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Background Delirium-related biochemical derangements lead to electrical changes that can be detected in electroencephalographic (EEG) patterns followed by behavioral signs and symptoms. Studies using limited lead EEG show a large difference between patients with and without delirium while discriminating delirium from other causes. Handheld rapid EEG devices may be capable of detecting delirium before symptom onset, thus providing an objective physiological method to detect delirium when it is most amenable to interventions. Objective The aim of this study was to explore the potential for rapid EEG to detect waveform pattern changes consistent with delirium status. Methods This prospective exploratory pilot study used a correlational design and mixed models to explore the relationships between handheld portable EEG data and delirium status. Results While being under powered minimized opportunities to detect statistical differences in EEG-derived ratios using spectral density analysis, sleep-to-wake ratios tended to be higher in patients with delirium. Conclusions Limited lead EEG may be useful in predicting adverse outcomes and risk for delirium in older critically ill patients. Although this population is at the highest risk for mortality, delirium is not easily identified by current clinical assessments. Therefore, further investigation of limited lead EEG for delirium detection is warranted.

Given the reduced ability of people with dementia to self-report pain, this study examined the feasibility of using a portable electroencephalography (EEG) headband (MUSE 2) as a pain measurement tool for long-term care residents with dementia. Ten minutes of resting-state EEG was acquired by MUSE 2 from people with dementia experiencing ongoing pain (n = 3) and without current pain (n = 1) over three days. The MUSE 2 was acceptable and feasible for use in people with dementia while challenges regarding software, data collection and analysis in using this device are reported. Compared to the resident not experiencing pain, EEG signals of residents with ongoing pain showed different EEG patterns, and this could be a potential biomarker to support pain measurement in people with dementia. Further research with larger sample size is warranted to verify study results.

Absence seizures are generalized nonmotor epileptic seizures with abrupt onset and termination. Transient impairment of consciousness and spike-slow wave discharges (SWDs) in EEG are their characteristic manifestations. This type of seizure is severe in two common pediatric syndromes: childhood (CAE) and juvenile (JAE) absence epilepsy. The appearance of low-cost, portable EEG devices has paved the way for long-term, remote monitoring of CAE and JAE patients. The potential benefits of this kind of monitoring include facilitating diagnosis, personalized drug titration, and determining the duration of pharmacotherapy. Herein, we present a novel absence detection algorithm based on the properties of the complex Morlet continuous wavelet transform of SWDs. We used a dataset containing EEGs from 64 patients (37 h of recordings with almost 400 seizures) and 30 age and sex-matched controls (9 h of recordings) for development and testing. For seizures lasting longer than 2 s, the detector, which analyzed two bipolar EEG channels (Fp1-T3 and Fp2-T4), achieved a sensitivity of 97.6% with 0.7/h detection rate. In the patients, all false detections were associated with epileptiform discharges, which did not yield clinical manifestations. When the duration threshold was raised to 3 s, the false detection rate fell to 0.5/h. The overlap of automatically detected seizures with the actual seizures was equal to ~96%. For EEG recordings sampled at 250 Hz, the one-channel processing speed for midrange smartphones running Android 10 (about 0.2 s per 1 min of EEG) was high enough for real-time seizure detection.

Researchers, parents and educators consistently observe a stark mismatch between biologically preferred and socially imposed sleep–wake hours in adolescents, fueling debate about high school start times. We contribute neural evidence to this debate with electroencephalogram data collected from high school students during their regular morning, mid-morning and afternoon classes. Overall, student alpha power was lower when class content was taught via videos than through lectures. Students’ resting state alpha brain activity decreased as the day progressed, consistent with adolescents being least attentive early in the morning. During the lessons, students showed consistently worse performance and higher alpha power for early morning classes than for mid-morning classes, while afternoon quiz scores and alpha levels varied. Together, our findings demonstrate that both class activity and class time are reflected in adolescents’ brain states in a real-world setting, and corroborate educational research suggesting that mid-morning may be the best time to learn.

ObjectiveElectroencephalography (EEG) has become very common in clinical practice due to its relatively low cost, ease of installation, non-invasiveness, and good temporal resolution. Portable EEG devices are increasingly popular in clinical monitoring applications such as sleep scoring or anesthesia monitoring. In these situations, for reasons of speed and simplicity only few electrodes are used and contamination of the EEG signal by artifacts is inevitable. Visual inspection and manual removal of artifacts is often not possible, especially in real-time applications. Our goal is to develop a flexible technique to remove EEG artifacts in these contexts with minimal supervision.MethodsWe propose here a new wavelet-based method which allows to remove artifacts from single-channel EEGs. The method is based on a data-driven renormalization of the wavelet components and is capable of adaptively attenuate artifacts of different nature. We benchmark our method against alternative artifact removal techniques.ResultsWe assessed the performance of the proposed method on publicly available datasets comprising ocular, muscular, and movement artifacts. The proposed method shows superior performances on different kinds of artifacts and signal-to-noise levels. Finally, we present an application of our method to the monitoring of general anesthesia.ConclusionsWe show that our method can successfully attenuate various types of artifacts in single-channel EEG.SignificanceThanks to its data-driven approach and low computational cost, the proposed method provides a valuable tool to remove artifacts in real-time EEG applications with few electrodes, such as monitoring in special care units.

Summary Objective Changes in EEG patterns during stroke are almost immediate; however, a full EEG test takes time and requires highly qualified staff. In this study, we examined whether a short recording using a portable EEG device can differentiate between a stroke and control group. Methods EEG samples were collected from patients with an acute ischemic stroke event. The control group comprised healthy volunteers. EEG recordings were recorded using a portable brain wave sensor device. The Revised Brain Symmetry Index (rsBSI) was used to quantify the symmetry of spectral power between the two hemispheres. Results The investigation group included 33 patients (ages 46–96, mean age 72 years, 66% male) who were diagnosed with ischemic stroke. The control group included 25 healthy individuals. Scores for the rsBSI of non-stroke patients (M = 0.1686, SD = 0.10) differed significantly from those of ischemic stroke patients (P Conclusions A statistically significant difference was observed between a group of stroke patients and a matched group of healthy controls with a short recording using a portable EEG device.

Global refugee crisis around the world has displaced millions of people from their homes. Although some of them adjust well, many suffer from significant psychological distress, such as post-traumatic stress disorder (PTSD), owing to exposure to traumatic events and hardships. Here, diagnosis and access to psychological health care present particular challenges for various human-centered design issues. Therefore, analyzing the case of Rohingya refugees in Bangladesh, we propose a two-way diagnosis of PTSD using (i) short inexpensive questionnaire to determine its prevalence, and (ii) low-cost portable EEG headset to identify potential neurobiological markers of PTSD. To the best of our knowledge, this study is the first to use consumer-grade EEG devices in the scarce-resource settings of refugees. Moreover, we explored the underlying structure of PTSD and its symptoms via developing various hybrid models based on Bayesian inference by combining aspects from both reflective and formative models of PTSD, which is also the first of its kind. Our findings revealed several key components of PTSD and its neurobiological abnormality. Moreover, challenges faced during our study would inform design processes of screening tools and treatments of PTSD to incorporate refugee experience in a more meaningful way during contemporary and future humanitarian crisis.

The increased need to perform a different control adapted to various areas such as: rehabilitation, entertainment, smart toys have created a new demand for small sized low-cost electroencephalogram (EEG) acquisition devices. Also, there is a need to develop portable EEG devices that can be used for long term, without the need for further operations such as the short-time replacement of signal capture electrodes or frequent rehydration in the case of semiconductor polymers-based sensors. In this paper, we propose the use of an OpenBCI neural headset whose structure has been designed and 3D printed, equipped with 16 reusable electroencephalogram electrodes that provide 16 channels of acquisition of EEG signals from the user to control a Lego NXT MindStorms 2.0 wheeled platform programmed in C# that accepts brain control signals as input.

Elderly people suffering from diseases like Parkinson’s disease will have difficulty in movement. For such patients, to analyse their health conditions and for diagnosis purpose, doctors may need their EEG. At present, extraction of these signals needs a short hospital visit. In this work, a simple portable brain computer interface using embedded system is developed to help such elderly people at home who may have difficulty in traveling to hospitals. The BCI system developed here utilizes EEG electrodes for signal extraction as well as high gain amplifier assembly, Raspberry Pi 4 for processing and decoding of the brain waves. The proposed system was simulated using Proteus software, fabricated on printed circuit board and interfaced with Raspberry Pi4 board. Google Colab software was used for processing the EEG dataset while integrating with Raspberry Pi4 using python coding These systems will be more practical and effective for usage outside the laboratory.

Brain computer interface (BCI) technology is an alternate communication option for individuals with neuromuscular impairments. There are several challenges to optimal BCI use including positioning of the screen, ease of use, independence in access, and calibration. Our study is directed at the development of a practical, accessible, at-home use BCI system that addresses these obstacles. Our design utilizes an augmented reality (AR) head-mounted display as a solution to provide BCI stimuli and output. We used a battery-operated Bluetooth-connected 8-channel portable EEG system and a custom P300 selection matrix displaying icons corresponding to various home control actions. Finally, the BCI system is integrated with a built-in smart assistant (Google Assistant) which allows the user to control their environment. In this paper, we describe the engineering of this home-use BCI system designed specifically for people with Amyotrophic Lateral Sclerosis, a neuromuscular disease causing severe motor deficits and loss of mobility.

Parkinson’s disease is a common and highly threatening neurodegenerative disease, which has no confirmed well-adopted diagnosis method to date. All research efforts in this regard focus on diagnosing in controlled environment such as laboratories. Going beyond this common limitation, in this paper, we exploit the Electroencephalogram (EEG) signal to effectively come up with a portable Parkinson’s disease diagnosis system that can go in-the-wild. To do so, we collect EEG signals from both Parkinson’s patients and healthy people in different emotional states. Our statistical analysis finds a substantial differences in the signals in several emotional states for Parkinson’s patients and healthy people. Accordingly, we develop a machine learning based model to diagnose Parkinson’s disease that results in 97% accuracy.

In this study, we demonstrated the use of low-cost portable electroencephalography (EEG) as a method for prehospital stroke diagnosis. We used a portable EEG system to record data from 25 participants, 16 had acute ischemic stroke events, and compared the results to age-matched controls that included stroke mimics. Delta/alpha ratio (DAR), (delta + theta)/(alpha + beta) ratio (DBATR) and pairwise-derived Brain Symmetry Index (pdBSI) were investigated, as well as head movement using the on-board accelerometer and gyroscope. We then used machine learning to distinguish between different subgroups. DAR and DBATR increased in ischemic stroke patients with increasing stroke severity (p = 0.0021, partial η2 = 0.293; p = 0.01, partial η2 = 0.234). Also, pdBSI decreased in low frequencies and increased in high frequencies in patients who had a stroke (p = 0.036, partial η2 = 0.177). Using classification trees, we were able to distinguish moderate to severe stroke patients and from minor stroke and controls, with a 63% sensitivity, 86% specificity and accuracy of 76%. There are significant differences in DAR, DBATR, and pdBSI between patients with ischemic stroke when compared to controls, and these effects scale with severity. We have shown the utility of a low-cost portable EEG system to aid in patient triage and diagnosis as an early detection tool.

This paper introduces a portable electroencephalogram (EEG) monitoring system for neonatal seizures. The hardware circuit can acquire 8 channels of EEG data or expand to 32 channels. It is characterized in high accuracy, small size, and low power consumption, which can meet the needs of long-term bedside monitoring of newborns. Users can choose different modes to view the historical data of the acquired EEG signals, or real-time data transmitted via WiFi. To further analyze and process the data, this paper introduces a machine learning-based scheme to automatically distinguish between normal and abnormal amplitude-integrated EEG (aEEG) signals. The program extracts the effective feature representation from the data and trains the model. The model can predict the class of unknown aEEG samples, which is helpful for further clinical detection of neonatal brain conditions.

The application of electroencephalography electrodes in Virtual Reality (VR) glasses allows users to relate cognitive, emotional, and social functions with the exposure to certain stimuli. The development of non-invasive portable devices, coupled with VR, allows for the collection of electroencephalographic data. One of the devices that embraced this new trend is Looxid LinkTM, a system that adds electroencephalography to HTC VIVETM, VIVE ProTM, VIVE Pro EyeTM, or Oculus Rift STM glasses to create interactive environments using brain signals. This work analyzes the possibility of using the Looxid LinkTM device to perceive, evaluate and monitor the emotions of users exposed to VR.

BACKGROUNDCommercially-made low-cost electroencephalography (EEG) devices have become increasingly available over the last decade. One of these devices, Emotiv EPOC, is currently used in a wide variety of settings, including brain-computer interface (BCI) and cognitive neuroscience research.PURPOSEThe aim of this study was to chart peer-reviewed reports of Emotiv EPOC projects to provide an informed summary on the use of this device for scientific purposes.METHODSWe followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following electronic databases: PsychINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, clinical, signal processing, experimental research, and validation) and location of use (as indexed by the first author’s address).RESULTSWe identified 382 relevant studies. The top five publishing countries were the United States (n = 35), India (n = 25), China (n = 20), Poland (n = 17), and Pakistan (n = 17). The top five publishing cities were Islamabad (n = 11), Singapore (n = 10), Cairo, Sydney, and Bandung (n = 7 each). Most of these studies used Emotiv EPOC for BCI purposes (n = 277), followed by experimental research (n = 51). Thirty-one studies were aimed at validating EPOC as an EEG device and a handful of studies used EPOC for improving EEG signal processing (n = 12) or for clinical purposes (n = 11).CONCLUSIONSIn its first 10 years, Emotiv EPOC has been used around the world in diverse applications, from control of robotic limbs and wheelchairs to user authentication in security systems to identification of emotional states. Given the widespread use and breadth of applications, it is clear that researchers are embracing this technology.

ObjectiveIn this pilot study, we investigated using portable electroencephalography (EEG) as a potential prehospital stroke diagnostic method.MethodsWe used a portable EEG system to record data from 25 participants, 16 had acute ischemic stroke events, and compared the results of age-matched controls that included stroke mimics. Delta/alpha ratio (DAR), (delta+theta)/(alpha+beta) ratio (DBATR) and pairwise-derived Brain Symmetry Index (pdBSI) were investigated, as well as accelerometer and gyroscope trends. We then made classification trees using TreeBagger to distinguish between different subgroups.ResultsDAR and DBATR showed an increase in ischemic stroke patients that correlates with stroke severity (p2 = 0.293; p2 = 0.234). pdBSI decreased in low frequencies and increased in high frequencies in patients who had a stroke (p2 = 0. 177). All quantitative EEG measures were significant between stroke patients and controls. Using classification trees, we were able to distinguish between subgroups of stroke patients and controls.ConclusionsThere are significant differences in DAR, DBATR, and pdBSI between patients with ischemic stroke when compared to controls; results relate to severity.SignificanceWith significant differences between patients with strokes and controls, we have shown the feasibility and utility for the Muse™ EEG system to aid in patient triage and diagnosis as an early detection tool.

Nonconvulsive status epilepticus (NCSE) is relatively common in older people. Although NCSE is a known cause of delirium, diagnosis is often delayed or missed. We report three cases where NCSE was identified as the cause of delirium in older people that illustrate the challenge of diagnosis. The cases illustrate that clinical manifestations such as slight twitching of the eyelids or periorally, eye signs, automatisms and disorders of speech and language are often very subtle. Electroencephalography (EEG), and monitoring the clinical and EEG effects of intravenous anticonvulsants, are crucial to diagnosis. Increased availability of portable EEG machines will facilitate early diagnosis. In situations where an EEG is not easily available, a trial of treatment is warranted. Hypoactive delirium is the main variant seen in NCSE but psychosis may also occur. Clinicians should maintain a high index of suspicion for NCSE in patients with delirium especially when no other obvious cause is identified.

International tourists in Japan are increasing year by year, and the Japanese government keeps promoting the tourism industry. To make Japan more attractive for tourists, the analysis of tourists' impressions is very important. One of the conventional approaches is to provide tourists with a questionnaire about sightseeing spots and their experiences there. However, the questionnaire has a problem, the memory of the interviewees tends to fade and detailed information cannot be retrieved. In this paper, we describe a novel approach to detect tourists' impression using information from their electroencephalogram (EEG). Since the developed system makes use of a portable EEG device, it enables impression detection even if the subject is walking. In order to confirm the validity of the proposed system, we carried out a real-world experience of the typical sightseeing spots.

Today, Alzheimer’s disease (AD) diagnosis is carried out using subjective mental status examinations assisted in research by scarce and expensive neuroimaging scans and invasive laboratory tests; all of which render the diagnosis time-consuming, geographically confined and costly. Driven by these limitations, quantitative analysis of electroencephalography (EEG) has been proposed as a non-invasive and more convenient technique to study AD. Published works on EEG-based AD diagnosis typically share two main characteristics: EEG is manually selected by experienced clinicians to discard artefacts that affect AD diagnosis, and reliance on EEG devices with 20 or more electrodes. Recent work, however, has suggested promising results by using automated artefact removal (AAR) algorithms combined with medium-density EEG setups. Over the last couple of years, however, low-density, portable EEG devices have emerged, thus opening the doors for low-cost AD diagnosis in low-income countries and remote regions, such as the Canadian Arctic. Unfortunately, the performance of automated diagnostic solutions based on low-density portable devices is still unknown. The work presented here aims to fill this gap. We propose an automated EEG-based AD diagnosis system based on AAR and a low-density (7-channel) EEG setup. EEG data was acquired during resting-awake protocol from control and AD participants. After AAR, common EEG features, spectral power and coherence, are computed along with the recently proposed amplitude-modulation features. The obtained features are used for training and testing of the proposed diagnosis system. We report and discuss the results obtained with such system and compare the obtained performance with results published in the literature using higher-density EEG layouts.

Background: There is a need to monitor brain resuscitation during CPR. Furthermore, 10% of CA survivors report cognitive activity and 2-3% report consciousness during CPR. Methods: Using data from a prospective study (AWARE-II) with bimodal brain monitoring during CPR (EEG, [Massimo] and regional cerebral oxygenation [rSO2 normal 60-80%]) using cerebral oximetry [Nonin]), we sought to identify EEG rhythms, their progression over time, and underlying rSO2 thresholds. Inclusion criteria: In hospital CA, ≥ 18 years. Raw EEG data were captured as images (1/second) during CPR pauses, while rSO2 was captured continuously. Results: 38 patients were recruited; mean age 69±16 with duration of CPR (10-60 mins). We captured 362 EEG images. 91 (25%) were artifacts. Among the 271 interpretable images, there were 13 EEG rhythms placed into 6 groups based on their association with normal/near normal, seizures, coma, absence of cortical activity as follows: Group 1 (Non-epileptogenic): Theta, Delta, Alpha, Group 2 (Epileptogenic/Spike and Wave): Generalized Rhythmic Delta Activity (GRDA) + Spike (S), Delta + S, Theta + S, Burst Suppression with GRDA + S, Group 3 (Flat-line): Marked Voltage Attenuation (MVA), Group 4 (Coma State): Burst Suppression (w/ GRDA, Delta, and/or Theta), Group 5: GRDA, Group 6: Generalized Periodic Discharges (GPD). MVA/absence of activity was observed throughout CPR time and across all rSO2 ranges. Normal/near normal (non-epileptogenic) including alpha rhythms (which to our knowledge is described for the first time), and epileptogenic rhythms were observed with rSO2>30% Conclusions: While unclear whether non-epileptogenic rhythms are associated with transient periods of ROSC, however real-time bimodal brain monitoring provides insights regarding brain resuscitation and its dynamic interaction with patient factors. While ischemia may cause epileptogenic activity, there are periods of normal/near-normal cortical activity despite prolonged CPR >45-60 mins. A minimal threshold of brain oxygen delivery (rSO2>30%) may be required for cortical activity. These data raise questions regarding assumptions of irreversible brain damage with prolonged CPR, as well as the possibility of consciousness and cognitive activity during CPR.

Made available in DSpace on 2019-10-04T12:39:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-07-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Portable EEG devices have a great potential to become efficient computer interfaces. However, a necessary step is to accurately describe the EEG signal in order to quantify brain rhythms. This study explored two signal analysis techniques, Matching Pursuit (MP) and Fast Fourier Transform (FFT), for differentiation between two states, eyes open (EO) and eyes closed (EC), through the detection of EEG alpha activity obtained from seven scalp regions, using a portable EEG device. Subjects were ten healthy male volunteers. MP results generally reproduced the results from FFT analysis, and all methods performed well on the occipital region. However, there was better state discrimination with MP atom number, and MP atom number was the only variable that reached statistical significance on all locations under study. When employing EEG alpha rhythm for EO vs. EC state discrimination, it may be useful to compute MP atom number, especially when extra-occipital acquisition is warranted. (C) 2019 Elsevier Ltd. All rights reserved. Univ Estadual Paulista, Inst Biosci, Dept Phys & Biophys, Dist Rubido Jr S-N, BR-18618970 Botucatu, SP, Brazil Univ Caxias Do Sul, Ctr Ciencias Exatas & Tecnol, Rua Francisco Getulio Vargas 1130, BR-95001970 Caxias Do Sul, RS, Brazil Hosp Clin Porto Alegre, Neurol Div, Rua Ramiro Barcelos 2350,Sala 2040, BR-90035003 Porto Alegre, RS, Brazil Univ Estadual Paulista, Inst Biosci, Dept Phys & Biophys, Dist Rubido Jr S-N, BR-18618970 Botucatu, SP, Brazil CNPq: 458810/2013-4 CNPq: 181242/2014-1 CNPq: 182472/2015-9 CNPq: 181963/2014-0 CNPq: 185855/2014-8 CNPq: 314178/2014-7 CNPq: 310001/2015-3

Sleeplessness and driver’s drowsiness is one of the reasons leading to accidents. Many studies as well as applied products have been recently developed and integrated into car. However, almost research concentrated on eyes-movement and pupillary stretch. Especially, mobile Electroencephalography (EEG) measurement device becomes new trend thanks to its convenience and affordable price. This research focused on using portable EEG device—EPOC Emotiv for detecting sleep-onset by analyzing power spectrum after filtering frequency band of brain waves. The processed features become the input of Support Vector Machine (SVM) classification and the prerequisite for real-time drowsiness detection. The changes of vigilance state classified by SVM in analysis show the result with over 70% data samples, reliably used in driving safety system in the future.

There are increasing needs for self-applicable methods assessing sleep in clinical and non-clinical settings. This study aimed to investigate the inter-scorer reliability for the sleep stage scoring and for the sleep variable assessments in the portable electroencephalography (EEG) and electro-oculography (EOG) recording system. Sleep recordings were performed simultaneously with polysomnography (PSG) and portable EEG/EOG recordings in 14 healthy adults. The inter-scorer concordance of the sleep stage scoring was assessed in the two recording methods. Sleep variables were compared between the two methods using the Bland–Altman plots in each scorer. The overall inter-scorer concordance rate of sleep stage scoring for the EEG/EOG data was 85.5 %, and the Cohen’s κ was 0.80 (p

INTRODUCTION: Intraoperative seizures under general anesthesia are rare and our observation is the first to demonstrate a distinct electroencephalogram (EEG) pattern on the Narcotrend monitor. PATIENT CONCERNS: We present the case of a 30-year-old man undergoing craniotomy for glioblastoma resection under general anesthesia who suffered tonic-clonic seizures captured in a specific pattern by the intraoperative EEG. DIAGNOSES: Our depth of anesthesia monitor recorded, before the seizure, a widening of the beta-wave performance in a distinct "triangular-shaped" pattern. This pattern was repeated before the second seizure. The patient had no previous history of seizures and following surgery no further seizures were recorded. INTERVENTIONS: A spectrogram analysis showed a distinct increase in mean absolute beta power immediately prior to the first seizure. The EEG immediately prior to the second seizure was characterized by broadband noise. Both seizures were characterized by increased mean absolute delta, theta, and beta power. OUTCOMES: The increase in EEG beta activity seen before the tonic-clonic movements may represent cortical irritability secondary to surgical manipulation, induced by electrical stimulation, reflecting progressive brain over-arousal. The attentive analysis of the relative beta power may have helped forecast the occurrence of the second seizure. LESSONS: We report the use of a simple, inexpensive, and portable EEG-based monitoring device to assist seizure detection and decision making.

The Electroencephalogram (EEG) is the non-invasive record of electrical cerebral activity. It is used commonly in sleep studies and in the diagnosis of brain diseases and injuries. In order to reduce noise in the acquisition process and enable new EEG applications such as BCI and home polysomnography studies, we carry out the design and manufacture of a portable wireless system for acquisition of EEG signals. In this paper, we carried out the validation of the system designed by comparing signals acquired with commercial EEG equipment from the AKONIC company. Different EEG recordings were obtained in 4 healthy adult subjects using standard Ag/AgCl cup electrodes and dry electrodes TDE-200 from Florida Research Instruments. Also, we performed the analysis of the acquired signals in eyes-closed and eyes-open resting condition. We conclude that the designed system has low noise levels (0.147 μVRMS and 0.984 μVp-p) and a signal quality comparable to that of commercial equipment, which allows its use in research tasks and portable studies. On the other hand, we observe that the quality of the signal obtained with the dry electrodes is worse, due to the high skin-electrode impedance, but even so it is good enough to detect the eyes-closed and eyes-open resting condition, allowing a more comfortable use of the system.

This study was performed to identify the effects of the improvement of brain function by CST on the elderly with the 2nd or 3rd grade of long-term care insurance service. A quasi-experimental design using a nonequivalent control group, pre-post test was used. A total of 12 elders (6 in the experimental group and 6 in the control group) were recruited. Upledger CST(10-Step Protocol) was performed on each subject for 50 minutes per session, once a week, for a total of 8 treatments over an 8 week period from Jan to Feb 2015. The brain function quotient was measured before the 1st CST and after the last CST by portable EEG measurement device using a 2 Channel neuro-feedback system. The data was analyzed by SPSS (Ver. 18.0) program. After CST intervention, the attention quotient (AQ), level of tension, anti-stress quotient (ASQ), emotion quotient (EQ) and brain quotient (BQ) of the experimental group was significantly better than that of the control group. These results showed that the CST was effective in reducing the level of fatigue by the AQ, increasing the physical and psychological stress relief by the ASQ, emotional balance by the EQ, and improving the total brain function by the BQ. Therefore, CST can be used as an effective intervention for improving the health and brain function of the elderly in health facilities.

：Brain activity measurement has become a useful tool to deepen our understanding of human beings as well as to develop new applications to improve the quality of people’s life. Though EEG (electroencephalogram) has a history of about 90 years in measuring human brain activities, it is still a popular method nowadays in both medical fronts and other research fields, such as neuroscience, psychology, physiology and engineering. The EEG devices have evolved dramatically in different directions to fit the measurement requirements with high spatial resolution or portable convenience. In this paper, the history of the non-invasive human brain activity measurement is looked back briefly. Then the mechanism of EEG and its evolvement are reviewed. Different types of EEG as well as their typical applications are introduced. Finally, the potential prospects of EEG are surveyed.脳活動計測は人間を理解するための有効なツールであり、その応用は生活の質を向上するにも役立つ。EEG は約90 年の歴史を持ちながら、現在でも医療現場や神経科学、心理学、生理学、工学などの研究分野で活用されている。EEG 装置は高い空間分解能や持ち運び便利さなどの要求に応じて、異なる方向に進化してきた。本論文は、人間の非侵襲脳活動計測の歴史を振り返し、EEG の仕組みやその進化を説明する上、異なるタイプのEEG 及び応用を紹介する。最後にEEG の将来を展望する。