Validation and Benchmarking of a Wearable EEG Acquisition Platform for Real-World Applications

A portable EEG signal acquisition system and a limited-electrode channel classification network for SSVEP

Brain-computer interfaces (BCIs) have garnered significant research attention, yet their complexity has hindered widespread adoption in daily life. Most current electroencephalography (EEG) systems rely on wet electrodes and numerous electrodes to enhance signal quality, making them impractical for everyday use. Portable and wearable devices offer a promising solution, but the limited number of electrodes in specific regions can lead to missing channels and reduced BCI performance. To overcome these challenges and enable better integration of BCI systems with external devices, this study developed an EEG signal acquisition platform (Gaitech BCI) based on the Robot Operating System (ROS) using a 10-channel dry electrode EEG device. Additionally, a multi-scale channel attention selection network based on the Squeeze-and-Excitation (SE) module (SEMSCS) is proposed to improve the classification performance of portable BCI devices with limited channels. Steady-state visual evoked potential (SSVEP) data were collected using the developed BCI system to evaluate both the system and network performance. Offline data from ten subjects were analyzed using within-subject and cross-subject experiments, along with ablation studies. The results demonstrated that the SEMSCS model achieved better classification performance than the comparative reference model, even with a limited number of channels. Additionally, the implementation of online experiments offers a rational solution for controlling external devices via BCI.

Design and implementation of high sampling rate and multichannel wireless recorder for EEG monitoring and SSVEP response detection

Introduction

The collection and process of human brain activity signals play an essential role in developing brain-computer interface (BCI) systems. A portable electroencephalogram (EEG) device has become an important tool for monitoring brain activity and diagnosing mental diseases. However, the miniaturization, portability, and scalability of EEG recorder are the current bottleneck in the research and application of BCI.

Methods

For scalp EEG and other applications, the current study designs a 32-channel EEG recorder with a sampling rate up to 30 kHz and 16-bit accuracy, which can meet both the demands of scalp and intracranial EEG signal recording. A fully integrated electrophysiology microchip RHS2116 controlled by FPGA is employed to build the EEG recorder, and the design meets the requirements of high sampling rate, high transmission rate and channel extensive.

Results

The experimental results show that the developed EEG recorder provides a maximum 30 kHz sampling rate and 58 Mbps wireless transmission rate. The electrophysiological experiments were performed on scalp and intracranial EEG collection. An inflatable helmet with adjustable contact impedance was designed, and the pressurization can improve the SNR by approximately 4 times, the average accuracy of steady-state visual evoked potential (SSVEP) was 93.12%. Animal experiments were also performed on rats, and spike activity was captured successfully.

Conclusion

The designed multichannel wireless EEG collection system is simple and comfort, the helmet-EEG recorder can capture the bioelectric signals without noticeable interference, and it has high measurement performance and great potential for practical application in BCI systems.

Objective assessment of impulse control disorder in patients with Parkinson's disease using a low-cost LEGO-like EEG headset: a feasibility study

Background

Patients with Parkinson’s disease (PD) can develop impulse control disorders (ICDs) while undergoing a pharmacological treatment for motor control dysfunctions with a dopamine agonist (DA). Conventional clinical interviews or questionnaires can be biased and may not accurately diagnose at the early stage. A wearable electroencephalogram (EEG)-sensing headset paired with an examination procedure can be a potential user-friendly method to explore ICD-related signatures that can detect its early signs and progression by reflecting brain activity.

Methods

A stereotypical Go/NoGo test that targets impulse inhibition was performed on 59 individuals, including healthy controls, patients with PD, and patients with PD diagnosed by ICDs. We conducted two Go/NoGo sessions before and after the DA-pharmacological treatment for the PD and ICD groups. A low-cost LEGO-like EEG headset was used to record concurrent EEG signals. Then, we used the event-related potential (ERP) analytical framework to explore ICD-related EEG abnormalities after DA treatment.

Results

After the DA treatment, only the ICD-diagnosed PD patients made more behavioral errors and tended to exhibit the deterioration for the NoGo N2 and P3 peak amplitudes at fronto-central electrodes in contrast to the HC and PD groups. Particularly, the extent of the diminished NoGo-N2 amplitude was prone to be modulated by the ICD scores at Fz with marginal statistical significance (r = − 0.34, p = 0.07).

Conclusions

The low-cost LEGO-like EEG headset successfully captured ERP waveforms and objectively assessed ICD in patients with PD undergoing DA treatment. This objective neuro-evidence could provide complementary information to conventional clinical scales used to diagnose ICD adverse effects.

Custom-Fitted In- and Around-the-Ear Sensors for Unobtrusive and On-the-Go EEG Acquisitions: Development and Validation

OBJECTIVES

This paper aims to validate the performance and physical design of a wearable, unobtrusive ear-centered electroencephalography (EEG) device, dubbed "EARtrodes", using early and late auditory evoked responses. Results would also offer a proof-of-concept for the device to be used as a concealed brain-computer interface (BCI).

DESIGN

The device is composed of a custom-fitted earpiece and an ergonomic behind-the-ear piece with embedded electrodes made of a soft and flexible combination of silicone rubber and carbon fibers. The location of the conductive silicone electrodes inside the ear canal and the optimal geometry of the behind-the-ear piece were obtained through morphological and geometrical analysis of the human ear canal and the region around-the-ear. An entirely conductive generic earpiece was also developed to assess the potential of a universal, more affordable solution.

RESULTS

Early latency results illustrate the conductive silicone electrodes' capability to record quality EEG signals, comparable to those obtained with traditional gold-plated electrodes. Additionally, late latency results demonstrate EARtrodes' capacity to reliably detect decision-making processes from the ear.

CONCLUSIONS

EEG results validate the performance of EARtrodes as a circum-aural and intra-aural EEG recording system adapted for a wide range of applications in audiology, neuroscience, clinical research, and as an unobtrusive BCI.

Validating a LEGO-Like EEG Headset for a Simultaneous Recording of Wet- and Dry-Electrode Systems During Treadmill Walking

Recent mobile and wearable electroencephalogram (EEG)-sensing technologies have been demonstrated to be effective for measuring rapid changes of spatio-spectral EEG correlates of brain and cognitive functions of interest with more ecologically natural settings. However, commercial EEG products are available commonly with a fixed headset in terms of the number of electrodes and their locations to the scalp practically constrains their generalizability for different demands of EEG and brain-computer interface (BCI) study. While most progress focused on innovation of sensing hardware and conductive electrodes, less effort has been done to renovate mechanical structures of an EEG headset. Recently, an electrode-holder assembly infrastructure was designed to be capable of unlimitedly (re)assembling a desired n-channel electrode headset through a set of primary elements (i.e., LEGO-like headset). The present work empirically demonstrated one of its advantage regarding coordinating the homogeneous or heterogeneous sensors covering the target regions of the brain. Towards this objective, an 8-channel LEGO headset was assembled to conduct a simultaneous event-related potential (ERP) recording of the wet- and dry-electrode EEG systems and testify their signal quality during standing still versus treadmill walking. The results showed that both systems returned a comparable P300 signal-to-noise ratio (SNR) for standing, yet the dry system was more susceptible to the movement artifacts during slow walking. The LEGO headset infrastructure facilitates a desired benchmark study, e.g., comparing the signal quality of different electrodes on non-stationary subjects conducted in this work, or a specific EEG and BCI application.

The Effect That Auditory Distractions Have on a Visual P300 Speller While Utilizing Low-Cost Off-the-Shelf Equipment

This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed at categorizing distractions in the P300b domain and the effect thereof. This work is part of a larger electroencephalography based project and is based on the P300 speller brain–computer interface (oddball) paradigm and the xDAWN algorithm, with eight to ten healthy subjects, using a non-invasive brain–computer interface based on low-fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the lab condition (LC) at 100%, followed by music at 90% (M90) at 98%, trailed by music at 30% (M30) and music at 60% (M60) equally at 96%, and shadowed by ambient noise (AN) at 92.5%, passive talking (PT) at 90%, and finally by active listening (AL) at 87.5%. The subjects’ preference prodigiously shows that the preferred condition was LC as originally expected, followed by M90, M60, AN, M30, AL, and PT. Statistical analysis between all independent variables shows that we accept our null hypothesis for both the amplitude and latency. This work includes data and comparisons from our previous papers. These additional results should give some insight into the practicability of the aforementioned P300 speller methodology and equipment to be used for real-world applications.

Cost-efficient and Custom Electrode-holder Assembly Infrastructure for EEG Recordings

Mobile electroencephalogram (EEG)-sensing technologies have rapidly progressed and made the access of neuroelectrical brain activity outside the laboratory in everyday life more realistic. However, most existing EEG headsets exhibit a fixed design, whereby its immobile montage in terms of electrode density and coverage inevitably poses a great challenge with applicability and generalizability to the fundamental study and application of the brain-computer interface (BCI). In this study, a cost-efficient, custom EEG-electrode holder infrastructure was designed through the assembly of primary components, including the sensor-positioning ring, inter-ring bridge, and bridge shield. It allows a user to (re)assemble a compact holder grid to accommodate a desired number of electrodes only to the regions of interest of the brain and iteratively adapt it to a given head size for optimal electrode-scalp contact and signal quality. This study empirically demonstrated its easy-to-fabricate nature by a low-end fused deposition modeling (FDM) 3D printer and proved its practicability of capturing event-related potential (ERP) and steady-state visual-evoked potential (SSVEP) signatures over 15 subjects. This paper highlights the possibilities for a cost-efficient electrode-holder assembly infrastructure with replaceable montage, flexibly retrofitted in an unlimited fashion, for an individual for distinctive fundamental EEG studies and BCI applications.

Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning

Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners' brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.