Wearable EEG Research Articles

A 950 nW Analog-Based Data Reduction Chip for Wearable EEG Systems in Epilepsy

Long-term electroencephalogram (EEG) monitoring is an important tool used for the diagnosis of epilepsy. Truly Wearable EEG can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting miniature and wearable devices which can be easily worn by patients will result in more EEG data being acquired for longer monitoring periods. This paper presents an analog-based data reduction integrated circuit that would reduce the amount of power required to transmit EEG data by identifying the sections of data that are interesting for diagnostic purposes while discarding the background activity. Using the data reduction system as part of a miniature wireless, EEG monitoring unit would yield significant reductions in power consumption since the transmitter will only be switched ON based on the data reduction system output. A system prototype chip has been fabricated in a 0.35 $\mu \text{m}$ CMOS process. The system consumes 760 nA from a 1.25 V supply and is able to achieve a sensitivity of 87%, while transmitting 45% of the overall EEG data.

NeuroMonitor: a low‐power, wireless, wearable EEG device with DRL‐less AFE

Electroencephalography (EEG) is an effective tool to non-invasively capture brain responses. Traditional EEG analogue front end (AFE) requires a driven right leg (DRL) circuit that restricts the number of channels of the device. The authors are proposing a new `DRL-less' AFE design, and have developed a wearable EEG device (NeuroMonitor), which is small, low-power, wireless, and battery operated. The EEG device with two independent channels was fabricated on an 11.35 cm 2 PCB that contained a system-on-a-chip microcontroller, a low-noise instrument amplifier, a low-power Bluetooth module, a microSD, a microUSB, and a LiPo battery. The DRL circuit was eliminated by utilising the high CMRR instrument amplifier with differential inputs, and followed by a modified high-Q active Twin-T notch filter (( f c Notch = 60 Hz, -38 dB). The signal was conditioned with a band-pass filter composed of a two-stage 2nd-order Chebyshev-I Sallen-Key low-pass filter cascaded with a passive 2nd-order low-pass filter ( f c LP = 125 Hz) and a 1st-order passive high-pass filter ( f c HP = 0.5 Hz). Finally, the signal was amplified to achieve an overall gain of 55.84 dB, and digitised with a 16-bit delta-sigma ADC (256 sps). The prototype weighs 41.8 gm, and has been validated against a research-grade EEG system (Neuroscan).

Estimation of Human Workload from the Auditory Steady-State Response Recorded via a Wearable Electroencephalography System during Walking.

Workload in the human brain can be a useful marker of internal brain state. However, due to technical limitations, previous workload studies have been unable to record brain activity via conventional electroencephalography (EEG) and magnetoencephalography (MEG) devices in mobile participants. In this study, we used a wearable EEG system to estimate workload while participants walked in a naturalistic environment. Specifically, we used the auditory steady-state response (ASSR) which is an oscillatory brain activity evoked by repetitive auditory stimuli, as an estimation index of workload. Participants performed three types of N-back tasks, which were expected to command different workloads, while walking at a constant speed. We used a binaural 500 Hz pure tone with amplitude modulation at 40 Hz to evoke the ASSR. We found that the phase-locking index (PLI) of ASSR activity was significantly correlated with the degree of task difficulty, even for EEG data from few electrodes. Thus, ASSR appears to be an effective indicator of workload during walking in an ecologically valid environment.

Monitoring workers' attention and vigilance in construction activities through a wireless and wearable electroencephalography system

Abstract As a sector associated with high injury and fatality rates, the construction industry requires constant caution with regard to construction laborers during project execution. Different from people in other industries, construction workers are less sensitive to hazards because of their long-term exposure to risks. Therefore, maintaining construction workers' vigilance and monitoring their attention levels are critical to successful safety management practices. However, current attention-assessing approaches are post hoc and subjective and difficult to implement in construction practice. To address these issues, we propose a wireless and wearable electroencephalography (EEG) system to quantitatively and automatically assess construction workers' attention level through processing human brain signals. To validate the proposed system, we conducted an on-site experiment to analyze the EEG signal patterns when construction workers avoid different obstacles in their tasks. The results suggest EEG signal properties such as frequency, power spectrum density, and spatial distribution can effectively reflect and quantify the construction workers' perceived risk level. Especially, lower gamma frequency bands and the frontal left EEG cluster provide the most direct and observable indications of their vigilance states. These conclusions could facilitate the future implementation of wearable EEG devices through data filtering and channel optimization.

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG.

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

Demonstration of long-distance hazard-free wearable EEG monitoring system using mobile phone visible light communication.

A wearable electroencephalogram (EEG) is a small mobile device used for long-term brain monitoring systems. Applications of these systems include fatigue monitoring, mental/emotional monitoring, and brain-computer interfaces. However, the usage of wireless wearable EEG systems is limited due to the risks posed by the wireless RF communication radiation in a long-term exposure to the human brain. A novel microwave radiation-free system was developed by integrating visible light communication technology into a wearable EEG device. In this work, we investigated the system's performance in transmitting EEG data at different illuminance level and proposed an algorithm that functions at low illuminance levels for increased transmission distance. Using a 30 Hz smartphone camera, the proposed system was able to transmit 2.4 kbps of error-free EEG data up to 4 meter, which is equal to ~300 lux using an aspheric focus lens.

Active Electrodes for Wearable EEG Acquisition: Review and Electronics Design Methodology.

Active electrodes (AEs), i.e., electrodes with built-in readout circuitry, are increasingly being implemented in wearable healthcare and lifestyle applications due to AEs' robustness to environmental interference. An AE locally amplifies and buffers μV-level EEG signals before driving any cabling. The low output impedance of an AE mitigates cable motion artifacts, thus enabling the use of high-impedance dry electrodes for greater user comfort. However, developing a wearable EEG system, with medical grade signal quality on noise, electrode offset tolerance, common-mode rejection ratio, input impedance, and power dissipation, remains a challenging task. This paper reviews state-of-the-art bio-amplifier architectures and low-power analog circuits design techniques intended for wearable EEG acquisition, with a special focus on an AE system interfaced with dry electrodes.

Behavior State Analysis through Brain Computer Interface using Wearable EEG Devices: A Review

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG) based human computer interface in order to enhance the quality of life for medically as well as non-medical applications. Industry and community of research have been attracted by wireless EEG reading devices and they are easily available in the market. Such technology can be incorporated into psychology, anesthesiology, and for real-time patients monitoring. A brain computer interface (BCI) is a direct communication channel between the human brain and the digital computer. In this paper, we present a review on characteristics and specification of EEG-based human computer interfaces for real-time applications using wearable or wireless EEG devices.

Smart Wearable EEG Sensor

Currently, traditional and ambulatory EEG systems are beyond ideal for patients suffering from different brain diseases. Traditional monitoring of electrical activity in a diseased brain is limited to the clinical environment where patients being put away from natural environment in which provoking factors of abnormalities in the electrical activity of the brain are more likely to occur. Similarly, ambulatory EEG systems have a drawback of being cumbersome and impose some restrictions on the patient, such as not being able to show in public due to the social acceptability of wearing such a head-mounted device. The will of the patients to not publicizing their disorder or illnesses is a major drawback for current EEG system to be widely adopted. This paper presents an attempt to develop a wearable EEG prototype using off-the-shelf components to record EEG signal from the ear and display the obtained brain signals in the LabVIEW software. The developed prototype was able to record Ear-EEG in real-time.

Behaviour state analysis through brain computer interface using wearable EEG devices: a review

Behaviour state analysis through brain computer interface using wearable EEG devices: a review

Autonomous OA Removal in Real-Time from Single Channel EEG Data on a Wearable Device Using a Hybrid Algebraic-Wavelet Algorithm

Electroencephalography (EEG) is a non-invasive technique to record brain activities in natural settings. Ocular Artifacts (OA) usually contaminates EEG signals, removal of which is critical for accurate feature extraction and classification. With the increasing adoption of wearable technologies, single-channel real-time EEG systems that often require real-time signal processing for immediate real-time feedback are becoming more prevalent. However, traditional OA removal algorithms usually require multiple channels of EEG data, are computationally expensive, and do not perform well in real-time. In this article, a new hybrid algorithm is proposed that autonomously detects OA and subsequently removes OA from a single-channel steaming EEG data in real-time. The proposed single EEG channel algorithm also does not require additional reference electrooculography (EOG) channel. The algorithm has also been implemented on an embedded hardware platform of single channel wearable EEG system (NeuroMonitor). The algorithm first detects the OA zones using an Algebraic approach and then removes these artifacts from the detected OA zones using the Discrete Wavelet Transform (DWT) decomposition method. The de-noising technique is applied only to the OA zone, which minimizes loss of neural information outside the OA zone. A qualitative and quantitative performance evaluation was carried out with a 0.5s epoch in overlapping sliding window technique using time-frequency analysis, mean square coherence, and correlation coefficient statistics. The hybrid OA removal algorithm demonstrated real-time operation with 3s latency on the PSoC-3-microcontroller-based EEG system. Successful implementation of OA removal from single-channel real-time EEG data using the proposed algorithm shows promise for real-time feedback applications of wearable EEG devices.

3D Printed Dry EEG Electrodes.

Electroencephalography (EEG) is a procedure that records brain activity in a non-invasive manner. The cost and size of EEG devices has decreased in recent years, facilitating a growing interest in wearable EEG that can be used out-of-the-lab for a wide range of applications, from epilepsy diagnosis, to stroke rehabilitation, to Brain-Computer Interfaces (BCI). A major obstacle for these emerging applications is the wet electrodes, which are used as part of the EEG setup. These electrodes are attached to the human scalp using a conductive gel, which can be uncomfortable to the subject, causes skin irritation, and some gels have poor long-term stability. A solution to this problem is to use dry electrodes, which do not require conductive gel, but tend to have a higher noise floor. This paper presents a novel methodology for the design and manufacture of such dry electrodes. We manufacture the electrodes using low cost desktop 3D printers and off-the-shelf components for the first time. This allows quick and inexpensive electrode manufacturing and opens the possibility of creating electrodes that are customized for each individual user. Our 3D printed electrodes are compared against standard wet electrodes, and the performance of the proposed electrodes is suitable for BCI applications, despite the presence of additional noise.

Assessment of driving fatigue based on intra/inter-region phase synchronization

Driver fatigue has been under more attention as it is a main cause of traffic accidents. This paper proposed a method which utilized the inter/intra-region phase synchronization and functional units (FUs) to explore whether EEG synchronization changes from the alert state to the fatigue state. Mean phase coherence (MPC) is adopted as a measure for the phase synchronization. In order to find spatial-frequency features associated with mental state, we studied the intra/inter-region phase synchronization of EEG in different frequencies. The major finding is that EEG synchronizations in delta and alpha bands in frontal and parietal lobe are significantly increased as the mental state of the driver shifted from alertness to fatigue. This finding is simultaneously validated by NASA-Task Load Index (TLX) and Karolinska sleepiness scale (KSS). The statistical analysis results suggest MPC may be used to distinguish between alert and fatigue state of mind. In addition, the another contribution of the work indicates a simple and significant spatial-frequency pair of electrodes, i.e., Fz-Oz in delta band, to evaluate driver fatigue. It helps to implement real-world applications with wearable EEG equipment.

Trends in EEG-BCI for daily-life: Requirements for artifact removal

Abstract Since the discovery of the EEG principles by Berger in the 20’s, procedures for artifact removal have been essential in its pre-processing. In literature, diverse approaches based on signal processing, data mining, statistic models, and others compile information from multiple electrodes to build filters for artifact removal in the time, frequency or space domains. For almost one century, EEG acquisitions have required strict experimental conditions that included an isolated room, clinical acquisition systems, rigorous experimental protocols and very precise stimulation control. Under these steady experimental conditions, artifact removal techniques have not significantly evolved since then. However, in the last decade technological advances in brain-computer interfaces permit EEG acquisition by means of wireless, mobile, dry, wearable, and low-cost EEG headsets, with new potential daily-life applications, such as in entertainment or industry. New aspects not considered before, such as massive muscular and electrical artifacts, reduced number of electrodes, uncontrolled concomitant stimulus or the need for online processing are now essential. In this paper, we present a critical review of EEG artifact removal approaches, discuss their applicability to daily-life EEG-BCI applications, and give some directions and guidelines for upcoming research in this topic. Based on the results of the review, existing artifact removal techniques need further evolution to be applied in daily-life EEG-BCI. The use of multiple-step procedures is recommended, combining source decomposition with blind source separation and adaptive filtering, rather than using them separately. It is also recommendable to define and characterize most of artifacts evoked in daily-life EEG-BCI for a more effective removal.

Wearable EEG via lossless compression.

This work presents a wearable multi-channel EEG recording system featuring a lossless compression algorithm. The algorithm, based in a previously reported algorithm by the authors, exploits the existing temporal correlation between samples at different sampling times, and the spatial correlation between different electrodes across the scalp. The low-power platform is able to compress, by a factor between 2.3 and 3.6, up to 300sps from 64 channels with a power consumption of 176μW/ch. The performance of the algorithm compares favorably with the best compression rates reported up to date in the literature.

High performance wearable two-channel hybrid BCI system with eye closure assist.

Generally, eye closure (EC) and eye opening (EO)-based alpha blocking has widely recognized advantages, such as being easy to use, requiring little user training, while motor imagery (MI) is difficult for some users to have concrete feelings. This study presents a hybrid brain-computer interface (BCI) combining MI and EC strategies - such an approach aims to overcome some disadvantages of MI-based BCI, improve the performance and universality of the BCI. The EC/EO is employed to control the machine to switch in different states including forward, stop, changing direction motions, while the MI is used to control the machine to turn left or right for 90° by imagining the hands grasp motions when the system is switched into "changing direction" state. Additionally, a wearable two-channel EEG device is utilized in order to increase the efficiency of EEG processing and improving the practical utility. Results show that proposed hybrid system can generate four control commands with the average accuracy of 87.72%, which is higher than only using MI. Besides, it is possible to reach the same good accuracy using two-channel EEG as with usual multi-channel EEG.

Design and validation of a wearable "DRL-less" EEG using a novel fully-reconfigurable architecture.

The conventional EEG system consists of a driven-right-leg (DRL) circuit, which prohibits modularization of the system. We propose a Lego-like connectable fully reconfigurable architecture of wearable EEG that can be easily customized and deployed at naturalistic settings for collecting neurological data. We have designed a novel Analog Front End (AFE) that eliminates the need for DRL while maintaining a comparable signal quality of EEG. We have prototyped this AFE for a single channel EEG, referred to as Smart Sensing Node (SSN), that senses brain signals and sends it to a Command Control Node (CCN) via an I2C bus. The AFE of each SSN (referential-montage) consists of an off-the-shelf instrumentation amplifier (gain=26), an active notch filter fc = 60Hz), 2nd-order active Butterworth low-pass filter followed by a passive low pass filter (fc = 47.5 Hz, gain = 1.61) and a passive high pass filter fc = 0.16 Hz, gain = 0.83). The filtered signals are digitized using a low-power microcontroller (MSP430F5528) with a 12-bit ADC at 512 sps, and transmitted to the CCN every 1 s at a bus rate of 100 kbps. The CCN can further transmit this data wirelessly using Bluetooth to the paired computer at a baud rate of 115.2 kbps. We have compared temporal and frequency-domain EEG signals of our system with a research-grade EEG. Results show that the proposed reconfigurable EEG captures comparable signals, and is thus promising for practical routine neurological monitoring in non-clinical settings where a flexible number of EEG channels are needed.

Performance analysis of a DRL-less AFE for battery-powered wearable EEG

The driven-right-leg (DRL) circuit has been commonly used in the wall-powered EEG systems to reduce common-mode interference in the bio-potential amplifier. However, DRL circuit imposes limitations on the number of channels preventing modular development, and its effectiveness is diminished for a newer generation of battery-powered EEG systems. We present a performance investigation of DRL-less EEG circuit by designing a single-channel EEG with a novel Analog Front End (AFE) that contains a differential amplifier followed by a high-Q active notch filter. The prototyped wearable EEG system has been validated to record neural signals with and without the DRL circuit. The time domain and frequency domain signals show that the designed AFE is not impacted significantly (maximum 4dB difference) by the DRL elimination and maintains similar signal quality. The customized EEG with and without DRL offers CMRR of 72.98dB and 71.74dB, respectively, at 60Hz (power-line interference range in the USA), whereas CMRR of 72.64dB and 71.01dB, respectively, at 20Hz (representative EEG signal range). DRL elimination allows us to envision a sensor-level modular EEG system for neural monitoring in non-clinical environments.

Towards continuous and real-time attention monitoring at work: reaction time versus brain response

Continuous and objective measurement of the user attention state still represents a major challenge in the ergonomics research. Recently available wearable electroencephalography (EEG) opens new opportunities for objective and continuous evaluation of operators’ attention, which may provide a new paradigm in ergonomics. In this study, wearable EEG was recorded during simulated assembly operation, with the aim to analyse P300 event-related potential component, which provides reliable information on attention processing. In parallel, reaction times (RTs) were recorded and the correlation between these two attention-related modalities was investigated. Negative correlation between P300 amplitudes and RTs has been observed on the group level (p < .001). However, on the individual level, the obtained correlations were not consistent. As a result, we propose the P300 amplitude for accurate attention monitoring in ergonomics research. On the other hand, no significant correlation between RTs and P300 latency was found on group, neither on individual level.Practitioner Summary: Ergonomic studies of assembly operations mainly investigated physical aspects, while mental states of the assemblers were not sufficiently addressed. Presented study aims at attention tracking, using realistic workplace replica. It is shown that drops in attention could be successfully traced only by direct brainwave observation, using wireless electroencephalographic measurements.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver’s vigilance level . Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.