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Independent Component Analysis (ICA) has became the most popular method to remove eye-blinking artifacts from electroencephalogram (EEG) recording. For long term EEG recording, ICA was commonly considered to costing a lot of computation time. Furthermore, with no ground truth, the discussion about the quality of ICA decomposition in a nonstationary environment was specious. In this study, we investigated the "signal" (P300 waveform) and the "noise" (averaged eye-blinking artifacts) on a cross-modal long-term EEG recording to evaluate the efficiency and effectiveness of different methods on ICA eye-blinking artifacts removal. As a result, it was found that, firstly, down sampling is an effective way to reduce the computation time in ICA. Appropriate down sampling ratio could speed up ICA computation 200 times and keep the decomposition performance stable, in which the computation time of ICA decomposition on a 2800 s EEG recording was less than 5 s. Secondly, dimension reduction by PCA was also a way to improve the efficiency and effectiveness of ICA. Finally, the comparison by cropping the dataset indicated that performing ICA on each run of the experiment separately would achieve a better result for eye-blinking artifacts removal than using all the EEG data input for ICA.

The goal of this paper is to develop a Brain Computer Interface (BCI) based on voluntary eye blinks decoding. In particular, the study was focused on the signals generated in the cortex by eye blinking, which can be collected by frontopolar scalp Electroencephalographic (EEG) sensors. Normally, EEG recording systems meant for clinical applications are expensive and cannot be used in large-scale user-friendly applications. Thanks to a prototype made by the STMicroelectronics company, based on an Open Source EEG project, a low-cost EEG recording system was created in this work. The goal is to develop an algorithm that can detect and discriminate between voluntary (forced) and involuntary (natural) blinking so that, in the future, an EEG-based BCI system that is able to control a device through eye movements could be developed, which would be of great use for all people with motor disabilities who can control eye movements. The proposed algorithm is based on a one-dimensional (1D) Convolutional Neural Network (CNN) architecture. Frontopolar EEG signals were collected during the execution of voluntary and spontaneous blinks by four healthy subjects. A dataset of EEG epochs of including blinks was constructed and used to train and validate the proposed CNN. The proposed system allowed to discriminate the blinks performed by the subjects (voluntary vs. involuntary) with an average accuracy of 97.92%.

Aim of this study was to measure possible hypnotizability related differences in the amplitude of cortical heartbeat evoked potentials (HEP) measured during nocturnal sleep stages. Polysomnographic EEG recording was performed in subjects with high (highs, n=6), medium (mediums, n=8) and low (lows, n=24) hypnotizability scores. The mean amplitude of the HEPs for each electrode and sleep stage was calculated in the time window from 200 ms to 600 ms after the R peaks. A multivariate ANOVA using hypnotizability as between-subjects factor was performed for each sleep stage. Results showed higher HEPs mean amplitude in mediums and highs with respect to lows in central, parietal and occipital right regions, during N2, N3 and REM sleep stages. We can hypothesize that, during sleep, individuals with medium-to-high hypnotizability maintain a higher responsiveness to interoceptive information - that is greater body awareness - with respect to lows.

Epilepsy is the most rampant neurological disorder that is diagnosed for around 50 million people in the world. We obtain beneficial information related to the different physiological states of the brain via the electroencephalogram (EEG). S pikes in an EEG recording serves as a major parameter for diagnosing epilepsy in an individual [2]. This research aims to efficiently identify and classify Spikes in an EEG Signal using multilayer filtration of noises to predict epilepsy. The methodology explained in the following research introduces two layers of a classifier to classify time series into spikes and nonspikes given a channel of EEG recording. The advantages of the proposed method are its computational simplicity, easy algorithmic implementation, and immunity to artifacts, noise, and implicit characteristics of EEG. As opposed to a naive approach of sliding windows for the classification of time series, the following method uses a layer of the preliminary classifier with very high classification sensitivity and a layer of classification, where the focus is given on high classification accuracy. The detection algorithm uses mathematical operations and thresholding in the first layer and uses the support vector machine(SVM) classifier in the second layer to classify the time series windows into spikes and non-spikes in an EEG recording.

Epilepsy patients experience challenges in daily life, and epilepsy seizures might cause injuries or endanger the life of the patients or others. The electroencephalogram (EEG) signals, recorded by a machine or device, are often used to analyze the brain electrical activity, which is noninvasive. Locating the seizure period in EEG recordings is usually difficult and time consuming for doctors. Therefore, automatic detection of seizures is necessary. In this paper, we use the autocorrelation function to extract the EEG features, and propose a method based on Recurrent Neural Network to detect the seizure period of the EEG signal, which combines the gated recurrent unit and a 1-D convolutional embedding head. We use the clinical EEG recording of 15 patients to simulate the results of our proposed method. The experimental results demonstrate that our method achieves an excellent performance with 99.6% detection accuracy for Absence Seizure, which can greatly reduce the workload of doctors in clinical diagnosis.

Motor Imagery (MI) is the most popular Brain-Computer Interface (BCI) model which aimed at analyzing and classifying the electroencephalogram (EEG) measured without direct human’s motor movements. The EEG recording is measured on the scalp noninvasively, which has nonstationarity and nonlinearity. To tackle the obstacle for analyzing the EEG obtained during MI tasks, we propose a novel feature extraction method by combining the Hilbert-Huang Transform (HHT) and the dispersion entropy (DisEn). Here, we develop the multivariate HHT using intrinsic mode functions (IMFs) obtained through multivariate empirical mode decomposition (MEMD) instead of HHT using existing EMD. By comparing the classification performance with other traditional methods, we validate the improved capacity of the proposed method, which shows its usefulness in MI BCI model.

Electroencephalographic (EEG) record provides the state of the brain activations and is useful for the clinical diagnosis of the brain dysfunction. However, the preparation of EEG recording such as attachment of electrodes, measurement device setting, etc., is time-consuming and complex. In addition, interpretation of recorded EEG requires particular knowledge and experiences. Therefore, the use of EEG recording is limited. Recent years, simple and easy wearable EEG devices have been developed and are considered to be the practical level. Those devices are easy to attach the measurement electrodes to the scalp without special skills and do not constrain the subjects' movement. In contrast to conventional EEG recordings, the number of electrodes is limited, so the accurate interpretation and/or analysis of EEG characteristics would rather be difficult. Especially, discrimination of actual EEG activities from various contaminated artifacts are crucial for clinical application. In this study, the characteristics of the recorded EEG by using a wearable EEG device was analyzed. Automatic detection method for contaminated artifacts such eye blinking, lateral eye movement (LEM) and electromyographic (EMG) activity were developed. Results were compared with the visual inspection for the recorded data.

Recently it has been demonstrated that electroencephalography (EEG) can be recorded from the ear canal (in-ear EEG), opening the door to using discreet earpieces as wearable brain-computer interfaces (BCIs). We present, for the first time to our knowledge, a wireless neural recording platform for recording EEG from the ear canal with dry multielectrode, user-generic earpieces. A low-cost manufacturing process involving vacuum forming and spray coating was developed to improve ear canal contact in a range of users and combined with a 2.5 x 2.5 cm2 wireless recording system. System performance was evaluated through electrode-skin interface (ESI) impedance characterization and measurement of common EEG signals simultaneously with wet scalp EEG, including eye blinks, alpha waves, and the auditory steady-state response (ASSR) across multiple users. The user-generic ear EEG recorded a mean alpha modulation of 2.17, outperforming the state-of-the-art.

Characterizing coordinated brain dynamics present in high-density neural recordings is critical for understanding the neurophysiology of healthy and pathological brain states and to develop principled strategies for therapeutic interventions. In this research, we propose a new modeling framework called State Space Global Coherence (SSGC), which allows us to estimate neural synchrony across distributed brain activity with fine temporal resolution. In this modeling framework, the cross-spectral matrix of neural activity at a specific frequency is defined as a function of a dynamical state variable representing a measure of Global Coherence (GC); we then combine filter-smoother and Expectation-Maximization (EM) algorithms to estimate GC and the model parameters. We demonstrate a SSGC analysis in a 64-channel EEG recording of a human subject under general anesthesia and compare the modeling result with empirical measures of GC. We show that SSGC not only attains a finer time resolution but also provides more accurate estimation of GC.

In this paper, a new type of EEG recording and electrical stimulation medical device with self-feedback function which using a microelectrode array is developed for the diagnosis and treatment of cranial nerve diseases. The main research of this paper is as follows: (1) Weak brain signal acquisition technology. It uses a noise suppression method without power frequency notch to obtain the true synchronous pulse width EEG waveform in this device. (2) Microcurrent stimulation technology. This device is designed with triple hardware protection and precision constant current source to meet clinical safety and high precision requirements. (3) An expert system for autonomous feedback control of stimulating currents. In this system, it uses a filter with infinite impulse response to preprocess the signal. And it uses wavelet transform combined with mathematical statistics method to extract EEG time-frequency information, integrated fuzzy c-means clustering search algorithm and expert system to control stimulation current. There are the functions of evoked potentials by using EEG nerve electrical stimulation and of using micro-current stimulation to feedback therapy in this device. It can be used for precise location of epilepsy lesions and clinical diagnosis of brain diseases such as Parkinson’s disease. It can also be used for neurological rehabilitation treatments such as depression, insomnia, and brain function disorders. This equipment has important scientific significance and application value for the research of cranial nerve clinical medicine, brain nerve function loop foundation and brain-computer interface

Electroencephalograph (EEG)records spontaneous electrical activity in the human brain, which can be utilized to read the human mind. The study works to decode the content of object image from EEG recorded while the subjects are looking at images. The identification system is developed based on the Long Short-Term Memory (LSTM). Inspired by the cognitive science, features from multiple stages in LSTM network are utilized to discriminate EEG. The proposed method reduces the dependence on the high-density recording EEG and obtains an accuracy of 96.2% for 40 categories. The good performance indicates that the proposed system can be applied to Brain-Computer-interface (BCI)in the future.

Scalp electrical recordings, or electroencephalograms (EEG), are heavily contaminated by cranial and cervical muscle activity from as low as 20 hertz, even in relaxed conditions. It is therefore necessary to reduce or remove this contamination to enable reliable exploration of brain neurophysiological responses. Scalp measurements record activity from many sources, including neural and muscular. Independent Component Analysis (ICA) produces components ideally corresponding to separate sources, but the number of components is limited by the number of EEG channels. In practice, at most 30% of components are cleanly separate sources. Increasing the number of channels results in more separate components, but with a significant increase in costs of data collection and computation. Here we present results to assist in selecting an appropriate number of channels. Our unique database of pharmacologically paralysed subjects provides a way to objectively compare different approaches to achieving an ideal, muscle free EEG recording. We evaluated an automatic muscle-removing approach, based on ICA, with different numbers of EEG channels: 21, 32, 64, and 115. Our results show that, for a fixed length of data, 21 channels is insufficient to reduce tonic muscle artefact, and that increasing the number of channels to 115 does result in better tonic muscle artefact reduction.

Electroencephalogram (EEG) is the most commonly utilized method of mapping brain wave patterns from the scalp. It is a process of extracting electrical activities of the brain in the form of EEG signals using non-invasive methods. In EEG based techniques, a challenge exists in the form of signal contamination caused by internal and external factors in the recorded EEG signals., such as the artifacts, interferences among multiple EEG sources, and noise. Signal pre-processing is the most significant phase for EEG based applications, for instance, the brain computer interface (BCI) systems. This is because the quality of data acquired from the brain through EEG measurements is dependent on the effectiveness of artifact detection and removal methods. During EEG recording there are several factors that influence the quality of data. One of the factors of signal contamination occurs as the result of physiological and non-physiological artifacts. To detect and remove such contaminations from EEG signals, there are several efficient artifact detection and elimination techniques based on certain pre-processing methods. The main objective of this study is to demonstrate various types of artifacts in EEG recordings as results of external and internal factors. Furthermore, the study will also present an extensive review of existing artifacts identification and elimination methods applied on EEG signals.

This paper presentsa physical wireless Electroencephalogram (EEG) based EEG sensor system for detecting drowsiness. Drowsiness of the driver being one of the major reasons for road accidents a drowsiness detector can prevent the road accidents with ease. This EEG sensor system monitors the human cognitive state and provides a biofeedback to the driver while he is drowsy. This system detects the fluctuation in driver's performance with respect to their brain activity and modulates the EEG recording. The drawback of this system is its inability to detect the driver's eye closure, poor accuracy and it's insufficient reaction time to detect the drowsiness.

Clinical evaluation of electroencephalogram (EEG) is essential for monitoring the electrical activity present in the brain. In collusion with engineering advances, the movement towards portable, rapid and low-cost EEG acquisition and monitoring is growing. A low-cost and portable system for neonatal EEG is presented in this paper that aims to improve signal quality and robustness, while decreasing the preparation time and efforts required to complete a routine EEG recording. Several novel designs of dry, wet, active and passive electrodes are presented and compared with the standard EEG acquisition method using objective tests and subjective assessment by an epileptologist. It is shown that sufficient quality is achievable using dry setups, such as microneedle electrodes, with minimal patient preparation for the acquisition of clinical grade recordings.

Any neurological ailment that critically torments the day-to-day survival of patients, reveal its epilogue in any of the alpha, beta, theta or delta spectrum of brain waves. EEG displays the electrical activity of brain and, nowadays it is the most common tool for the diagnosis of neurological maladies. This paper aims at effective de-noising of EEG signals. EEG is obtained by recording the spontaneous electrical activity of the brain over a period of time and it may contain a whole lot of information. This information can be decoded by signal processing methods, but in most cases artifacts interrupt these signals. The recommended approach is based on ICA which is proven better by performance analysis having 96% accuracy and can decompose EEG recording into a number of event related and artifacts related potentials. This study shows that the proposed method significantly enhance the classification accuracy, by effective identification and removal of artifacts.

Epilepsy is a common neural disorder disease; about 1.7% of the global population has epilepsy. Most patients use antiepileptic drugs to reduce their seizures. Among them, nearly one-third of the patients are drug-resistant epilepsy. The alternative treatment is the resection surgery of removing the epileptogenic zone. However, all above patients will still have some seizures, which will influence the patients' quality-of-life, and further introduce danger and inconvenience to patients and people around. This paper presents a design and development of a frontend circuitry in EEG reading for a smart epileptic seizure detection headband. The headband will consist of a textile headband with printed-circuit-board (PCB) inside, and textiled electrodes on it. The PCB includes following circuits: an electroencephalography (EEG) recorder, an epileptic seizure detector circuit, and a Bluetooth transmitter. The analog frontend circuit of EEG recording circuits and its prototype with system integration will be present in this paper. The result of designed circuits yields a compact and low-power design for EEG recording which is suitable for wearable headband designs.

Ear-EEG is a non-invasive EEG recording method, where EEG is recorded from electrodes placed in the ear. Ear-EEG could be implemented into hearing aids, and provide neurofeedback for e.g. objective hearing assessment through measurements of the auditory steady-state response. In cases where the objective is to measure a specific feature of an event-related potential, there will be a subject specific optimal reference configuration. This work presents a method for optimizing the reference configuration for steady-state type potentials. For given electrode positions, the method maximizes the signal-to-noise (SNR) ratio of the first harmonic of the steady-state response. This is obtained by estimating a set of weights applied to the electrode signals. The method was validated on a dataset recorded from 12 subjects. The weights were estimated from one part of the dataset, and the validation was performed on another part of the dataset. For all subjects the proposed method demonstrated a robust SNR estimate, yielding on par or better SNR compared to other well-known methods.

Cochlear implant (CI) stimulation artifacts are currently removed from electrically evoked steady-state response (EASSR) measurements based on a linear interpolation (LI) over the artifact-contaminated signal parts. LI is only successful if CI stimulation artifacts are shorter than the interpulse interval, i.e., for contralateral channels and stimulation pulse rates up to 500 pulses per second (pps). The objective of this paper is to develop and evaluate a template subtraction (TS) method to remove continuous CI stimulation artifacts in order to accurately measure EASSRs. The template construction (TC) is based on an EEG recording containing CI stimulation artifacts but no synchronous neural response. The constructed templates are subtracted from the recording of interest. Response amplitudes and latencies are compared for the TS and LI method, and for different TC durations. The response amplitudes and latencies in contralateral channels are the same after TS and LI, as expected. In ipsilateral channels, response amplitudes and latencies are within the expected range only after TS. The TC duration can be reduced from 5 min to 1 min without a significant effect on response latency. TS with a TC duration of only 1 min allows to remove all CI stimulation artifacts in individual contra- and ipsilateral EEG recording channels. ispartof: IEEE Transactions on Neural Systems and Rehabilitation Engineering vol:25 issue:8 pages:1322- ispartof: location:United States status: published

Multi-modality data acquisition is a topic of research that gained interest in the recent years. It provides the opportunity to gather detailed information for analysis. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging is one good example of it. The information we get after fusing data from EEG and fMRI have both high temporal and spatial resolution. On the other side, this EEG recording suffers from some additional artifacts due to the fMRI environment, in particular, the Ballistocardiogram artifact. In this article, a new method of removing Ballistocardiogram Artifact from evoked potential studies is proposed. The method does not require any reference signal or prior information. The results presented are using the data of three subjects (volunteers). The results show that the proposed method can efficiently reduce Ballistocardiogram artifact and has performed better compared to the conventional methods.

In this paper, we aim to introduce a design of active dry electrodes for EEG based BCI systems. The proposed electrodes consist of dry probes and an active circuit for easy installation and higher quality of EEG recording. To verify the evaluation of the proposed electrodes, we tried to detect alpha rhythms as a typical EEG feature by using our own EEG acquisition boards and Matlab. Experimental results show that the power of alpha rhythm reaches over 50 percent after 10 seconds when the subject closes his eyes. We will verify the performance of the proposed electrodes through various analysis such as detection of visual evoked potential (VEP) and sensorimotor rhythm (SMR) in future research.

The difference in cognitive abilities of humans could be assessed by indexes extracted from EEG. In this paper, we propose and implement an experiment with 60 subjects to study how cognitive abilities can be identified through EEG. We analyzed parameters of the individual frequency band that can be used for prediction of cognitive abilities of subjects. In the experiment, the subjects performed cognitive tests with EEG recording done prior to the tests. Different patterns of alpha band activity are proven to be indicators of cognitive abilities and performances. Our hypothesis is that cognitive abilities can be predicted based on EEG measurements. The results of analysis of the experiment show significant correlation between subjects' cognitive abilities assessed by the tests and the EEG measurements.

A novel methodology is proposed for identifying epileptiform discharges associated with individuals exhibiting Infantile Spasms (ISS) also known as West Syndrome, which is characterized by electroencephalogram (EEG) recordings exhibiting hypsarrythmia (HYPS). The approach to identify these discharges consists of three stages: first - construct the time-frequency domain (TFD) of the EEG recording using matching pursuit TFD (MP-TFD), second - decompose the TFD matrix into two submatrices (W, H) using non-negative matrix factorization (NMF), and third - use the decomposed spectral and temporal vectors to locate the epileptiform discharges, referred to as spikes, during intervals of HYPS. The method was applied to an EEG dataset of five individuals and the identification of spike locations was compared with those which were visually identified by the epileptologists and those obtained using commercially available clinical analysis software. The MP-TFD method resulted in average true positive and false negative percentages of 86% and 14%, respectively, which represents a significant improvement over the clinical software, which achieved average true positive and false negative percentages of 4% and 96%, respectively.

There is a growing interest in data-analytic modeling for prediction and/or detection of epileptic seizures from EEG recording of brain activity [1–10]. Even though there is clear evidence that many patients have changes in EEG signal prior to seizures, development of robust seizure prediction methods remains elusive [1]. We argue that the main issue for development of effective EEG-based predictive models is an apparent disconnect between clinical considerations and dataanalytic modeling assumptions. We present an SVM-based system for seizure prediction, where design choices and performance metrics are clearly related to clinical objectives and constraints. This system achieves very accurate prediction of preictal and interictal EEG segments in dogs with naturally occurring epilepsy. However, our empirical results suggest that good prediction performance may be possible only if the training data set has sufficiently many preictal segments, i.e. at least 6–7 seizure episodes.

Epilepsy is the disease of brain with more than 60 million (approx) cases worldwide. The cure for epilepsy is surgery and medication. Some patients are not cured with surgery and medicine. One third of the cases still remain with unrestrained epilepsy. There is an existing need of constant monitoring for epileptic seizures in this kind of cases. Treatment can be better provided by the doctors if seizure or any atypical brain activity is predicted before its happening or preventive measures can be taken by the patients themselves also if occurrence of the seizure is predicted beforehand. In inter-ictal and postictal periods brain behaves normally. In case of epilepsy, long time EEG recording are essential from days to week. This ultimately reside the patients in the hospital for too many days. Our proposed methodology is on the way to predict the epileptic seizure prior to its occurrence and administer the brain abnormality in real time. There is no such epileptic seizure prediction algorithm using EEG for clinical applications. Our aim is to learn and develop a good epileptic seizure prediction method/algorithm using scalp EEG i.e. noninvasive approach or by using HVR. The proposed pattern detection approach to be used in real time monitoring for patients of epilepsy has great potential and it can be helpful in civilizing the quality of life of the patients. This paper is to review the current state of research and to compare the performance of different algorithms for real-time classification of electroencephalogram signals and the seizure prediction.

Electroencephalography (EEG) is the recording of electrical activity along the scalp of human brain. EEG is most often used to diagnose brain disorders i.e. epilepsy, sleep disorder, coma, brain death etc. EEG signals are frequently contaminated by Eye Blink Artifacts generated due to the opening and closing of eye lids during EEG recording. To analyse signal of EEG for diagnosis it is necessary that the EEG recording should be artifact free. This paper is based on the work to detect the presence of artifact and its actual position with extent in EEG recording. For the purpose of classification of artifact or non-artifact activity Artificial Neural Network (ANN) is used and for detection of contaminated zone the Discrete Wavelet Transform with level 6 Haar is used. The part of zone detection is necessary for further appropriate removal of artifactual activities from EEG recording without losing the background activity. The results demonstrated from the ANN classifier are very much promising such as- Sensitivity 98.21 %, Specificity 87.50 %, and Accuracy 95.83 %.

In EEG recording, the signal obtained is not entirely derived from the brain, but maybe contaminated by other signals called artifacts, such as Electrooculogram (EOG), Electrocardiogram (ECG) and Electromyogram (EMG). While EEG signals were recorded, especially electrodes located near the eyes, will be affected by EOG artifacts. These artifacts or noise may affect the outcome of the EEG recording. In this paper, an independent component analysis algorithm, i.e. JADE algorithm, is proposed to remove the artifacts. The proposed method was tested in real EEG records acquired from sevenhealthy subjects. The experimental result show that the JADE method was effectively remove the artifacts from all subjects.

Electroencephalogram (EEG) is the activity of bioelectrical signals that recorded from electrodes on the scalp. In EEG recording, the signal obtained is not entirely derived from the brain, but may be contaminated by other signals such as Electrooculogram (EOG), Electrocardiogram (ECG) and Electromiogram (EMG). EEG signals that recorded, especially by electrodes located near the eyes, will be affected by EOG. So that necessary action is needed to eliminate or reduce these EEG signals artifacts. This paper proposed a method using ICA for EOG artifact removal and compared which ICA algorithm (JADE and SOBI) is more effective and has better results in the removal of EOG artifacts in EEG recording.

Due to the low signal-to-noise ratio of electroencephalographic (EEG) recordings, the quality of the electrode-scalp contact is an important factor in EEG-based brain-computer interfaces (BCIs). For this reason, the impedance between each individual electrode and the scalp is measured prior to each EEG recording session. In order to obtain high quality EEG signals and accurate performance, the impedance has to be low (below 5K Ohms). Typically, researchers have reduced the electrode-scalp impedance by performing time-consuming electrode adjustments prior to the data acquisition stage. In this paper, we utilize the electrode-scalp impedance information to remove the EEG artifacts caused by high impedance electrodes in order to enhance the signal quality during the signal processing stage. Our proposed method is based on the independent component analysis (ICA) algorithm, which is used to decompose the EEG signals into independent components. The electrode-scalp impedance is employed to automatically distinguish irrelevant components from event-related components. The experimental results show that our method can effectively remove artifacts and enhance the BCI performance compared to the scenario where no artifacts were removed, and the scenario in which irrelevant independent components were removed manually based on prior knowledge.

Automated analysis and grading of the neonatal EEG has a potential to assist clinical decision making for neonates with hypoxic-ischemic encephalopathy. This paper proposes a method to grade the degree of abnormality in hour-long segments of neonatal EEG. The HMM-based speaker diarization approach is employed to segment and cluster the neonatal EEG into homogeneous states. Several features are proposed to characterize the resultant state sequence to provide a single measure for a complete hour-long EEG recording. These features aim at capturing both the statistics of the state durations (e.g. average state duration, average number of segments), and any patterns contained in the sequentiality of the obtained states (e.g. permutation entropy, entropy rate). Statistical analysis indicates that the proposed features contain discriminative information for the task of automated neonatal EEG grading. Unlike other studies, the developed framework of the EEG 'diarization' provides an easy and intuitive interpretation of the computed features, which is a clinically important aspect. © 2014 IEEE.

The electroencephalographic (EEG) activity is analyzed here to detect alpha rhythms asymmetry related to induced depression; EEG signals were acquired from the frontal lobes. Seven young subjects, students of similar age, voluntarily participated in the experiment. The initial emotional state of the subjects was assessed by three independent questionnaires, designed for psychological evaluation of depression and the results were compared. Several sequences of recordings were taken afterward (base, induced happiness, and induced sadness). The left dominant alpha-asymmetry (LDAA) index was calculated for each EEG recording. The outputs were analyzed and compared with similar assessments presented in the scientific literature, leading to similar conclusions, i.e. stimulated happiness lowers the LDAA index and stimulated sadness raises the LDAA index. The results also suggested that the repetition of the process tends to lower personal index values from one session to another.

This paper discusses the characteristics of the electroencephalogram (EEG) signals due to the effects of mobile phone RF radiation exposure. The observation is focused on beta and alpha sub-bands. EEG recording was conducted on 66 healthy subjects for 3 sessions, pre, during and post RF radiation with 5 minutes exposure for each session. The subjects were divided into two groups refer to the side of exposure which is Left Exposure (LE) and Right Exposure (RE) group. Power Asymmetry Ratio (PAR) has been applied to determine the brainwave characteristics due to RF radiation. PAR value of the EEG signals in LE and RE group shows a decrement from pre to post RF radiation session. The correlation of PAR beta-alpha signals decrease when comparing between the sessions after exposed to the RF radiation (0.774 to 0.618 in LE and 0.579 to 0.295 in RE).

This paper considers the problem of classification of electroencephalography (EEG) recordings without the precise time locking between stimulus presentation times and the recorded EEG waveforms. Traditionally, time locking, or perfect timing, information between stimulus and EEG recordings have been crucial in locating the region of possible neural response. In reality, the stimulus' time information is usually unavailable and the latency of test subjects may not be constant (due to fatigue, concentration, interference, etc.). Therefore, new classification approaches that do not depend on stimulus' time information are needed. To tackle this problem, we firstly characterized the brain response pattern of the target event using the EEG data, in which the timing information is available. Then, based on the pattern, a sliding window was applied to the EEG recordings to detect possible target image response started from each individual location. Finally, the probability of a target image event appeared during an entire EEG recording epoch is estimated by summarizing all the possible locations. The results show that, for classification of EEG epochs of 5s, the approach we proposed can obtain a median area under ROC 0.96, a result that comparable to that with perfect stimulus time information.

Recording of awake electroencephalogram (EEG) with sufficient quality is crucial for accurate and significant EEG analysis. However, since the EEG recording is time consuming task, to maintain the quality of records enough is not with the ease. Real-time evaluation system was developed to improve the quality of awake EEG records. Possibility for vigilance level decline of subjects and artifacts contamination were taken account of items on evaluating the quality of EEG records. EEG organization was adopted for estimating the deterioration of quality of recorded EEG due to drowsiness. Developed system was applied to the recorded EEG of three normal healthy adults and five patients with neurological disorders. Detected information of possibility for vigilance level decline and artifacts contamination was displayed in real-time during the recording. Results were investigated and were within a satisfactory level. Real-time evaluation system can be used as an effective computer-assisted tool for improving the quality of awake EEG records.

EEG recording involves having subjects sit on a chair for a couple of hours without being allowed to move and being asked to repeatedly perform various mental, computational, motor imaginary or any other tasks for some specific amount of time. This is a time consuming, boring and complicated procedure during which there is no guarantee that the subject will maintain the proper level of concentration on the requested task at all times, this is apart from the possible muscle activity that might be accidentally generated. This might cause complications in terms of generating signals that do not necessarily contain useful information for classification in the whole tasks time duration. This effect is more likely to appear on recordings in which the task period is longer than usual as in the dataset IVa from BCI competition III in which the task time duration is set to 3.5s. This study investigate the impact of various fragments of time on classification performance. The idea is to improve the classification performance by providing higher concentration on segments of the signal that we assume the subject had better concentration on the task. The results indicate the importance of the middle and end sub-epochs while it illustrate lower performance during the earlier sub-windows.

Being noninvasive, low-risk and inexpensive, EEG is a promising methodology in the application of human Brain Computer Interface (BCI) to help those with motor dysfunctions. Here we employed a center-out task paradigm to study the decoding of hand velocity in the EEG recording. We tested the hypothesis using a linear regression model and found a significant correlation between velocity and the low-pass filtered EEG signal (

The aim of this research is to investigate any effects of mobile phone usage on human brainwaves using electroencephalograph (EEG) particularly on alpha wave. EEG signals were recorded from thirty samples that make calls from a mobile phone to another party without conversation. The mobile phone is strapped to the right ear. The EEG recording took place before, during and after the mobile phone calls. In addition, samples will be interviewed with questions related to the usage of hand phones prior to EEG recording. The brainwave signals were analyzed using statistical analysis. The EEG result shows that the alpha level of the right side decreases significantly during the calls and further decreases within the period of five minutes after the calls were ended. However, the alpha level of the left side remains consistent throughout the experiment. It follows that the correlation between the left and the right brainwaves signal decreases significantly during the calls and further decreases within the period of five minutes after calls. There is evidence that the usage of mobile phones affect the alpha brainwaves.

Although the concept of using brain waves, e.g. Electroencephalogram (EEG) in personal identification has been validated, some unanswered practical and theoretical questions prevent this technology from further development for commercialization. In this paper, the recording factors (e.g. diet, times of day, etc.) affecting a personal EEG recordings are investigated. Experimental results show that recording factors have some effects in the performance of EEG-based personal identification system. This research suggests that it is necessary to build more comprehensive training sets to obtain a higher system performance. Based on a well-designed personal identification EEG recording experiment, it is the first time to give the quantitative measurements of the diet and person's circadian effects on the performance of EEG-based personal identification system.

In a critical care setting, delays in the diagnosis of neurological conditions can have a significant impact on patient outcomes. Recording the electrical activity of the brain (EEG) is often used to diagnose and monitor neurological conditions. A trained neurophysiologist is then required to analyse these signals. However, in many cases this expertise is not available on-site. A web-based remote monitoring system for EEG data is presented that reduces the delays that can arise when off-site expert analysis is required. The system allows EEG data to be streamed to a remote location in near-real-time and viewed while acquisition is ongoing. Data streamed earlier may also be reviewed, providing the user with a continually updating view of the entire EEG recording. All communications are performed using web technologies in order to minimise issues with firewalls and to enable analysis on most web-connected PCs. The only tool required for viewing EEG data is a modern web browser with the commonly-available Adobe Flash plugin installed. Since these are ubiquitous, data analysis is not limited by geographic location. Moreover, multiple users can independently view the data simultaneously

This paper presents an evaluation conducted between human stress questionnaires with EEG Power Spectrum of Beta and Alpha band. Cohen's Perceived Stress Scale (PSS) was used as stress questionnaires to evaluate human stress. The EEG recording of 13 volunteers were carried out immediately after them answering the stress questionnaires. The scores from the stress questionnaires were calculated and used to figure out its relationship with the ratio of EEG Beta and Alpha band power. The results of the study showed the PSS was negatively correlated with the ratio of EEG Power Spectrum. Besides, the study suggested that it was feasible to use PSS and the ratio of EEG Power Spectrum to determine human stress.

Epilepsy is one of the most common neurological disorders and affects more than 50 million individuals worldwide. Neurosurgery is an option for patients and intracranial EEG monitoring needs to be performed before surgery, which requires a multi-channel implantable EEG device. This paper presents a 1V 32-channel reconfigurable sensor interface IC for intracranial EEG recording. The power consumption of the overall system including amplification, filtering, and 10b ADC is 22µW, which makes it suitable for other implantable applications such as deep-brain stimulation.

In this experiment, we record the EEG data during the sleeping and the awakening for the drowsiness cognition. As is well known, analyzing the frequency components of the EEG is important for the sleeping. There are many studies to analyze the EEG frequency data for recognizing the sleeping quality, and so on. However, there is no established method for the analysis of the sleeping EEG. From these reason, we are going to apply the rhythmic component extraction (RCE), proposed by Tanaka et al., to extract the rhythmic component in the brain. RCE finds a component extracted by using a weighted sum of observed channel signals. The weights are optimized by maximizing the power in a certain frequency range of interest. In the experiment, the subjects lie back, relax in a chair, and sleep. We take the EEG recording at 30 channels. From the results, the EEG features, such as the alpha and the theta wave, are different between the sleeping and the waking. These rhythmic components are extracted by the Fourier transform and the RCE. By using the RCE, we confirmed the alpha wave and the theta wave when they are not extracted in a single channel signal. Furthermore, we confirmed the effective measurement locations by analyzing the RCE weights.

Epilepsy is one of the most common neurological disorders that greatly impair patient' daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of seizure (ictal) activities. Nowadays, there are many systems helping the neurologists to quickly find interesting segments of the lengthy signal by automatic seizure detection. However, we notice that it is very difficult, if not impossible, to obtain long-term EEG data with seizure activities for epilepsy patients in areas lack of medical resources and trained neurologists. Therefore, we propose to study automated epileptic diagnosis using interictal EEG data that is much easier to collect than ictal data. The authors are not aware of any report on automated EEG diagnostic system that can accurately distinguish patients' interictal EEG from the EEG of normal people. The research presented in this paper, therefore, aims to develop an automated diagnostic system that can use interictal EEG data to diagnose whether the person is epileptic. Such a system should also detect seizure activities for further investigation by doctors and potential patient monitoring. To develop such a system, we extract four classes of features from the EEG data and build a Probabilistic Neural Network (PNN) fed with these features. Leave-one-out cross-validation (LOO-CV) on a widely used epileptic-normal data set reflects an impressive 99.5% accuracy of our system on distinguishing normal people's EEG from patient's interictal EEG. We also find our system can be used in patient monitoring (seizure detection) and seizure focus localization, with 96.7% and 77.5% accuracy respectively on the data set., Comment: 5 pages, 6 figures, 1 table, submitted to IEEE ICTAI 2008

In this paper a new coherence based method to extract the appropriate EEG features for a five mental tasks classification problem is proposed. The new introduced method has the advantage of using an adaptive new technique that models the EEG signal using the frequency information obtained by employing the coherence function to each EEG recording channel. The adaptive attribute of the technique is due to both, to the amplitude and, respective, to the phase adaptive processes used to model the EEG signal. Another specificity of the new modeling technique is given by the fact of exploiting the nonlinear dynamics of the brain system; this is reflected in the particular spectral mixing of the fundamental spectral components obtained first, by using the coherence function. Finally, to conclude the obtained results in comparison with the results reported in the literature, by using this new approach the classification rate was noticeably improved.

This paper presents the results of the first human trials with the ENOBIO electrophysiology electrode prototype plus the initial results of a new wireless prototype with flexible electrodes based on the same platform. The results indicate that a dry active electrode that employs a CNT array as the electrode interface can perform on a par with traditional ";wet"; electrodes for the recording of EEG, ECG, EOG and EMG. We also demonstrate a new platform combining wireless technology plus flexible electrodes for improved comfort for applications that take advantage of the dry electrode concept.

A new method for detecting low level epileptic spikes is presented in this paper. The method is based on FMH filtering and utilises the correlation between different channels of the EEG recording. Attention is paid to rejecting spikes caused by the EMG (electromyogram) artifacts and artifacts caused by movements of the recording electrodes. Efficiency of the method with different parameters is demonstrated by simulations with 5 hours of real EEG-signal.