–33. [CrossRef]
81.
Bhagat, N.A.; Venkatakrishnan, A.; Abibullaev, B.; Artz, E.J.; Yozbatiran, N.; Blank, A.A.; French, J.; Karmonik, C.; Grossman,
R.G.; O’Malley, M.K.; et al. Design and optimization of an eeg-based brain machine interface (bmi) to an upper-limb exoskeleton
for stroke survivors. Front. Neurosci. 2016,10, 122. [CrossRef]
Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.