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Owing to the large inter-subject variability, early post-stroke prognosis is challenging, and objective biomarkers that can provide further prognostic information are still needed. The relation between quantitative EEG parameters in pre-thrombolysis hyper-acute phase and outcomes has still to be investigated. Hence, possible correlations between early EEG biomarkers, measured on bedside wireless EEG, and short-term/long-term functional and morphological outcomes were investigated in thrombolysis-treated strokes. EEG with a wireless device was performed in 20 patients with hyper-acute (

Objective This study characterizes the current capabilities of seizure detection device (SDD) technology and evaluates the fitness of these devices for use in anti-seizure medication (ASM) clinical trials. Methods Through a systematic literature review, 36 wireless SDDs featured in published device validation studies were identified. Each device’s seizure detection capabilities that addressed ASM clinical trial primary endpoint measurement needs were cataloged. Results The two most common types of seizures targeted by ASMs in clinical trials are generalized tonic-clonic (GTC) seizures and focal with impaired awareness (FIA) seizures. The Brain Sentinel SPEAC achieved the highest performance for the detection of GTC seizures (F1-score = 0.95). A non-commercial wireless EEG device achieved the highest performance for the detection of FIA seizures (F1-score = 0.88). Discussion A preliminary assessment of device capabilities for measuring selected ASM clinical trial secondary endpoints was performed. The need to address key limitations in validation studies is highlighted in order to support future assessments of SDD fitness for ASM clinical trial use. In tandem, a stepwise framework to streamline device testing is put forth. These suggestions provide a starting point for establishing SDD reporting requirements before device integration into ASM clinical trials.

Brain electrical activity in acute ischemic stroke is related to the hypoperfusion of cerebral tissue as manifestation of neurovascular coupling. EEG could be applicable for bedside functional monitoring in emergency settings. We aimed to investigate the relation between hyper-acute ischemic stroke EEG changes, measured with bedside wireless-EEG, and hypoperfused core-penumbra CT-perfusion (CTP) volumes. In addition, we investigated the association of EEG and CTP parameters with neurological deficit measured by NIHSS. We analyzed and processed EEG, CTP and clinical data of 31 anterior acute ischemic stroke patients registered within 4.5 h from symptom onset. Delta/alpha ratio (DAR), (delta + theta)/(alpha + beta) ratio (DTABR) and relative delta power correlated directly (ρ = 0.72; 0.63; 0.65, respectively), while alpha correlated inversely (ρ = − 0.66) with total hypoperfused volume. DAR, DTBAR and relative delta and alpha parameters also correlated with ischemic core volume (ρ = 0.55; 0.50; 0.59; − 0.51, respectively). The same EEG parameters and CTP volumes showed significant relation with NIHSS at admission. The multivariate stepwise regression showed that DAR was the strongest predictor of NIHSS at admission (p < 0.001). The results of this study showed that hyper-acute alterations of EEG parameters are highly related to the extent of hypoperfused tissue highlighting the value of quantitative EEG as a possible complementary tool in the evaluation of stroke severity and its potential role in acute ischemic stroke monitoring.

Disable people who lost their limb have difficulty to perform their normal activities. Most assistive devices for disabled people require the use of upper limb or arm to control the devices. A wireless brain-controlled device can help the disable people who are limbless or paralyzed to be independent. This paper describes the wireless neuro-based fan that uses intention via EEG signals to control the fan speed by transmitting the EEG and control signals through Bluetooth. The EEG signals were acquired and processed in real time. The intention which is in the form of eyeblink was distinguished from the EEG signals using peak detection and the information was then used to control the fan speed levels. The results demonstrated that the combination of neuro-based and wireless technology enable the fan to be controlled successfully without the use of hands and allow the user to move comfortably.

Recent advances in dry electrodes technology have facilitated the recording of EEG in situations not previously possible, thanks to the relatively swift electrode preparation and avoidance of applying gel to subject's hair. However, to become a true alternative, these systems should be compared to state-of-the-art wet EEG systems commonly used in clinical or research applications. In our study, we conducted a systematic comparison of electrodes application speed, subject comfort, and most critically electrophysiological signal quality between the conventional and wired Biosemi EEG system using wet active electrodes and the compact and wireless F1 EEG system consisting of dry passive electrodes. All subjects (n = 27) participated in two recording sessions on separate days, one with the wet EEG system and one with the dry EEG system, in which the session order was counterbalanced across subjects. In each session, we recorded their EEG during separate rest periods with eyes open and closed followed by two versions of a serial visual presentation target detection task. Each task component allows for a neural measure reflecting different characteristics of the data, including spectral power in canonical low frequency bands, event-related potential components (specifically, the P3b), and single trial classification based on machine learning. The performance across the two systems was similar in most measures, including the P3b amplitude and topography, as well as low frequency (theta, alpha, and beta) spectral power at rest. Both EEG systems performed well above chance in the classification analysis, with a marginal advantage of the wet system over the dry. Critically, all aforementioned electrophysiological metrics showed significant positive correlations (r = 0.54–0.89) between the two EEG systems. This multitude of measures provides a comprehensive comparison that captures different aspects of EEG data, including temporal precision, frequency domain as well as multivariate patterns of activity. Taken together, our results indicate that the dry EEG system used in this experiment can effectively record electrophysiological measures commonly used across the research and clinical contexts with comparable quality to the conventional wet EEG system.

Background and Objective: The ever-increasing fatality rate due to traffic and workplace accidents, resulting from drowsiness have been a persistent concern during the past years. An efficient technology capable of monitoring and detecting drowsiness can help to alleviate this concern and has potential applications in driver vigilance monitoring, vigilance monitoring in air traffic control rooms and other safety critical work places. In this paper, we present the feasibility of a wearable light weight wireless consumer grade Electroencephalogram (EEG)-based drowsiness detection. Methods: A set of informative features were extracted from short daytime nap EEG signals and their applicability in discriminating between alert and drowsy state was studied. We derived an optimal set of EEG features, that give maximum detection rate for the drowsy state. In addition, heart rate was also recorded concurrently with EEG and correlation between heart rate and the EEG features corresponding to drowsiness was also studied. Results: Using the selected features, the EEG data is shown to be capable of classifying alert and drowsy states with an accuracy of 78.3% using Support Vector Machine classifier employing cross subject validation. The feature selection results also revealed that, the EEG features extracted from the temporal electrodes are more significant for drowsiness detection than the features from frontal electrodes. In addition, EEG features extracted from the temporal electrodes yielded higher correlation coefficient with heart rate, which was in concordance with the feature selection results. Conclusions: The results reveal that using the proposed drowsiness detection algorithm, it is possible to perform drowsiness detection using a single EEG electrode placed behind the ear.

In this paper, the design and implementation of wireless data transmission and low power consumption of a low cost electroencephalogram (EEG) to monitor the brainwave signals for medical and non-medical applications is presented. It can be used in smart city applications such as for brain-computer interface in industrial and transportation applications or intelligent wireless wearable EEG solutions for daily life applications. The wireless EEG wearable system is designed, simulated, constructed and tested to monitor the brainwave signals. The Zigbee module was used to construct the wireless data transmission system. The experimental results show that the proposed EEG system was successfully developed and tested. Its total coast is cheaper compared to the commercial EEG system.

The results of three different groups of customers, each exposed to a different sales technique, were compared in order to evaluate which sales technique was more effective for achieving higher levels of sustained attention. The study used the Neurosky headband to measure brainwaves in millivolts. The three compared sales techniques were: 1) the training technique for sale, termed as structured by the AIDA model; 2) the traditional sale, here called unstructured; 3) and a structured technique with the help of simultaneous stimulation by several neuroattentional pathways, prescribed by the developments of the neuroscience of consumption (CN). The results showed a statistically significant difference in achieved sustained attention levels, presenting higher levels in clients exposed to the sales training technique and ower scores in those exposed to one of the techniques used by the CN. These results are discussed in relation to the concept of sustained attention in clients when the stimulation of a single neuroattentional pathway is used as compared to the activation of several pathways simultaneously. Resumen Con el objetivo de conocer qué tipo de técnica de venta es más efectiva en lograr mayores niveles de atención sostenida, se compararon los resultados obtenidos por tres grupos de clientes expuestos, cada uno, a una técnica de venta de un mismo producto. Para el efecto se recurrió a la medición en milivoltios de ondas cerebrales de atención a través de diademas Neurosky. Las tres técnicas de venta comparadas fueron: 1) la técnica de entrenamiento en venta, estructurada según el modelo AIDA; 2) la venta tradicional, denominada aquí como no estructurada; 3) y una técnica estructurada con ayuda de estimulación simultánea por varias vías neuroatencionales, prescrita por los desarrollos de la neurociencia del consumo (NC). Los resultados mostraron una diferencia estadísticamente significativa en los niveles de atención sostenida alcanzados, presentando mayores niveles en los clientes expuestos a la técnica de entrenamiento en venta y menores puntajes en aquellos expuestos a una de las técnicas usadas por la NC. Estos resultados se discuten en relación con el concepto de atención sostenida en los clientes, cuando se recurre a la estimulación de una vía neuroatencional en comparación con la activación de varias vías simultáneamente.

The authors present wearable noninvasive electronics that prevent a human from falling. It deducts a probable fall from EEG and EMG information and provides a real-time alarm signal for protection.

Multi-user electroencephalogram (EEG) system is necessary to study concurrent activity among many persons. It is difficult to find a system that measures multiple EEG signals from more than even three people simultaneously. Therefore, we suggested a framework that is able to acquire EEG signals of more than eight persons at the same time and investigated the feasibility of this system. Acquisition was performed by using OpenViBE software developed by INRIA. Wireless EEG devices for our proposed framework were manufactured by BioBrain, Corp. in Korea. A device consists of eight channels measuring frontal EEG at a speed of 1 KHz sampling rate. While participants wore this system and did emotional video watching task as a group audience, their brain signals were acquired. To show its feasibility and efficacy, our preliminary result is analyzed using deep learning technique.

Several companies are developing an EEG recording system that uses dry, wireless electrodes, and one has recently been approved by the Food and Drug Administration (FDA) for medical use. Today, the quality of EEG signals recorded by these dry systems may not always match those of conventionally applied electrodes, but the gap is closing fast. As dry, wireless EEG systems find their way into hospitals and clinics over the next few years, the job of the EEG technologist will change. Dry systems can be placed on like a cap and, after a few minutes of adjustment, begin broadcasting EEG. No longer will it be as necessary to spend time meticulously measuring, cleaning, affixing, and adjusting electrodes on the scalp. This upcoming time of rapid change can be an opportunity to redefine the role of the EEG technologist.

Objective The study compared two channel-reduction approaches in order to investigate the effects of systematic motor imagery (MI) neurofeedback practice in an everyday environment using a very user-friendly EEG system consisting of individualized caps and highly portable hardware. Methods Sixteen BCI novices were trained over four consecutive days to imagine left and right hand movements while receiving feedback. The most informative bipolar channels for use on the subsequent days were identified on the first day for each individual based on a high-density online MI recording. Results Online classification accuracy on the first day was 85.1% on average (range: 64.7–97.7%). Offline an individually-selected bipolar channel pair based on common spatial patterns significantly outperformed a pair informed by independent component analysis and a standard 10–20 pair. From day 2 to day 4 online MI accuracy increased significantly (day 2: 69.1%; day 4: 73.3%), which was mostly caused by a reduction in ipsilateral event-related desynchronization of sensorimotor rhythms. Conclusion The present study demonstrates that systematic MI practice in an everyday environment with a user-friendly EEG system results in MI learning effects. Significance These findings help to bridge the gap between elaborate laboratory studies with healthy participants and efficient home or hospital based MI neurofeedback protocols.

This paper presents recent updates of performed and undergoing ergonomic studies inspired by observed and analyzed industrial case studies. Ergonomic problems and issues from two workplaces at automotive industry were identified and analyzed through their replication in laboratory conditions and implementation of multimodal approach for using of selected neuroergonomic, psychophysiology and Human–Computer Interface (HCI) tools and methods. Initial results shows significant potential for real-time and continuous monitoring and assessment of various parameters that could serve as objective identifications of operators' mental and physical load in operational environment.

Este trabalho mostra o desenvolvimento de um sistema de condicionamento de sinais EEG sem fio onde os mesmos são tratados por meio de processamento digital de sinais usando o software LabVIEW para a aplicação específica de acionamento de um cooler que controla a altura de uma bola de isopor que está dentro de um tubo plástico transparente de acordo com o tipo do sinal alfa e beta que corresponde, respectivamente, ao estado de relaxamento e concentração de um indivíduo. A comunicação sem fio das informações do hardware biomédico torna a aquisição de dados mais portátil à pessoa que fará uso de tal dispositivo. Obteve-se um ganho total da placa de condicionamento de sinais EEG de 11326,36 [V/V] para sinais aplicados a uma frequência de 20 Hz e a comunicação sem fio dos dados foi obtida com êxito através de dois módulos de rádio frequência NRF24L01. O protótipo para a aplicação deste sistema de condicionamento de sinais EEG sem fio controlou perfeitamente a aplicação, ou seja, a altura da bola de isopor. E validou-se o sistema utilizando um gerador de onda arbitrária da Agilent 30MHz 32521A que simularam sinais biológicos para a faixa de frequências de 0,1 e 40 Hz.

This Electroencephalogram (EEG) has been widely used to capture brain signals for affective recognition through computational modelling of affective states based on valence, arousal and dominance. Not until recently wireless EEG machines are introduced to record brain signals during physical activities and high range of mobility. Nevertheless, such devices are expensive. Therefore, the aim of this study is to design a mobile EEG systems using an existing stationary EEG amplifier, EXG Brainmarker. The main component for the proposed mobile EEG system are the electrodes cap connected to EXG Brainmarker which will be powered up using portable rechargeable battery, Surface tablet to record and store EEG signals, Portable Wi-Fi device as a medium for remotely connection to monitor the Surface tablet. The proposed EEG mobile system was tested in a Simulation Centre at Akademi Laut Malaysia (ALAM). The data was obtained from three marine pilot who were given tasks to navigate ships in the simulated scenes. Findings and observations from the experiment are reported in this paper. It was also observed that there are several common movements took place when maneuvering the ship such as lift up their hand, turn their head to left and right and a few steps walking.

To build a low-cost, small, and wireless electroencephalogram (EEG) system suitable for field recordings, we merged consumer EEG hardware with an EEG electrode cap. Auditory oddball data were obtained while participants walked outdoors on university campus. Single-trial P300 classification with linear discriminant analysis revealed high classification accuracies for both indoor (77%) and outdoor (69%) recording conditions. We conclude that good quality, single-trial EEG data suitable for mobile brain-computer interfaces can be obtained with affordable hardware.

The brain Electroencephalographic (EEG) of 32 healthy right-handed college students (mean age = 20.6, SD = 1.6, 16 males) was measured before and during a 20 min in situ sitting exposure to nature and that to an urban built environment, using a portable wireless EEG recording device. Results revealed higher EEG correlations during nature exposure than that to built environment, while no significant differences in their baselines.

Background Electroencephalography (EEG) is a widely used neuroimaging technique with applications in healthcare, research, assessment, treatment, and neurorehabilitation. Conventional EEG systems require extensive setup time, expensive equipment, and expertise to utilize and therefore are often limited to clinical or laboratory settings. Technological advancements have made it possible to develop wireless EEG systems with dry electrodes to reduce many of these barriers. However, due to the lack of homogeneity in hardware, electrode evaluation, and methodological procedures the clinical acceptance of these systems has been limited. Methods In this investigation the validity of a wireless dry electrode system compared to a conventional wet electrode system was assessed, while addressing methodological limitations. In Experiment 1, the signal output of both EEG systems was examined at Fz, C3, Cz, C4, and Pz using a conductive head model and generated test signals at 2.5 Hz, 10 Hz, and 39 Hz. In Experiment 2, two-minutes of eyes-closed and eyes-open EEG data was recorded simultaneously with both devices from the adjacent electrode sites in a sample of healthy adults. Results Between group effects and frequency*device and electrode*device interactions were assessed using a mixed ANOVA for the simulated and in vivo signal output, producing no significant effects . Bivariate correlation coefficients were calculated to assess the relationship between electrode pairs during the simultaneous in vivo recordings, indicating a significant positive relationship (all p's ± 0.5) between the dry and wet electrode signal amplitude were observed for theta, alpha, beta 1, beta 2, beta 3, and gamma in both the eyes-closed and eyes-open conditions. Conclusions This report demonstrates preliminary but compelling evidence that EEG data recorded from the wireless dry electrode system is comparable to data recorded from a conventional system. Small correlation values in delta activity were discussed in relation to minor differences in hardware filter settings, variation in electrode placement, and participant artifacts observer during the simultaneous EEG recordings. Study limitations and impact of this research on neurorehabilitation were discussed.

A novel non-contact controller of the electric bed for medical healthcare was proposed in this study. Nowadays, the electric beds are widely used for hospitals and home-care, and the conventional control method of the electric beds usually involves in the manual operation. However, it is more difficult for the disabled and bedridden patients, who might totally depend on others, to operate the conventional electric beds by themselves. Different from the current controlling method, the proposed system provides a new concept of controlling the electric bed via visual stimuli, without manual operation. The disabled patients could operate the electric bed by focusing on the control icons of a visual stimulus tablet in the proposed system. Besides, a wearable and wireless EEG acquisition module was also implemented to monitor the EEG signals of patients. The experimental results showed that the proposed system successfully measured and extracted the EEG features related to visual stimuli, and the disabled patients could operate the adjustable function of the electric bed by themselves to effectively reduce the long-term care burden.

Traditional EEG systems are limited when utilized in point-of-care applications due to its immobility and tedious preparation procedures. We are designing a novel device named single-unit wireless EEG sensor to solve these problems. The sensor has a size similar to a U.S. penny. Four electrodes are installed within a 20mm diameter cylinder. It can be applied to scalp in seconds to amplify, digitize and wirelessly transmit EEG. Before the design and construction of an actual sensor, in this paper, we perform a set of simulations to quantitatively study: 1) can the sensor acquire EEG reliably? 2) will the selection of sensor orientation be an important factor to influence signal strength? Our results demonstrate positive answers to these questions. Moreover, the signal sensor acquired appears to be comparable to the signal from the standard 10–20 system. These results warrant the further design and construction of a single-unit wireless EEG sensor.

Background: A number of benefits have been described for the long-term practice of meditation, yet little is known regarding the immediate neurological and cardiovascular responses to meditation. Wireless sensor technology allows, for the first time, multi-parameter and quantitative monitoring of an individual’s responses during meditation. The present study examined inter-individual variations to meditation through continuous monitoring of EEG, blood pressure, heart rate and its variability (HRV) in novice and experienced meditators. Methods: Participants were 20 experienced and 20 novice meditators involved in a week-long wellness retreat. Monitoring took place during meditation sessions on the first and last full days of the retreat. All participants wore a patch that continuously streamed ECG data, while half of them also wore a wireless EEG headset plus a non-invasive continuous blood pressure monitor. Results: Meditation produced variable but characteristic EEG changes, significantly different from baseline, even among novice meditators on the first day. In addition, although participants were predominately normotensive, the mean arterial blood pressure fell a small (2-3 mmHg) but significant (p

The emergence of inexpensive off-the-shelf wireless EEG devices led researchers to explore novel paradigms in the field of Human Computer Interaction. In fact, the compliance of these devices with the IoT principles towards pervasive EEG signaling in smart home environments enables new models of interaction and a different perspective from traditional affective computing. In this paper, the implementation of wireless EEG (Emotiv EPOC and Mindawave) IoT connectivity of real time raw signals, through IoT hardware devices and through the Raspberry Pi 2, is presented.

A WLAN interface for a Wireless EEG System is presented in this paper. Selection of broadcasting band, avail-able hardware, and connection algorithm to use are discussed before making a choice. Two alternatives were explored: Wireless EEG Device (Holter) and its Server communicate with each other within the same physical network, and from a complex network like the Internet. Results of experimental tests carried out on the prototype demonstrate the functionality of the implemented interface.

This paper describes the design of a wireless Electroencephalograph (EEG) system using a new generation of high resolution Analog Front End circuits that minimize signal acquisition hardware and are aimed at simplifying recordings from patients by not requiring them to be tied down with current bulky recording hardware. Such a modality opens up EEG monitoring to more ambulatory themed studies, and further solidifies the future of EEG for diagnostic applications by providing a way for investigators to easily record from active subjects and be less intrusive while doing so. Design was validated with Alpha Band monitoring when eyes are open vs. closed and observing a 40dB increases in 12Hz RMS power when eyes are closed.

Monitoring the neurophysiological activities of driver in an operational environment poses a severe measurement challenge using a current laboratory-oriented biosensor technology. The aims of this research are to 1) introduce a dry and wireless EEG system used for conveniently recording EEG signals from forehead regions, 2) propose an effective system for processing EEG recordings and translating them into the vigilance level, and 3) implement the proposed system with a JAVA-based graphical user interface (GUI) for online analysis. To validate the performance of the proposed system, this study recruited eight voluntary subjects to participate a 90-min sustained-attention driving task in a virtual-realistic driving environment. Physiological evidence obtained from the power spectral analysis showed that the dry EEG system could distinguish an alert EEG from a drowsy EEG by evaluating the spectral dynamics of delta and alpha activities. Furthermore, the experimental result of the comparison of the prediction performance using four forehead electrode sites (AF8, FP2, FP1, and AF7) implied that a single-electrode EEG signal used in the mobile and wireless EEG system is able to obtain a high prediction accuracy (~93%). Taken together, the proposed system applied a dry-EEG device combined with an effective algorithm can be a promising technology for real driving applications.

This study aims to develop a wireless EEG system to provide critical point-of-care information about brain electrical activity. A novel dry electrode, which can be installed rapidly, is used to acquire EEG from the scalp. A wireless data link between the electrode and a data port (i.e., a smartphone) is established based on the Bluetooth technology. A prototype of this system has been implemented and its performance in acquiring EEG has been evaluated.

Development of Wireless EEG system is described. Realtime impedance monitoring and active electrodes are introduced in order to reduce noise from impedance changes caused due to body motion, and to prevent noise from power line interference, respectively. EEG ASICs are developed for the system. The complete system has a low noise (60nV/√Hz) and is packaged in a compact enclosure (38mm × 38mm × 16mm). The system is evaluated against different types of artefacts and possible applications with the system are discussed.

In this paper, we present a miniaturized (

Background We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs). Methods The microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the EEG laboratory, and studies recorded from patients in the ED or ICU were also used for comparison. In one experiment, a signal splitter was used to record simultaneous microEEG and standard EEG from the same electrodes. Results EEG signal analysis techniques indicated good agreement between microEEG and the standard system in 66 EEGs recorded in the EEG laboratory and the ED. In the simultaneous recording the microEEG and standard system signals differed only in a smaller amount of 60 Hz noise in the microEEG signal. In a blinded review by a board-certified clinical neurophysiologist, differences in technical quality or interpretability were insignificant between standard recordings in the EEG laboratory and microEEG recordings from standard or electrode cap electrodes in the ED or EEG laboratory. The microEEG data recording characteristics such as analog-to-digital conversion resolution (16 bits), input impedance (>100MΩ), and common-mode rejection ratio (85 dB) are similar to those of commercially available systems, although the microEEG is many times smaller (88 g and 9.4 × 4.4 × 3.8 cm). Conclusions Our results suggest that the technical qualities of microEEG are non-inferior to a standard commercially available EEG recording device. EEG in the ED is an unmet medical need due to space and time constraints, high levels of ambient electrical noise, and the cost of 24/7 EEG technologist availability. This study suggests that using microEEG with an electrode cap that can be applied easily and quickly can surmount these obstacles without compromising technical quality.

Evaluation of the loss of consciousness in poultry is an essential component in evaluating bird welfare under a variety of situations and applications. Many current approaches to evaluating loss of consciousness are qualitative and require observation of the bird. This study outlines a quantitative method for determining the point at which a bird loses consciousness. In this study, commercial broilers were individually anesthetized and the brain activity recorded as the bird became unconscious. A wireless EEG transmitter was surgically implanted and the bird anesthetized after a 24–48 h recovery. Each bird was monitored during treatment with isoflurane anesthesia and EEG data was evaluated using a frequency based approach. The alpha/delta ( A / D ) ratio and loss of posture (LOP) were used to determine the point at which the birds went unconscious. There was no statistically significant difference between time to unconsciousness as measured by A / D ratio or LOP.

The development and realization of a portable wireless electroencephalogram (EEG) monitor has become feasible due to the recent active research activities in the area of Wireless Sensor Networks (WSN). In this paper, we propose an EEG monitor design based on a commercially available WSN device, Body Sensor Networks node (BSN node). The device utilizes Zigbee technology and operates on the TinyOS operating system. The EEG signal from subjects is detected and radioed to a PC for monitoring. This study validates our approach and suggests the feasibility of future enhancements.

This paper presents a complete non-invasive Wireless acquisition system based on dry electrodes for electroencephalograms (WiDE-EEG) with emphasis in the electronic system design. The WiDE-EEG is composed by a 2.4 GHz radio-frequency (RF) transceiver, biopotential acquisition electronics and dry electrodes. The WiDE-EEG can acquire electroencephalogram (EEG) signals from 5 unipolar channels, with a resolution of 16 bits and minimum analog amplitude of 9.98 μV(pp), at a sampling rate of 1000 samples/s/channel and sends them to a processing unit through RF in a 10 m range. The analog channels were optimized for EEG signals (with amplitudes in the range 70-100 μV) and present the following characteristics: a signal gain of 66 dB and a common mode rejection ratio of 56.5 dB. Each electrode is composed by 16 microtip structures that were fabricated through bulk micromachining of a100-type silicon substrate in a potassium hydroxide (KOH) solution. The microtips present solid angles of 54.7°, a height of 100-200 μm and 2 μm spaced apart. The electrodes have a thin layer (obtained by sputtering) of iridium oxide (IrO) to guaranty their biocompatibility and improve the contact with the skin. These dry electrodes are in direct contact with the electrolyte fluids of the inner skin layers, and avoid the use of conductive gels. The complete WiDE-EEG occupies a volume of 9 cm×8.5 cm×1 cm, which makes it suitable for true mobility of the subjects and at the same time allows high data transfer rates. Since the WiDE-EEG is battery-powered, it overcomes the need of galvanic isolation for ensuring patient safety observed on conventional EEG instrumentation systems. The WiDE-EEG presents a total power consumption of 107 mW, divided as follows: the acquisition system contributes with 10 mW per channel, whereas the commercial MICAz module contributes with 57 mW (e.g., 24 mW from the microcontroller and 33 mW from the RF chip). The WiDE-EEG also presents autonomy of about 25 h with two class AA 1.5 V batteries.

Most researchers acquired EEG by using standard measurement system like NeuroScan system, which includes AgCl electrode cap, SynAmps Amplifier and Scan software to provide good reliability for the acquisition of EEG data. However, it is still not convenient for Brain Computer Interface (BCI) application in daily life because of needing conduction gels to contact skins and being wired, expensive and heavy. Moreover, the conduction gel will trend to be drying, so it does not suitable for long-term monitoring. In this study, we developed a mobile and wireless EEG system. The system consists of frond-end 16-channel dry electrode cap, a miniature low-power wireless portable circuitry, and a back-end program receiving events and digital EEG data simultaneously. We demonstrate the recorded EEG data have high correlations between from our system and from NeuroScan system.

Recent advances in low-power wireless technologies for health are instrumental in bringing EEG monitoring from the hospital to the home environment. This talk provides an overview of imec's research on low-power wireless EEG monitoring. Enabling technologies, integrated systems and remaining challenges are discussed.

Due to the recent active research activities in the area of Wireless Sensor Networks (WSN), the development and realization of a portable wireless electroencephalogram (EEG) monitor has become feasible. In this paper, we propose an EEG monitor design based on a commercially available WSN device, Body Sensor Networks node (BSN node). The device utilizes Zigbee technology and operates on TinyOS operating system. The EEG signal from subjects is detected and radioed to a PC for monitoring. This study validates our approach and suggests the feasibility of future enhancements.

Electroencephalography (EEG) can be used as an indicator of fatigue. Several studies have shown that slow wave brain activities, delta (0-4 Hz) and theta (4- 8 Hz), increase as an individual becomes fatigued, while the fast brain activities, alpha (8-13 Hz) and beta (13-35 Hz), decrease. However, an EEG is a complex piece of equipment that is generally used in laboratory based studies. In order to develop a fatigue countermeasure device for train drivers using EEG, there is a need for a simple and wireless EEG monitor. This paper explains the development of a single channel wireless EEG device.

This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload.

With the continued miniaturisation of portable embedded systems, wireless EEG recording techniques are becoming increasingly prevalent in animal behavioural research. However, in spite of their versatility and portability, they have seldom been used inside water-maze tasks designed for rats. As such, a novel 3D printed implant and waterproof connector is presented, which can facilitate wireless water-maze EEG recordings in freely-moving rats, using a commercial wireless recording system (W32; Multichannel Systems). As well as waterproofing the wireless system, battery, and electrode connector, the implant serves to reduce movement-related artefacts by redistributing movement-related forces away from the electrode connector. This implant/connector was able to successfully record high-quality LFP in the hippocampo-striatal brain regions of rats as they undertook a procedural-learning variant of the double-H water-maze task. Notably, there were no significant performance deficits through its use when compared with a control group across a number of metrics including number of errors and speed of task completion. Taken together, this method can expand the range of measurements that are currently possible in this diverse area of behavioural neuroscience, whilst paving the way for integration with more complex behaviours.

Most hospitals in the world do not perform electroencephalograms in the emergency departments due to space, cost, training, and complexity of the equipment and the test. New miniature, low-cost, simple, digital, wireless EEG machines have been developed that solve all four of these inhibiting factors to allow EEG, to be used in emergency departments to evaluate patients presenting with altered mental status for nonconvulsive seizures. Four-channel wireless EEG used in the ED is feasible, provides good quality screening EEGs, and was able to diagnose underlying seizures or slowing in a significant number of patients.

This paper presents the design and fabrication of a radio-frequency (RF) transceiver fabricated in a UMC RF 0.18 mum CMOS process. The RF transceiver was built to operate at the 2.4 GHz ISM band. The receiver has a sensibility of -60 dBm and consumes 6.3 mW from a 1.8 V supply. The transmitter delivers an output power of 0 dBm with a power consumption of 11.2 mW. The application is a wireless wearable electroencephalogram (EEG) braincap. Wireless EEG allows patients to wear the brain cap and maintain their mobility while simultaneously having their electrical brain activity monitored. A solution of an individual EEG module composed by an electrode, processing electronics and an antenna, allows a plug-and-play electrodes solution.

Economic decision making refers to the process of individuals translating their preference into subjective value (SV). Little is known about the dynamics of the neural processes that underpin this form of value-based decision making and no studies have investigated these processes outside of controlled laboratory settings. The current study investigated the spatio-temporal dynamics that accompany economic valuation of products using mobile electroencephalography (EEG) and eye tracking techniques. Participants viewed and rated images of household products in a gallery setting while EEG and eye tracking data were collected wirelessly. A Becker-DeGroot-Marschak (BDM) auction task was subsequently used to quantify the individual's willingness to pay (WTP) for each product. WTP was used to classify products into low, low medium, high medium and high economic value conditions. Eye movement related potentials (EMRP) were examined, and independent component analysis (ICA) was used to separate sources of activity from grand averaged EEG data. Four independent components (ICs) of EMRPs were modulated by WTP (i.e., SV) in the latency range of 150-250 ms. Of the four value-sensitive ICs, one IC displayed enhanced amplitude for all value conditions excluding low value, and another IC presented enhanced amplitude for low value products only. The remaining two value-sensitive ICs resolved inter-mediate levels of SV. Our study quantified, for the first time, the neural processes involved in economic value based decisions in a natural setting. Results suggest that multiple spatio-temporal brain activation patterns mediate the attention and aversion of products which could reflect an early valuation system. The EMRP parietal P200 component could reflect an attention allocation mechanism that separates the lowest-value products (IC7) from products of all other value (IC4), suggesting that low-value items are categorized early on as being aversive. While none of the ICs showed linear amplitude changes that parallel SV's of products, results suggest that a combination of multiple components may sub-serve a fine-grained resolution of the SV of products.