Current implications of EEG and fNIRS as functional neuroimaging techniques for motor recovery after stroke

Persistent motor deficits are highly prevalent among post-stroke survivors, contributing significantly to disability. Despite the prevalence of these deficits, the precise mechanisms underlying motor recovery after stroke remain largely elusive. The exploration of motor system reorganization using functional neuroimaging techniques represents a compelling yet challenging avenue of research. Quantitative electroencephalography (qEEG) parameters, including the power ratio index, brain symmetry index, and phase synchrony index, have emerged as potential prognostic markers for overall motor recovery post-stroke. Current evidence suggests a correlation between qEEG parameters and functional motor outcomes in stroke recovery. However, accurately identifying the source activity poses a challenge, prompting the integration of EEG with other neuroimaging modalities, such as functional near-infrared spectroscopy (fNIRS). fNIRS is nowadays widely employed to investigate brain function, revealing disruptions in the functional motor network induced by stroke. Combining these two methods, referred to as integrated fNIRS-EEG, neural activity and hemodynamics signals can be pooled out and offer new types of neurovascular coupling-related features, which may be more accurate than the individual modality alone. By harnessing integrated fNIRS-EEG source localization, brain connectivity analysis could be applied to characterize cortical reorganization associated with stroke, providing valuable insights into the assessment and treatment of post-stroke motor recovery.

Evaluation of consumer-grade wireless EEG systems for brain-computer interface applications

With the growing popularity of consumer-grade electroencephalogram (EEG) devices for health, entertainment, and cognitive research, assessing their signal quality is essential. In this study, we evaluated four consumer-grade wireless and dry-electrode EEG systems widely used for brain-computer interface (BCI) research and applications, comparing them with a research-grade system. We designed an EEG phantom method that reproduced µV-level amplitude EEG signals and evaluated the five devices based on their spectral responses, temporal patterns of event-related potential (ERP), and spectral patterns of resting-state EEG. We discovered that the consumer-grade devices had limited bandwidth compared with the research-grade device. A late component (e.g., P300) was detectable in the consumer-grade devices, but the overall ERP temporal pattern was distorted. Only one device showed an ERP temporal pattern comparable to that of the research-grade device. On the other hand, we confirmed that the activation of the alpha rhythm was observable in all devices. The results provide valuable insights for researchers and developers when it comes to selecting suitable EEG devices for BCI research and applications.

Recent progress on smart lower prosthetic limbs: a comprehensive review on using EEG and fNIRS devices in rehabilitation

The global rise in lower limb amputation cases necessitates advancements in prosthetic limb technology to enhance the quality of life for affected patients. This review paper explores recent advancements in the integration of EEG and fNIRS modalities for smart lower prosthetic limbs for rehabilitation applications. The paper synthesizes current research progress, focusing on the synergy between brain-computer interfaces and neuroimaging technologies to enhance the functionality and user experience of lower limb prosthetics. The review discusses the potential of EEG and fNIRS in decoding neural signals, enabling more intuitive and responsive control of prosthetic devices. Additionally, the paper highlights the challenges, innovations, and prospects associated with the incorporation of these neurotechnologies in the field of rehabilitation. The insights provided in this review contribute to a deeper understanding of the evolving landscape of smart lower prosthetic limbs and pave the way for more effective and user-friendly solutions in the realm of neurorehabilitation.

Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial-temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area.

A Multi-channel Eye-Mask-based Wearable System for Accurate Eye Movement Recognition and Saccadic Angle Estimation

Advanced wearable systems can accurately capture eye movement patterns, providing users with precise, comfortable, and reliable HCI solutions. In this paper, a Multichannel Eye-mask-based Wearable (MEW) system that offers a portable, high-sensitive eye movement recognition (EMR) and saccadic angle estimation (SAE) via the combination of multi-channel eye-mask electrodes, high-precision EOG acquisition system, and automatic EMR and SAE algorithms is proposed. The multi-channel eye-mask electrodes provide high-spatial-resolution EOG sensing, and the high-precision EOG acquisition system leverages an STM32 microcontroller and a BLE transceiver for wireless EOG data transmission. The proposed system was validated with EOG signals collected from 20 subjects over two days, during which they performed 8-directional EMR and SAE tasks. An average accuracy of 87.2% can be achieved on the EMR task. Besides, the mean absolute error (MAE) and root mean square error (RMSE) for the SAE task are 4.19°and 5.27°, indicating its effectiveness in capturing and decoding multiple eye movements. Overall, the proposed MEW system offers a reliable, portable, and high-sensitive solution in eye movement directions and angle tracking, which is also expected to be easily employed in practical scenarios for EOG-based HCI applications.

Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces

Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.

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.

Evolution of Bioamplifiers: From Vacuum Tubes to Highly Integrated Analog Front-Ends

The past century has seen the ongoing development of amplifiers for different electrophysiological signals to study the work of the heart. Since the vacuum tube era, engineers and designers of bioamplifiers for recording electrophysiological signals have been trying to achieve similar objectives: increasing the input impedance and common-mode rejection ratio, as well as reducing power consumption and the size of the bioamplifier. This review traces the evolution of bioamplifiers, starting from circuits on vacuum tubes and discrete transistors through circuits on operational and instrumental amplifiers, and to combined analog-digital solutions on analog front-end integrated circuits. Examples of circuits and their technical features are provided for each stage of the bioamplifier development. Special emphasis is placed on the review of modern analog front-end solutions for biopotential registration, including their generalized structural diagram and table of comparative characteristics. A detailed review of analog front-end circuit integration in various practical applications is provided, with examples of the latest achievements in the field of electrocardiogram, electroencephalogram, and electromyogram registration. The review concludes with key points and insights for the future development of the analog front-end concept applied to bioelectric signal registration.

Immersive media experience: a survey of existing methods and tools for human influential factors assessment

Virtual reality (VR) applications, especially those where the user is untethered to a computer, are becoming more prevalent as new hardware is developed, computational power and artificial intelligence algorithms are available, and wireless communication networks are becoming more reliable, fast, and providing higher reliability. In fact, recent projections show that by 2022 the number of VR users will double, suggesting the sector was not negatively affected by the worldwide COVID-19 pandemic. The success of any immersive communication system is heavily dependent on the user experience it delivers, thus now more than ever has it become crucial to develop reliable models of immersive media experience (IMEx). In this paper, we survey the literature for existing methods and tools to assess human influential factors (HIFs) related to IMEx. In particular, subjective, behavioural, and psycho-physiological methods are covered. We describe tools available to monitor these HIFs, including the user's sense of presence and immersion, cybersickness, and mental/affective states, as well as their role in overall experience. Special focus is placed on psycho-physiological methods, as it was found that such in-depth evaluation was lacking from the existing literature. We conclude by touching on emerging applications involving multiple-sensorial immersive media and provide suggestions for future research directions to fill existing gaps. It is hoped that this survey will be useful for researchers interested in building new immersive (adaptive) applications that maximize user experience.

Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System

The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals' features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study's findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface.

Development of an Integrated EEG/fNIRS Brain Function Monitoring System

In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is based on the ADS1298IPAG Analog Front-End (AFE) and can simultaneously acquire two-channel EEG signals with a sampling rate of 250 SPS and six-channel fNIRS signals with a sampling rate of 8 SPS. AFE is controlled by Teensy 3.2 and powered by a lithium polymer battery connected to two protection circuits and regulators. The acquired EEG and fNIRS signals are monitored and stored using a Graphical User Interface (GUI). The system was evaluated by implementing several tests to verify its ability to simultaneously acquire EEG and fNIRS signals. The implemented system can acquire EEG and fNIRS signals with a CMRR of -115 dB, power consumption of 0.75 mW/ch, system weight of 70.5 g, probe weight of 3.1 g, and a total cost of USD 130. The results proved that this system can be qualified as a low-cost, light-weight, low-power-consumption, and fully integrated EEG/fNIRS brain monitoring system.

Wearable, Integrated EEG-fNIRS Technologies: A Review

There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG–fNIRS can provide a comprehensive picture of brain electrical and hemodynamic function and has been applied across various fields of brain science. The development of wearable, mechanically and electrically integrated EEG–fNIRS technology is a critical next step in the evolution of this field. A suitable system design could significantly increase the data/image quality, the wearability, patient/subject comfort, and capability for long-term monitoring. Here, we present a concise, yet comprehensive, review of the progress that has been made toward achieving a wearable, integrated EEG–fNIRS system. Significant marks of progress include the development of both discrete component-based and microchip-based EEG–fNIRS technologies; modular systems; miniaturized, lightweight form factors; wireless capabilities; and shared analogue-to-digital converter (ADC) architecture between fNIRS and EEG data acquisitions. In describing the attributes, advantages, and disadvantages of current technologies, this review aims to provide a roadmap toward the next generation of wearable, integrated EEG–fNIRS systems.

Review of semi-dry electrodes for EEG recording

Developing reliable and user-friendly electroencephalography (EEG) electrodes remains a challenge for emerging real-world EEG applications. Classic wet electrodes are the gold standard for recording EEG; however, they are difficult to implement and make users uncomfortable, thus severely restricting their widespread application in real-life scenarios. An alternative is dry electrodes, which do not require conductive gels or skin preparation. Despite their quick setup and improved user-friendliness, dry electrodes still have some inherent problems (invasive, relatively poor signal quality, or sensitivity to motion artifacts), which limit their practical utilization. In recent years, semi-dry electrodes, which require only a small amount of electrolyte fluid, have been successfully developed, combining the advantages of both wet and dry electrodes while addressing their respective drawbacks. Semi-dry electrodes can collect reliable EEG signals comparable to wet electrodes. Moreover, their setup is as fast and convenient similar to that of dry electrodes. Hence, semi-dry electrodes have shown tremendous application prospects for real-world EEG acquisition. Herein, we systematically summarize the development, evaluation methods, and practical design considerations of semi-dry electrodes. Some feasible suggestions and new ideas for the development of semi-dry electrodes have been presented. This review provides valuable technical support for the development of semi-dry electrodes toward emerging practical applications.

Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using EEG

With the recent surge of affordable, high-performance virtual reality (VR) headsets, there is unlimited potential for applications ranging from education, to training, to entertainment, to fitness and beyond. As these interfaces continue to evolve, passive user-state monitoring can play a key role in expanding the immersive VR experience, and tracking activity for user well-being. By recording physiological signals such as the electroencephalogram (EEG) during use of a VR device, the user's interactions in the virtual environment could be adapted in real-time based on the user's cognitive state. Current VR headsets provide a logical, convenient, and unobtrusive framework for mounting EEG sensors. The present study evaluates the feasibility of passively monitoring cognitive workload via EEG while performing a classical n-back task in an interactive VR environment. Data were collected from 15 participants and the spatio-spectral EEG features were analyzed with respect to task performance. The results indicate that scalp measurements of electrical activity can effectively discriminate three workload levels, even after suppression of a co-varying high-frequency activity.

Two-Wired Active Spring-Loaded Dry Electrodes for EEG Measurements

Dry contact electrode-based EEG acquisition is one of the easiest ways to obtain neural information from the human brain, providing many advantages such as rapid installation, and enhanced wearability. However, high contact impedance due to insufficient electrical coupling at the electrode-scalp interface still remains a critical issue. In this paper, a two-wired active dry electrode system is proposed by combining finger-shaped spring-loaded probes and active buffer circuits. The shrinkable probes and bootstrap topology-based buffer circuitry provide reliable electrical coupling with an uneven and hairy scalp and effective input impedance conversion along with low input capacitance. Through analysis of the equivalent circuit model, the proposed electrode was carefully designed by employing off-the-shelf discrete components and a low-noise zero-drift amplifier. Several electrical evaluations such as noise spectral density measurements and input capacitance estimation were performed together with simple experiments for alpha rhythm detection. The experimental results showed that the proposed electrode is capable of clear detection for the alpha rhythm activation, with excellent electrical characteristics such as low-noise of 1.131 μV_RMS and 32.3% reduction of input capacitance.