EEG Brain Signals Research Articles

A Novel Approach to Brain Biometric User Recognition

Advances in the field of information technology makes information security is a major part of it. To deal with security, Recognition plays an important role. Now a day the numbers of organizations are using automated person recognition systems to improve customer satisfaction, to enhance operating efficiency and to secure critical resources. The reliable biometric recognition systems to either confirm or determine the individual's identity to servicing their requests. This paper gives a novel approach on user recognition using EEG signals of Brain. EEG is sensitive to the electrical field generated by the electric currents induced in the brain. EEG recordings are acquired with portable and relatively inexpensive devices when compared to the other brain imaging technique like fMRI. The Emotiv EPOC EEG neuro headset has 14 saline electrodes with two reference sensors is used for the acquisition of brainwaves.

A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals.

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM.

Discussion of Approach for Extracting Pure EOG Reference Signal from EEG Mixture Based on Wavelet Denoising Technique

Several problems in EEG-brain signal analysis are not solved, such as presence of an artifact during the recording process, particularly the eye artifact (ElectroOculoGram (EOG)) which makes the analysis of EEG-brain signals very difficult. Blind source separation technique is one of the important techniques used to clean the EEG signals from different types of artifacts. Independent component analysis (ICA) techniques are widely used for this purpose, but unfortunately the ICA techniques have inherent shortcoming such as source ambiguity and unordered components. Therefore, the researchers used ICA-Reference algorithm. The main problem in ICA-Reference algorithm is to find clean reference signal to extract the wanted signal. Recently, many algorithms proposed to generate the artifact reference, but unfortunately, clean artifact signal not satisfied. In this paper wavelet denoising technique is used to solve this problem by decompose the artifact reference signal into pure artifact signal and residual neural signal. The proposed algorithm used frontal channels instead of EOG channels to extract the EOG reference signal.

Emotion Interaction with Virtual Reality Using Hybrid Emotion Classification Technique toward Brain Signals

Human computer interaction (HCI) considered main aspect in virtual reality (VR) especially in the context of emotion, where users can interact with virtual reality through their emotions and it could be expressed in virtual reality. Last decade many researchers focused on emotion classification in order to employ emotion in interaction with virtual reality, the classification will be done based on Electroencephalogram (EEG) brain signals. This paper provides a new hybrid emotion classification method by combining self- assessment, arousal valence dimension and variance of brain hemisphere activity to classify users’ emotions. Self-assessment considered a standard technique used for assessing emotion, arousal valence emotion dimension model is an emotion classifier with regards to aroused emotions and brain hemisphere activity that classifies emotion with regards to right and left hemisphere. This method can classify human emotions, two basic emotions is highlighted i.e. happy and sad. EEG brain signals are used to interpret the users’ emotional. Emotion interaction is expressed by 3D model walking expression in VR. The results show that the hybrid method classifies the highlighted emotions in different circumstances, and how the 3D model changes its walking style according to the classified users’ emotions. Finally, the outcome is believed to afford new technique on classifying emotions with feedback through 3D virtual model walking expression.

A Wireless Visual Attention Brain Signal Monitoring System

With the recent advances in the field of electroencephalography (EEG), researchers have been able to detect and predict cognitive states and patterns based on electromagnetic signals emitted by the brain with greater precision than ever before. EEG has become a viable means of implementing brain-computer-interfaces (BCIs), which translate brain signals into machine commands. We have developed a system with reconfigurable hardware and open architecture component based software to enable multi-sensor physiological signal monitoring. The goal of this research is to study the feasibility of using the system that can facilitate monitoring of visual attention EEG brain signal.

Quality of teleoperator adaptive control for telerobotic operations

Extensive studies have been conducted on telerobotic operations for decades due to their widespread applications in a variety of areas. Most studies have been focused on two major issues: stability and telepresence. Few have studied the influence of the operation status of the teleoperator on the performance of telerobotic operations. As subnormal operation status of the teleoperator may result in insufficient and even incorrect operations, the quality of teleoperator (QoT) is an important impact on the performance of the telerobotic operations in terms of the efficiency and safety even if both the stability and telepresence are guaranteed. Therefore, this paper investigates the online identification of the QoT and its application to telerobotic operations. The QoT is identified based on five QoT indicators which are generated based on the teleoperator’s brain EEG signals. A QoT adaptive control method is designed to adapt the velocity and responsivity of the robotic system to the operation status of the teleoperator such that the teleoperation efficiency and safety can be enhanced. The online QoT identification method was conducted on various teleoperators and the QoT adaptive control method was implemented on a mobile manipulator teleoperation system. The experimental results demonstrated the effectiveness and advantages of the proposed methods.

Extracting features from phase space of EEG signals in brain–computer interfaces

Conventional feature extraction methods based on autoregressive and amplitude–frequency analysis assume stationarity in the Electroencephalogram signal along short time intervals in Brain–Computer Interface (BCI) studies. This paper proposes a feature extraction method based on phase space for motor imagery tasks recognition. To remodel the single nonlinear sequence is to reveal variety information hidden in the original series by its phase space reconstruction, and maintaining the original information continuity. Three phase space features (PSF) datasets are used to classify two Graz BCI datasets. The simulation has shown that the linear discriminant analysis (LDA) classifiers based on the PSF outperform all the winners of the BCI Competition 2003 and other similar studies on the same Graz dataset in terms of the competition criterion of the mutual information (MI) or misclassification rate. The maximal MI was 0.67 and the minimal misclassification rate was 9.29% for Graz2003 dataset by using the combined PSF. According to the competition criterion of the maximal MI steepness, the LDA classifier based on PSF yielded a better performance than the winner of the BCI Competition 2005 and other similar research on the same Graz dataset for subject O3. The maximal MI steepness of O3 is 0.7355.

Prognostication of Human Brain EEG Signal Dynamics Using a Refined Coupled Oscillator Energy Exchange Model

This article introduces the coupled oscillator energy exchange model (COEEM) which simulates experimentally observed human brain EEG signal dynamics. This model is in some ways similar to the Kuramoto model, but essentially differs in that the Kuramoto model oscillator amplitude is constant, while the COEEM model oscillator amplitude is dependent on the phase of the oscillators. The reasoning behind the COEEM model construction is based on an energy exchange and synchronization simulation in a localized brain area using (i) the coupled oscillators approach and (ii) experimental (non-filtered and filtered) EEG observations. For this purpose, we proposed a unique and very narrow spectral band prognostication and superposition method of just 0.01-0.1 Hz. It has been shown that the COEEM model, is suitable not only for accurate short term prognoses of human brain EEG signal dynamics (several ms) but also for the long term (several seconds). In the latter case, for the chosen mind states and EEG channel prognostication, we created and effectively applied a method for the superposition of prognostication results for very narrow spectral bandwidths. In short, based on the promising prognostication results for the real EEG signals, we infer that the oscillatory model presented here well simulates energy exchange features characteristic to localized human brain activations dynamics.

The Enhancement of Presence with Respect to Moving Images

In this paper we measured individual emotions and sense of presence. This is measured by two methods, a questionnaire survey and electroencephalography (EEG), in order to obtain objective data. EEG brain signals were measured when participants looked at the moving images. The psychophysiology test results of questionnaire survey and EEG for the figurative images were good. In this paper we revealed that the concept of presence is a psychological and physiological reaction related with the individual emotions to the images and that EEG signals can be used as one of the important tool for the visual arts.

Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography

Objective. Brain–computer interfaces (BCIs) based on electroencephalography (EEG) have been shown to accurately detect mental activities, but the acquisition of high levels of control require extensive user training. Furthermore, EEG has low signal-to-noise ratio and low spatial resolution. The objective of the present study was to compare the accuracy between two types of BCIs during the first recording session. EEG and tripolar concentric ring electrode (TCRE) EEG (tEEG) brain signals were recorded and used to control one-dimensional cursor movements. Approach. Eight human subjects were asked to imagine either ‘left’ or ‘right’ hand movement during one recording session to control the computer cursor using TCRE and disc electrodes. Main results. The obtained results show a significant improvement in accuracies using TCREs (44%–100%) compared to disc electrodes (30%–86%). Significance. This study developed the first tEEG-based BCI system for real-time one-dimensional cursor movements and showed high accuracies with little training.

Preliminary Study on Analyzing EEG Alpha Brainwave Signal Activities Based on Visual Stimulation

A current conceptual studies and researches are focusing on rehabilitation of SCI (Spinal Cord Injury) and stroke sufferer by using FES (Functional Electrical Stimulation) induced. The amount of FES induced is depending on level of injury and the muscle since the electrical stimulate is only in μvolt. To have sufficient amount of FES stimulation, the brain signal that transmit to muscle movement is the main controller of the body. Therefore, this study is conducted to measure brainwave signal of alpha wave electrical activity and observed the effect on visual stimulation of alpha wave on healthy normal brain. The brainwave signals measured from scalp using surface electrodes and EEG (Electroencephalograph) to detect the brain activities based on visual stimulation. From the analyzing results, it indicates that alpha waves during periods of eyes closed are the strongest EEG brain signal. Alpha waves is the most suitable signals to be integrated for FES stimulation as it consistency in producing electrical brainwave signals The findings of this conceptual study can be recommended to be implemented to control amount of FES induced based on physical activities specifically for stroke rehabilitation therapy.

Analysis of emotion disorders based on EEG signals of Human Brain

In this research, the emotions and the patterns of EEG signals of human brainare studied. The aim of this research is to study the analysis of the changes in the brain signals in the domain of different emotions. The observations can be analysed for its utility in the diagnosis of psychosomatic disorders like anxiety and depression in economical way with hi gher precision. Emotions play a significant and powerful role in everyday life of human beings. Impulsive emotions express an indication of psychosomatic disorders. These di sorders are reflected as the changes in the electrical activities and chemical activ ities in the brain. The changes can be observed by capturing the brain signals and images. Psychiatrists nowadays, have to deal with the patients with either of two prom inent psychological disorders, viz., Anxiety and Depression. Moreover, the patients are not ready to accept that the symptoms they are suffering from are indicative of some psychological disorder. It becomes a difficult job for the Psychiatrist, rel atives of the patients and people around him to convince that he needs to be treated. The proposed research is expected to quantify the psychological health of the p atient from his EEG, as far as the two problems mentioned above, i.e. Anxiety and Depression are considered.

Emotion Recognition Using Speech and EEG Signal A Review

years the research interest is improving in the field of human computer interaction. This paper focus on one of the aspect of human computer interaction in concern with, the recognition of emotion in a person with the help of Electroencephalogram (EEG) signals and speech. EEG uses an electrical activity of the neurons inside the brain. EEG machine is used for acquisition of the electrical potential generated by the neurons when they are active. The Brain cells communicate with each other by sending electrical impulses. Emotions allow people to express themselves beyond the verbal domain. Speech is the most natural form of communication. A much of work is been done in speech recognition in various languages. It is one of the components that closely related to emotions. Very less work has been carried out using combine aspects of speech, emotion and EEG. Thus this paper attempts to review the combine efforts of EEG brain signal and Speech to recognize the emotions in humans.

Brain–computer interfaces in neurological rehabilitation

Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.

Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface

In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain–computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.

Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis

Independent component analysis (ICA) has been proven useful for suppression of artifacts in EEG recordings. It involves separation of measured signals into statistically independent components or sources, followed by rejection of those deemed artificial. We show that a “leak” of cerebral activity of interest into components marked as artificial means that one is going to lost that activity. To overcome this problem we propose a novel wavelet enhanced ICA method (wICA) that applies a wavelet thresholding not to the observed raw EEG but to the demixed independent components as an intermediate step. It allows recovering the neural activity present in “artificial” components. Employing semi-simulated and real EEG recordings we quantify the distortions of the cerebral part of EEGs introduced by the ICA and wICA artifact suppressions in the time and frequency domains. In the context of studying cortical circuitry we also evaluate spectral and partial spectral coherences over ICA/wICA-corrected EEGs. Our results suggest that ICA may lead to an underestimation of the neural power spectrum and to an overestimation of the coherence between different cortical sites. wICA artifact suppression preserves both spectral (amplitude) and coherence (phase) characteristics of the underlying neural activity.

Replicable Functional Magnetic Resonance Imaging Evidence of Correlated Brain Signals Between Physically and Sensory Isolated Subjects

Previous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) experiments have suggested that correlated neural signals may be detected in the brains of individuals who are physically and sensorily isolated from each other. Functional MRI and EEG methods were used in the present study in an attempt to replicate these findings. DESIGN/SETTINGS: Subjects were electrically and magnetically shielded because of the characteristic surroundings of the scanner room. During the experiment, the nonstimulated subject was placed in the scanner with sensory isolating goggles covering the subject's eyes. The stimulated subject was placed 30 feet away and sat in front of a video monitor that presented an alternating schedule of six stimulus-on/stimulus-off conditions. The stimulus- on condition consisted of a flickering checkerboard pattern whereas the stimulus-off condition consisted of a static checkerboard. Stimulus-on/-off conditions were presented in the sequence on/off/on/off/on/off. The duration of these intervals was randomly assigned but consistently provided a total of 150 seconds of flicker and 150 seconds of static. Sessions were repeated twice to assess possible replication of the phenomenon. Changes in fMRI brain activation (relating to blood oxygenation) and EEG signals were measured in the nonstimulated subjects. Changes occurring during stimulus-on conditions were statistically compared to changes occurring during the stimulus-off conditions. Statistically significant changes in fMRI brain activation and EEG signals were observed when comparing the stimulus-on condition to the stimulus-off condition in nonstimulated subjects (p < 0.001, corrected for multiple comparisons). For fMRI, these changes were observed in visual brain areas 18 and 19 (Brodmann areas). One of the subjects replicated the results. These data replicate previous findings suggesting that correlated neural signals may be detected by fMRI and EEG in the brains of subjects who are physically and sensorily isolated from each other.

SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in Brain–Computer Interfaces

To allow motor-disabled people for communication, Brain–Computer Interfaces (BCIs) are being developed. Such a communication does not depend on the brain's normal output pathways of peripheral nerves and muscles, but is based on analysis of recorded brain activity. In this paper, we compare the performance of power features and phase locking values (PLVs) computed from broadband and narrowband filtered EEG signals for discriminating 3 mental tasks in the framework of a BCI. EEG signals were recorded from 5 subjects while performing the 3 mental tasks left- and right-hand movement imagination and word generation. To reduce the total amount of features, the most discriminative features were selected in a 2-step feature selection procedure by SVM-based recursive feature elimination. Significance tests demonstrated that band power features were more discriminative when they were computed in the narrower frequency band 8–12 Hz. In case of PLV features, the discrimination of mental tasks was significantly better when they were computed from the broader 8–30 Hz frequency band, as compared to the narrower bands 8–12, 13–18 and 19–30 Hz.

Statistical information approaches for the modelling of the epileptic brain

First, the theory of random process is linked with the statistical description of epileptic human brain process. A statistical information approach to the adaptive analysis of the electroencephalogram (EEG) is proposed. Then, the problem of time window recognition of the global stochastic model based upon Bayesian estimation and the use of global optimization for restricted experimental data are proposed. A robust algorithm for estimating unknown parameters of stochastic models is considered. The ability of nonlinear time-series analysis to extract features from brain EEG signal for detecting epileptic seizures is evaluated.