EEG Waves Research Articles

A fuzzy neural network approach for automatic K-complex detection in sleep EEG signal

The study of sleep stages and the associated signals have emerged as a very important parameter to identify the neurological disorders and test of mental activities nowadays. Electroencephalogram (EEG) is an electrophysiological method for monitoring, managing, and diagnosing the mental disorders or neurological problems. The EEG signals are highly transient and nonlinear in nature. It varies with the mental conditions. In the sleep state, a non-stationary wave generates with comparatively higher peaks is known as K-complex. The K-complex is a kind of transient wave which can be seen in the NREM stage II sleep. The main difficulty behind the design of the automated K-complex detection system is a nonlinear and dynamic characterization of it. The other difficulty for the system design is the very much similar behaviour of K-complex to other EEG wave. To overcome these problems, in this paper we are giving the detailed description for developing an automatic K-complex detector using fuzzy neural network approach. In this method, fuzzy C-means algorithm is utilized for the rough and rapid recognition of K-complex and the neural network classifier does the exact evaluation on the detected K-complex. One more fast computing Back Proportion algorithm is used for train the network in this work. This technique of detection of K-complex with a well-known pattern present in sleep EEG is a fuzzy neural based software solution in the field of biomedical signal processing.

Neonatal EEG- an overview

Electro-encephalogram (EEG) is the best non-invasive modality for brain monitoring. As brain continues to develop and mature in neonatal period, EEG of a normal newborn varies from time to time. Wave patterns may be normal at one developmental stage and abnormal at another stage. There are many types of EEG waves like alpha, beta, gamma & delta waves. Burst suppression occurs in very sick neonates following brain damage due to asphyxia & predicts poor prognosis. Isoelectric pattern occurs in severe asphyxia, circulatory collapse, massive intracerebral hemorrhage, severe inborn metabolic deficits, CNS bacterial or viral infections, drug-induced state, hypothermia, postictal recording and in malformations like hydranencephaly or massive hydrocephalus. Grading of severity of disease condition can be done based on EEG. They can be vital in arriving etiological diagnosis. EEG distinguishes between normal paroxysmal movements from epileptic seizures. Nearly 90% of abnormal movements mimiking seizures may be nonepileptic after EEG study. Early recordings, prolonged recordings at different activity states, serial short interval EEGs increase the prognostic value of EEGs. Long-time bedside monitoring of brain function can be done by amplitude-integrated EEG (aEEG). Some rare neonatal epilepsy syndromes have characteristic EEG features. Artifacts mimicking electrical seizures include environmental interference, electrode impedence abnormalities, motion artifacts and endogenous non-cerebral potentials which can be distinguished by Polygraphy. Drugs can alter background activity. EEG is a boon for noninvasive bedside continuous monitoring of the brain. Judicious application of this technique can help in prompt management of various pathologies like seizures , encephalopathies, epilepsy and asphyxia.

A review on Machine Learning Techniques for Neurological disorders estimation by Analyzing EEG Waves

A review on Machine Learning Techniques for Neurological disorders estimation by Analyzing EEG Waves

Artifact Removal from Sleep Hipnograms using Complete Ensemble Empirical Decomposition with Adaptive Noise

Objectives: Many sleep related disorders can hamper the performance of an individual and there is an absolute need to research upon such disorders. Methods /Statistical Analysis: The neuro physiologists record an electrical activity of the brain through Electroencephalogram (EEG), since the emphasis of study is sleep. Artifacts are the recorded activities which are not the records of cerebral origin. As the EEG record itself runs for longer periods, it suffers from some artifacts. In this article Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMD-AN) method is proposed for removing the artifacts from the contaminated sleep EEG. Findings: In this article we consider Index of Orthogonality (IORT), Percentage Error in Energy (PEE) and Number of sifting iterations to assess the performance of CEEMD-AN method over the earlier methods are explored. Application/Improvement: The artifact free data gives best results for neuro physiologists to identify the disorder. Keywords: Artifacts, CEEMD-AN, EEG Waves, Noise Adaptive Data Analysis, Sleep EEG

Exploring the Effects of Technology-Enabled Mindfulness and Meditation on Stress Management

Background: Studies suggest that certain technologies may help regulate emotional states and reduce stress. Spire is a wearable device that measures breathing patterns to identify distinct emotional states—calm, tense or focus—to send feedback to the user. Muse is a meditation device that gives real time user audio-feedback based on the EEG wave patterns.

Epileptic interictal discharges are more frequent during NREM slow wave downstates.

Epileptiform activity in various but not all epilepsy and recording types and cerebral areas is more frequent in NREM sleep, and especially during sleep periods with high-amplitude EEG slow waves. Slow waves synchronize high-frequency oscillations: physiological activity from the theta through the gamma band usually appears during scalp-positive upstates while epileptiform activity occurs at transitory phases and the scalp-negative downstate. It has been proposed that interictal discharges (IIDs) are facilitated by the high degree of neuronal firing synchrony during slow wave transitory and downstates. This would suggest that their occurrence increases as a function of slow wave synchronization, indicated by greater amplitude, steeper slopes and higher EEG signal synchronization. We investigated the occurrence of IIDs during NREM sleep slow waves in epileptic patients undergoing presurgical electrophysiological monitoring. Intracranially registered IIDs preferentially occurred during the scalp-negative downstates of frontal scalp slow waves in all subjects. IID occurrence was more frequent during larger slow waves in the pooled sample and a subset of subjects. However, slow wave slope steepness and EEG signal synchronization between two frontal scalp channels was not significantly associated with IID occurrence. Our results indicate that IIDs indeed do not occur at the same slow wave phase as physiological rhythms, but contrary to previous hypotheses their occurrence is not strongly affected by EEG synchronization.

EEG Waves for Robotics and Prosthesis Grasping and Motorization

EEG Waves for Robotics and Prosthesis Grasping and Motorization

Refractoriness Accounts for Variable Spike Burst Responses in Somatosensory Cortex.

Neurons in the primary somatosensory cortex (S1) respond to peripheral stimulation with synchronized bursts of spikes, which lock to the macroscopic 600-Hz EEG waves. The mechanism of burst generation and synchronization in S1 is not yet understood. Using models of single-neuron responses fitted to unit recordings from macaque monkeys, we show that these synchronized bursts are the consequence of correlated synaptic inputs combined with a refractory mechanism. In the presence of noise these models reproduce also the observed trial-to-trial response variability, where individual bursts represent one of many stereotypical temporal spike patterns. When additional slower and global excitability fluctuations are introduced the single-neuron spike patterns are correlated with the population activity, as demonstrated in experimental data. The underlying biophysical mechanism of S1 responses involves thalamic inputs arriving through depressing synapses to cortical neurons in a high-conductance state. Our findings show that a simple feedforward processing of peripheral inputs could give rise to neuronal responses with nontrivial temporal and population statistics. We conclude that neural systems could use refractoriness to encode variable cortical states into stereotypical short-term spike patterns amenable to processing at neuronal time scales (tens of milliseconds).

A New Method for Human Mental Fatigue Detection with Several EEG Channels

Previous researchers have applied EEG in human mental fatigue detection and concluded that the ratio of slow wave to fast wave EEG activities increased with increasing mental fatigue. But these methods failed to reflect functional interactions between different brain areas. The objective of this paper is to develop an indicator for detecting human mental fatigue with several electroencephalogram (EEG) channels considering functional interactions among the brain. To this end, we collected eighteen participants’ EEG data, and constructed the adjacency matrix on the basis of EEG electrode positions using mutual information (MI) to determine the functional connectivity between all pairwise EEG channels. The maximum eigenvalue of adjacency matrix was discussed during the mental fatigue process and considered as the reflections of the corresponding network features. The results indicated that significant statistical differences of the maximum eigenvalue were found only in alpha1 (8–10 Hz) band during task state. With increasing levels of mental fatigue, the maximum eigenvalue increased, indicating that the maximum eigenvalue can be used as an indicator for mental fatigue estimation taking brain function changes into account. For real-time engineering applications, we reduced the EEG channels by finding the central vertices in the brain functional network using weighted degree centrality. Finally, the channels of F3, F4, C3, C4, P3, P4, Fz, Cz, and Pz were obtained for further applications. We also provided an algorithm for automatic human mental fatigue evaluation with these 9 EEG channels. Our results have potential applications in several domains, including in flight, traffic and industrial human safety.

P182 Effects of transcranial direct current stimulation (tDCS) on cognitive activity

Transcranial direct current stimulation (tDCS) is known to modulate neuronal network activity thus affecting cognitive performance. tDCS has demonstrated prolonged effects, reflecting plastic processes in the brain (Nitsche and Paulus, 2001). We investigated an influence of tDCS on EEG waves, cognitive performance and activation of regulation mechanisms in autonomic nervous system. Eight healthy male volunteers underwent anodal/cathodal and sham stimulation on separate days in a single blind crossover trial. tDCS was applied for 10 min at 1.5 mA current with an active electrode over T3 area and a passive electrode over A2 lead. EEG and heart rate variability were recorded during a two-minute interval of eyes-closed/eyes-opened background state before and after stimulation, during a cognitive load (a letter cancelation and an arithmetic test) and during a background state at the end of the experiment. Before the experiment volunteers have passed tapping test to assess Pavlovian nervous system properties. We found that an anodal stimulation provoked an increase in cognitive performance in 87% cases, while the cathodal one showed a slight decrease in 38% cases; other probes demonstrated an opposite effect. We found subject-depended changes in frequency band power after tDCS. Generally, anodal stimulation increased power of all bands, while cathodal one had an opposite effect (Fig. 1). The maximum effects were observed at T3/T4, F3 and O2 leads. We found correlations between the balance of the strength of excitation and inhibition in the nervous system and the changes of theta and beta power after cathodal stimulation. We observed an increase in beta activity across well-balanced individuals while solving cognitive tasks after the cathodal stimulation. Consequently, tDCS affects cognitive and regulatory activity of the brain. Effects depend on the polarity of the active electrode. Anodal stimulation mostly increases cortical electrical activity in temporal and left prefrontal areas. The effect entails an increase in cognitive performance and a reorganization in control of autonomic nervous system which is responsible for activation of behavioral physiological resources. Effects of cathodal stimulation depend on individual balance of the strength of excitation and inhibition in the brain. Download : Download high-res image (122KB) Download : Download full-size image

The effect of hydro-alcoholic of Phoenix Dactylifera extract on sleep and EEG in rat.

Hard envelope of date palm pollen is used as a sedative and calmative compounds in Iranian traditional medicine. We tried to study the effects of Phoenix dactylifera (Tarooneh) extract on sleep time and Brian waves. Rats were divided into control and test groups in sleep experiment. Control groups included intact group (without any injection), negative control group (saline) and positive control group (midazolam 0.1 mg/kg). Test groups received three doses of Tarooneh extract (62.5, 125 and 250 mg/kg). Rat were placed in sleep physiograph system and recording started 20 min after 2-hr calming down. Four parameters including sleep time, awakening, most activity period and main sleep time interval were measured. In EEG experiment electrodes were placed under the cranium for EEG recording and waves were compared with their baselines. All doses of the extract increased sleeping time (p< 0.05) but just the dose of 250 mg/kg (p<0.05) and midazolam (p< 0.001) decreased the awakening time. EEG results showed that the dose of 125 mg/kg increased the low frequency waves (p< 0.05) and the dose of 250 mg/kg decreased high frequency waves of alpha and beta (p< 0.05). Due to these effect on sleeping time and EEG, Tarooneh extract consumption can be useful as a sedative agent in Iranian traditional medicine. According to this study, the doses of 125 and 250 mg/kg of the extract would be the appropriate doses to be further studied.

M62 - GABRA2 Association With Addiction-Related Endophenotypes Is Environmentally Influenced

Background The GABRA2 gene contains more than 250 SNPs that in Caucasians form two major common “Yin-Yang” haplotypes - having either all or none of the non-ancestral (NA) alleles. Since 2004, association of the non-ancestral, slightly less common haplotype with alcohol use disorder (AUD) and with increased beta waves in EEG has been reported and replicated many times. Methods In a longitudinal sample of >400 low SES families at high risk for AUD, we investigated the influence of these GABRA2 haplotypes on addiction related endophenotypes, and started to investigate potential functional implications. We found that the NA haplotype is associated with impulsivity and with increased activation of the insula in response to reward expectation, both of which partially mediate the association of the NA alleles with alcoholism. Impulsivity was also associated with reward expectation, independent of genetics. Results Moreover, the effect of parental monitoring on externalizing behavior trajectories across childhood and adolescence, i.e., consistently low, developmentally limited, and rising trajectories, was moderated by the NA alleles of GABRA2. Subjects with the NA alleles were more strongly influenced – both positively and negatively--by the extent of parental monitoring, while subjects with the ancestral alleles were not significantly influenced by the monitoring. In addition to parents, peers are also known to influence addiction-related behaviors such as rule breaking during adolescence. Association with delinquent peers increased rule breaking, particularly in those with NA alleles of GABRA2, while association with peers who displayed positive behaviors (such as religious activity and scholastic competence) decreased externalizing behaviors. Subjects with the ancestral alleles of GABRA2 were less affected by peers. Discussion Our results illustrate a more complex influence of genotypes on risk for common traits such as those associated with addiction. Our data suggest that the previously identified “risk” alleles impacts the strength of both adaptive and maladaptive environmental influences on risky behaviors such as rule breaking, and hence might be thought of as plasticity factors. None of the alleles of the GABRA2 haplotypes change an amino acid, and they have no major influence on level of expression (eQTL) in several large brain expression samples. Allelic imbalance is more sensitive to slight expression differences. Preliminary allelic imbalance RNASeq analyses of brain mRNA suggest that the NA alleles may increase expression. Our result is in contrast to a recent report of decreased expression of the risk alleles in iPSCs. Epigenetic modifications may be one pathway of how environmental influences on behavior are being molecularly mediated, and just as our results demonstrate plasticity at the behavioral level, plasticity may exist on the molecular level, in that expression of GABRA2 haplotypes may be influenced molecularly by different environments.

Analysis on the Correlation Degree between the Driver’s Reaction Ability and Physiological Parameters

In this paper, the correlation degree between driver’s reaction time and the physiological signal is analyzed. For this purpose, a large number of road experiments are performed using the biopac and the reaction time test systems to collect data. First, the electroencephalograph (EEG) signal is processed by using the fast Fourier and the inverse Fourier transforms. Then, the power spectrum densities (PSD) of α, β, δ, and EEG wave are calculated by Welch procedure. The average power of the power spectrum of α, β, and θ is calculated by the biopac software and two ratio formulas, (α + θ)/β and α/β, are selected to be the impact factors. After that the heart rate and the standard deviation of RR interval are calculated from the electrocardiograph (ECG) signal. Lastly, the correlation degree between the eight impact factors and the reaction time are analyzed based on the grey correlation analysis. The results demonstrate that α/β has the greatest correlation to the reaction time except EEG‐PSD. Furthermore, two mathematical models for the reaction time‐driving time and the α/β‐driving time are developed based on the Gaussian function. These mathematical models are then finally used to establish the functional relation of α/β‐the reaction time.

Establishment of electroencephalographic synchrony and continuity pattern along with disappearance of trace alternant in full-term newborns in a longitudinal pattern up to twelve months of age

Background: This study is concerned with the changes of electroencephalographic (EEG) from immature neonatal to the development of mature normal EEG pattern with special attention given to the infant’s age (0–12 months), growth, and developmental level. Aims and Objectives: The objective is to study serially development of specific EEG patterns in infants, i.e., from birth to 12 months of age. Materials and Methods: This is a longitudinal follow-up study conducted on 50 (27 female and 23 male) healthy, normal, full-term infants delivered vaginally in the Department of Obstetrics and Gynaecology, Netaji Subhash Chandra Bose, Medical College and Hospital, Jabalpur, Madhya Pradesh, starting from the birth to 12 months of age in normal environment in the Department of Physiology EEG-unit for 60 min in 5 different visits, i.e., at birth/0–2 week, 3-month, 6 months, 9 months, and 12 months. Result: This study provides clearly established strong changes in EEG wave pattern and their appearances with increased speed of response, which are most clearly visible from the time of birth, 3–6 months, and at 12 months of age. These marked changes of EEG may be associated with language and other motor milestone development. Conclusion: Sleep characteristics during this period may have predictive value in the assessment of future neurodevelopmental conditions. The study also concluded that the percent of time that infants spend in sleep decreased sharply from 3 to 6 months, but the sleep cycle becomes more defined. The duration of infant’s sleep did not change significantly across the 1st year, but the percent of time spent in active sleep is markedly decreased, i.e., ≤25%, and the sleep progresses toward maturity which is confirmed by the establishment of continuity, symmetrical synchronous EEG pattern, and the disappearance of neonatal trace alternant, along with the focusing on measurement of weight and head circumference.

Slow and fast eeg waves analysis for Kolb's learning style classification

Slow and fast eeg waves analysis for Kolb's learning style classification

Optimizing detection and analysis of slow waves in sleep EEG

BackgroundAnalysis of individual slow waves in EEG recording during sleep provides both greater sensitivity and specificity compared to spectral power measures. However, parameters for detection and analysis have not been widely explored and validated. New methodWe present a new, open-source, Matlab based, toolbox for the automatic detection and analysis of slow waves; with adjustable parameter settings, as well as manual correction and exploration of the results using a multi-faceted visualization tool. ResultsWe explore a large search space of parameter settings for slow wave detection and measure their effects on a selection of outcome parameters. Every choice of parameter setting had some effect on at least one outcome parameter. In general, the largest effect sizes were found when choosing the EEG reference, type of canonical waveform, and amplitude thresholding. Comparison with existing method: Previously published methods accurately detect large, global waves but are conservative and miss the detection of smaller amplitude, local slow waves. The toolbox has additional benefits in terms of speed, user-interface, and visualization options to compare and contrast slow waves. ConclusionsThe exploration of parameter settings in the toolbox highlights the importance of careful selection of detection MethodsThe sensitivity and specificity of the automated detection can be improved by manually adding or deleting entire waves and or specific channels using the toolbox visualization functions. The toolbox standardizes the detection procedure, sets the stage for reliable results and comparisons and is easy to use without previous programming experience.

Electroencephalography Versus Psychometric Tests in Diagnosis of Minimal Hepatic Encephalopathy

Background: Minimal hepatic encephalopathy (MHE) in patients with liver cirrhosis is defined by the presence of otherwise unexplained cognitive abnormalities, only detectable on psychometric or neurophysiological testing, in the absence of overt hepatic encephalopathy (OHE). The objectives were to study the incidence of MHE in patients with liver cirrhosis, and to compare the sensitivity of the electroencephalography (EEG) versus psychometric hepatic encephalopathy score (PHES) in its diagnosis. Methods: This study was conducted on 50 patients with liver cirrhosis. All patients underwent complete medical and neurological examination, laboratory investigations, abdominal ultrasound, EEG, and PHES involving star construction test, the number connection tests, block design test, the digit symbol test, the line drawing test and the circle dotting test. Results: The neuropsychiatric symptoms (but not sufficient to diagnose OHE) were present in 40% of our patients. The psychometric test results were positive in 80% of them. EEG records showed that 64.7% of the patients had no slow waves, 23.5% showed theta waves, 9.8% showed delta waves, while no patients showed triphasic waves. There was a significant correlation between slow waves in EEG and inattention, amnesia and disturbed thinking (P 0.05). Conclusion: The PHES and EEG are important in diagnosis of MHE in patients with liver cirrhosis, but PHES appears to be more sensitive than EEG. J Neurol Res. 2016;6(4):65-71 doi: http://dx.doi.org/10.14740/jnr391w

Restricting Time in Bed in Early Adolescence Reduces Both NREM and REM Sleep but Does Not Increase Slow Wave EEG.

School night total sleep time decreases across adolescence (9-18 years) by 10 min/year. This decline is comprised entirely of a selective decrease in NREM sleep; REM sleep actually increases slightly. Decreasing sleep duration across adolescence is often attributed to insufficient time in bed. Here we tested whether sleep restriction in early adolescence produces the same sleep stage changes observed on school nights across adolescence. All-night sleep EEG was recorded in 76 children ranging in age from 9.9 to 14.0 years. Each participant kept 3 different sleep schedules that consisted of 3 nights of 8.5 h in bed followed by 4 nights of either 7, 8.5, or 10 h in bed. Sleep stage durations and NREM delta EEG activity were compared across the 3 time in bed conditions. Shortening time in bed from 10 to 7 hours reduced sleep duration by approximately 2 hours, roughly equal to the decrease in sleep duration we recorded longitudinally across adolescence. However, sleep restriction significantly reduced both NREM (by 83 min) and REM (by 47 min) sleep. Sleep restriction did not affect NREM delta EEG activity. Our findings suggest that the selective NREM reduction and the small increase in REM we observed longitudinally across 9-18 years are not produced by sleep restriction. We hypothesize that the selective NREM decline reflects adolescent brain maturation (synaptic elimination) that reduces the need for the restorative processes of NREM sleep.

Cannabinoid receptor 1 (CNR1) gene variant moderates neural index of cognitive disruption during nicotine withdrawal.

Nicotine withdrawal-related disruption of cognitive control may contribute to the reinforcement of tobacco use. Identification of gene variants that predict this withdrawal phenotype may lead to tailored pharmacotherapy for smoking cessation. Variation on the cannabinoid receptor 1 gene (CNR1) has been related to nicotine dependence, and CNR1 antagonists may increase attention and memory functioning. We targeted CNR1 variants as moderators of a validated neural marker of nicotine withdrawal-related cognitive disruption. CNR1 polymorphisms comprising the 'TAG' haplotype (rs806379, rs1535255 and rs2023239) were tested independently, as no participants in this sample possessed this haplotype. Nicotine withdrawal-related cognitive disruption was indexed as increased resting electroencephalogram (EEG) alpha-1 power density across 17 electrodes. Seventy-three Caucasian Non-Hispanic smokers (≥15 cigarettes per day) visited the laboratory on two occasions following overnight smoking/nicotine deprivation. Either two nicotine or two placebo cigarettes were smoked prior to collecting EEG data at each session. Analyses showed that rs806379 moderated the effects of nicotine deprivation increasing slow wave EEG (P = 0.004). Smokers homozygous for the major allele exhibited greater nicotine withdrawal-related cognitive disruption. The current findings suggest potential efficacy of cannabinoid receptor antagonism as a pharmacotherapy approach for smoking cessation among individuals who exhibit greater nicotine withdrawal-related cognitive disruption.

Detecting slow eye movement for recognizing driver's sleep onset period with EEG features.

Slow eye movement (SEM) is reported as a reliable indicator of sleep onset period (SOP) in sleep researches, but its characteristics and functions for detecting driving fatigue have not been fully studied. Through visual observations on ten subjects' experimental data, we found that SEMs tend to occur during eye closure events (ECEs). SEMs accompanied with alpha wave's attenuation during simulated driving was observed in our study. We used box plots to analyze the distribution of durations of different ECEs to measure sleepiness level. Experimental results indicate that the ECEs with SEM have higher duration distribution, representing higher sleepiness level, especially for those accompanied by alpha wave's attenuation. This verifies that SEM can be used as a reliable indicator for recognizing driver's SOP. In light of this and considering the possible accompanying of Electroencephalograph (EEG) wave changes, we propose a new algorithm for detecting SEM, which extracted EEG power related features from occipital O2 signal to add them into features set of horizontal Electro-Oculogram (HEOG) signal. Then, maximum relevance and minimum redundancy (mRMR) method was used for feature selection and support vector machine (SVM) was used to classify the SEM class and non-SEM class. Experimental results demonstrate that using EEG power related features can improve the algorithm's accuracy by an average 1.4%. The feature P(α+θ)/β was ranked highest by mRMR among all EEG features, indicating the interactive relationship between EEG waves and SEM.