scholarly journals Spectral EEG Features of a Short Psycho-physiological Relaxation

2014 ◽  
Vol 14 (4) ◽  
pp. 237-242 ◽  
Author(s):  
Michal Teplan ◽  
Anna Krakovská ◽  
Marián Špajdel
Keyword(s):  
Time Course ◽  
Total Power ◽  
Frequency Range ◽  
Eyes Closed ◽  
Beta Power ◽  
Eeg Data ◽  
Physiological Relaxation ◽  
Potential Applications ◽  
Minute Duration ◽  
Eeg Features

Abstract Short-lasting psycho-physiological relaxation was investigated through an analysis of its bipolar electroencephalographic (EEG) characteristics. In 8 subjects, 6-channel EEG data of 3-minute duration were recorded during 88 relaxation sessions. Time course of spectral EEG features was examined. Alpha powers were decreasing during resting conditions of 3-minute sessions in lying position with eyes closed. This was followed by a decrease of total power in centro-parietal cortex regions and an increase of beta power in fronto-central areas. Represented by EEG coherences the interhemispheric communication between the parieto-occipital regions was enhanced within a frequency range of 2-10 Hz. In order to discern between higher and lower levels of relaxation distinguished according to self-rated satisfaction, EEG features were assessed and discriminating parameters were identified. Successful relaxation was determined mainly by the presence of decreased delta-1 power across the cortex. Potential applications for these findings include the clinical, pharmacological, and stress management fields.

scholarly journals Audiovisual incongruence differentially impacts left and right hemisphere sensorimotor oscillations: Potential applications to production

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258335
Author(s):  
David Jenson
Keyword(s):  
Time Course ◽  
Right Hemisphere ◽  
Time Frequency ◽  
Multimodal Signals ◽  
Eeg Data ◽  
Sensorimotor Activity ◽  
Potential Applications ◽  
Beta Frequency ◽  
Resolution Processes ◽  
Level Of Analysis

Speech production gives rise to distinct auditory and somatosensory feedback signals which are dynamically integrated to enable online monitoring and error correction, though it remains unclear how the sensorimotor system supports the integration of these multimodal signals. Capitalizing on the parity of sensorimotor processes supporting perception and production, the current study employed the McGurk paradigm to induce multimodal sensory congruence/incongruence. EEG data from a cohort of 39 typical speakers were decomposed with independent component analysis to identify bilateral mu rhythms; indices of sensorimotor activity. Subsequent time-frequency analyses revealed bilateral patterns of event related desynchronization (ERD) across alpha and beta frequency ranges over the time course of perceptual events. Right mu activity was characterized by reduced ERD during all cases of audiovisual incongruence, while left mu activity was attenuated and protracted in McGurk trials eliciting sensory fusion. Results were interpreted to suggest distinct hemispheric contributions, with right hemisphere mu activity supporting a coarse incongruence detection process and left hemisphere mu activity reflecting a more granular level of analysis including phonological identification and incongruence resolution. Findings are also considered in regard to incongruence detection and resolution processes during production.

scholarly journals EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

2021 ◽  
Vol 11 (2) ◽  
pp. 214
Author(s):  
Anna Kaiser ◽  
Pascal-M. Aggensteiner ◽  
Martin Holtmann ◽  
Andreas Fallgatter ◽  
Marcel Romanos ◽  
...  
Keyword(s):  
Data Quality ◽  
Large Scale ◽  
Linear Models ◽  
Spectral Power ◽  
Control Group ◽  
Continuous Performance Task ◽  
Related Data ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

SIMULATION AND MODELING OF EVOKED RESPONSE ELECTROENCEPHALOGRAPH SIGNAL

1999 ◽  
Vol 10 (04) ◽  
pp. 759-776
Author(s):  
D. R. KULKARNI ◽  
J. C. PARIKH ◽  
R. PRATAP
Keyword(s):  
Small Range ◽  
Eeg Signal ◽  
Simulation And Modeling ◽  
State Space Reconstruction ◽  
Eyes Closed ◽  
Normal Individuals ◽  
Eeg Data ◽  
Closed Data ◽  
Simulation Results ◽  
Low Dimensional

Electroencephalograph (EEG) data for normal individuals with eyes-closed and under stimuli is analyzed. The stimuli consisted of photo, audio, motor and mental activity. We use several measures from nonlinear dynamics to analyze and characterize the data. We find that the dynamics of the EEG signal is deterministic and chaotic but it is not a low dimensional chaotic system. The evoked responses lead to a redistribution of strengths relative to eyes-closed data. Basically, strength in α waves decreases whereas that in β wave increases. We also carried out simulations separately and in combination for δ, θ, α and β waves to understand the data. From the simulation results, it appears that the characteristics of EEG data are consequences of filtering the data with a relatively small range of frequency (0.5–32 Hz). In view of this, we believe that calculation of known nonlinear measures is not likely to be very useful for studying the dynamics of EEG data. We have also successfully modeled the EEG time series using the concept of state space reconstruction in the framework of artificial neural network. It gives us confidence that one would be able to understand, in a more basic way, how collectivity in EEG signal arises.

scholarly journals A Novel Tool for the Removal of Muscle Artefacts from EEG: Improving Data Quality in the Gamma Frequency Range

2020 ◽  
Author(s):  
Alina Pauline Liebisch ◽  
Thomas Eggert ◽  
Alina Shindy ◽  
Elia Valentini ◽  
Stephanie Irving ◽  
...  
Keyword(s):  
Data Quality ◽  
Time Course ◽  
Muscular Activity ◽  
Gamma Oscillations ◽  
Gamma Band ◽  
Gamma Activity ◽  
Single Muscle ◽  
Frequency Range ◽  
Continuous Eeg ◽  
Artefact Removal

AbstractBackgroundThe past two decades have seen a particular focus towards high-frequency neural activity in the gamma band (>30Hz). However, gamma band activity shares frequency range with unwanted artefacts from muscular activity.New MethodWe developed a novel approach to remove muscle artefacts from neurophysiological data. We re-analysed existing EEG data that were decomposed by a blind source separation method (independent component analysis, ICA), which helped to better spatially and temporally separate single muscle spikes. We then applied an adapting algorithm that detects these singled-out muscle spikes.ResultsWe obtained data almost free from muscle artefacts; we needed to remove significantly fewer artefact components from the ICA and we included more trials for the statistical analysis compared to standard ICA artefact removal. All pain-related cortical effects in the gamma band have been preserved, which underlines the high efficacy and precision of this algorithm.ConclusionsOur results show a significant improvement of data quality by preserving task-relevant gamma oscillations of cortical origin. We were able to precisely detect, gauge, and carve out single muscle spikes from the time course of neurophysiological measures. We advocate the application of the tool for studies investigating gamma activity that contain a rather low number of trials, as well as for data that are highly contaminated with muscle artefacts. This validation of our tool allows for the application on event-free continuous EEG, for which the artefact removal is more challenging.

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

scholarly journals Evaluation of physical euthanasia for neonatal piglets on-farm

2020 ◽  
Vol 98 (7) ◽  
Author(s):  
Filipe Antonio Dalla Costa ◽  
Troy J Gibson ◽  
Steffan Edward Octávio Oliveira ◽  
Neville George Gregory ◽  
Arlei Coldebella ◽  
...  
Keyword(s):  
Nasal Bone ◽  
Occipital Lobe ◽  
Total Power ◽  
Weight Class ◽  
Blunt Force ◽  
Concrete Floor ◽  
Neonatal Piglets ◽  
Blunt Force Trauma ◽  
Beta Power ◽  
On Farm

Abstract Twenty-seven neonate piglets (range from 0.35 to 1.17 kg) were evaluated for the effectiveness of blunt force trauma as a method of on-farm cull. Brainstem function, brain injury, and hemorrhage scores (increasing from 0 to 3) were assessed after striking the head against a concrete floor. Electroencephalograms (EEG) from a subset of 15 piglets were recorded before and after blunt force trauma for electrophysiological assessments. Blunt force trauma was performed by a single experienced farmer in a commercial farm by holding the piglet by its both hind legs and striking the head against the concrete floor. All piglets remained recumbent and did not show brainstem reflexes. Only one piglet did not presented tonic/clonic physical activity. The mean time to the onset of persistent isoelectric EEG was 64.3 ± 7.3 s (range 18 to 115). Total power, theta, alpha, and beta power decreased to approximately 45%, 30%, 20%, and 15% from pretreatment power, respectively, by 15-s post-impact. There were no periods of normal-like EEG after the culling. Bruises in the neck and shoulder were found in 67% and 70% of piglets, respectively. All piglets presented skull fractures with 20% having the nasal bone(s) fractured. Brain damage was found in all piglets, mainly in the frontal lobe(s). The occipital lobe(s) presented the greatest frequency of severe damage. The analysis of the radiographs also found a high frequency of fractures in this region. Hemorrhage was most frequent in the frontal, parietal, occipital lobes, and midbrain. When performed correctly with the appropriate weight class, blunt force trauma can be used as an effective method for the on-farm killing of nursing piglets resulting in death. However, this method should not be promoted over more reliable and repeatable cull methods such as captive bolt gun. As with blunt force trauma, there is a significant potential for animal welfare harm associated with inappropriate practice, lack of accuracy, issues with repeatability, and operator fatigue.

scholarly journals Amplitude of fNIRS Resting-State Global Signal Is Related to EEG Vigilance Measures: A Simultaneous fNIRS and EEG Study

2020 ◽  
Vol 14 ◽  
Author(s):  
Yuxuan Chen ◽  
Julia Tang ◽  
Yafen Chen ◽  
Jesse Farrand ◽  
Melissa A. Craft ◽  
...  
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Near Infrared ◽  
Low Frequency ◽  
Temporal Correlation ◽  
Frequency Range ◽  
Functional Near Infrared Spectroscopy ◽  
Physical Constraints ◽  
Global Signal ◽  
Eeg Data

Recently, functional near-infrared spectroscopy (fNIRS) has been utilized to image the hemodynamic activities and connectivity in the human brain. With the advantage of economic efficiency, portability, and fewer physical constraints, fNIRS enables studying of the human brain at versatile environment and various body positions, including at bed side and during exercise, which complements the use of functional magnetic resonance imaging (fMRI). However, like fMRI, fNIRS imaging can be influenced by the presence of a strong global component. Yet, the nature of the global signal in fNIRS has not been established. In this study, we investigated the relationship between fNIRS global signal and electroencephalogram (EEG) vigilance using simultaneous recordings in resting healthy subjects in high-density and whole-head montage. In Experiment 1, data were acquired at supine, sitting, and standing positions. Results found that the factor of body positions significantly affected the amplitude of the resting-state fNIRS global signal, prominently in the frequency range of 0.05–0.1 Hz but not in the very low frequency range of less than 0.05 Hz. As a control, the task-induced fNIRS or EEG responses to auditory stimuli did not differ across body positions. However, EEG vigilance plays a modulatory role in the fNIRS signals in the frequency range of less than 0.05 Hz: resting-state sessions of low EEG vigilance measures are associated with high amplitudes of fNIRS global signals. Moreover, in Experiment 2, we further examined the epoch-to-epoch fluctuations in concurrent fNIRS and EEG data acquired from a separate group of subjects and found a negative temporal correlation between EEG vigilance measures and fNIRS global signal amplitudes. Our study for the first time revealed that vigilance as a neurophysiological factor modulates the resting-state dynamics of fNIRS, which have important implications for understanding and processing the noises in fNIRS signals.

scholarly journals EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Bingtao Zhang ◽  
Tao Lei ◽  
Hong Liu ◽  
Hanshu Cai
Keyword(s):  
Visual Inspection ◽  
Sleep Stages ◽  
Subjective Time ◽  
Sleep Staging ◽  
Large Bulk ◽  
State Classification ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Eeg Features

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.

A Comparison of the Electroencephalogram between Institutionalized and Community Children in Romania

2004 ◽  
Vol 16 (8) ◽  
pp. 1327-1338 ◽  
Author(s):  
Peter J. Marshall ◽  
Nathan A. Fox ◽  
BEIP Core Group
Keyword(s):  
High Frequency ◽  
Local Community ◽  
System Development ◽  
Low Frequency ◽  
Nervous System Development ◽  
Learning Disorders ◽  
Temporal Region ◽  
Beta Power ◽  
Eeg Data ◽  
Infants And Young Children

Electroencephalographic (EEG) data were collected from a sample of institutionalized infants and young children in Bucharest, Romania, and were compared with EEG data from age-matched children from the local community who had never been institutionalized and who were living with their families in the Bucharest area. Compared with the never-institutionalized group, the institutionalized group showed a pattern of increased low-frequency (theta) power in posterior scalp regions and decreased high-frequency (alpha and beta) power, particularly at frontal and temporal electrode sites. This finding is consistent with EEG studies of children facing environmental adversity and children with learning disorders. The institutionalized group also showed less marked hemispheric EEG asymmetries than the never-institutionalized group, particularly in the temporal region. The results are discussed in the context of two models: that the pattern of EEG in the institutionalized children reflects a maturational lag in nervous system development, or that it reflects tonic cortical hypoactivation.

A Numerical Study of Information Entropy in EEG Wavelet Analysis

2016 ◽  
Author(s):  
Parham Ghorbanian ◽  
Hashem Ashrafiuon
Keyword(s):  
Information Entropy ◽  
Signal Analysis ◽  
Numerical Study ◽  
Window Size ◽  
Normal Subjects ◽  
Sample Entropy ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eeg Signal Analysis ◽  
The Ideal

The purpose of this study is to numerically evaluate the performance of information entropy in electroencephalography (EEG) signal analysis. In particular, we use EEG data from an Alzheimer’s disease (AD) pilot study and apply several wavelet functions to determine the signals’ time and frequency characteristics. The wavelet entropy and wavelet sample entropy of the continuous wavelet transformed data are then determined at various scale ranges corresponding to major brain frequency bands. Non-parametric statistical analysis is then used to compare the entropy features of the EEG data obtained in trials with AD patients and age-matched healthy normal subjects under resting eyes-closed (EC) and eye-open (EO) conditions. The effectiveness and reliability of both choice of wavelet functions and the parameters used in wavelet sample entropy calculations are discussed and the ideal choices are identified. The result shows that, when applied to wavelet transformed filtered data, information entropy can be effective in determining EEG discriminant features, after selecting the best wavelet functions and window size of the sample entropy.

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