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.

Effect of a Static Magnetic Field (1.5T) on Brain Oscillatory Activities in Resting State Condition

2008 ◽  
Vol 21 (5) ◽  
pp. 629-635 ◽  
Author(s):  
E. Formaggio ◽  
M. Avesani ◽  
S.F. Storti ◽  
F. Milanese ◽  
A. Gasparini ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Static Magnetic Field ◽  
Alpha Power ◽  
Eeg Signal ◽  
Eyes Closed ◽  
Prime Concern ◽  
Eeg Data ◽  
Significant Difference ◽  
Eyes Open ◽  
The Magnetic Field

The aim of the present study was to compare the EEG signal recorded outside and inside a 1.5T magnetic resonance (MR) scanner. The EEG was recorded in eyes open and eyes closed conditions using a digital recording MR-compatible system. To characterize how a static magnetic field induces changes in EEG signal, EEG data were analyzed using FFT frequency analysis. No significant difference between the alpha powers recorded outside and inside the magnetic field was observed in eyes closed conditions. However, in eyes open condition there was a significant increase in alpha power inside the magnet in comparison to the outside position. The changes in alpha power according to the eyes open/closed conditions could be inversely correlated to a subject's state of wakefulness and due to some physiological changes, rather than an effect of the magnetic field. This experiment suggests that subjects' state of wakefulness is of prime concern when performing functional MRI.

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.

scholarly journals The Influence of Filters on EEG-ERP Testing: Analysis of Motor Cortex in Healthy Subjects

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7711
Author(s):  
Ilona Karpiel ◽  
Zofia Kurasz ◽  
Rafał Kurasz ◽  
Klaudia Duch
Keyword(s):  
Gold Standard ◽  
Healthy Subjects ◽  
Data Interpretation ◽  
Eeg Signal ◽  
Sensorimotor Area ◽  
Eeg Data ◽  
Reduction Methods ◽  
Characteristic Values ◽  
No Gold Standard ◽  
Correct Data

The raw EEG signal is always contaminated with many different artifacts, such as muscle movements (electromyographic artifacts), eye blinking (electrooculographic artifacts) or power line disturbances. All artifacts must be removed for correct data interpretation. However, various noise reduction methods significantly influence the final shape of the EEG signal and thus its characteristic values, latency and amplitude. There are several types of filters to eliminate noise early in the processing of EEG data. However, there is no gold standard for their use. This article aims to verify and compare the influence of four various filters (FIR, IIR, FFT, NOTCH) on the latency and amplitude of the EEG signal. By presenting a comparison of selected filters, the authors intend to raise awareness among researchers as regards the effects of known filters on latency and amplitude in a selected area—the sensorimotor area.

scholarly journals Increased signal diversity/complexity of spontaneous EEG, but not evoked EEG responses, in ketamine-induced psychedelic state in humans

2019 ◽  
Author(s):  
Nadine Farnes ◽  
Bjørn E. Juel ◽  
André S. Nilsen ◽  
Luis G. Romundstad ◽  
Johan F. Storm
Keyword(s):  
Brain Activity ◽  
Altered States Of Consciousness ◽  
Altered States ◽  
Eeg Signal ◽  
Eyes Closed ◽  
Electrical Brain Activity ◽  
States Of Consciousness ◽  
Significant Difference ◽  
Eyes Open ◽  
Signal Diversity

AbstractObjectiveHow and to what extent electrical brain activity is affected in pharmacologically altered states of consciousness, where it is mainly the phenomenological content rather than the level of consciousness that is altered, is not well understood. An example is the moderately psychedelic state caused by low doses of ketamine. Therefore, we investigated whether and how measures of evoked and spontaneous electroencephalographic (EEG) signal diversity are altered by sub-anaesthetic levels of ketamine compared to normal wakefulness, and how these measures relate to subjective assessments of consciousness.MethodsHigh-density electroencephalography (EEG, 62 channels) was used to record spontaneous brain activity and responses evoked by transcranial magnetic stimulation (TMS) in 10 healthy volunteers before and after administration of sub-anaesthetic doses of ketamine in an open-label within-subject design. Evoked signal diversity was assessed using the perturbational complexity index (PCI), calculated from the global EEG responses to local TMS perturbations. Signal diversity of spontaneous EEG, with eyes open and eyes closed, was assessed by Lempel Ziv complexity (LZc), amplitude coalition entropy (ACE), and synchrony coalition entropy (SCE).ResultsAlthough no significant difference was found in the index of TMS-evoked complexity (PCI) between the sub-anaesthetic ketamine condition and normal wakefulness, all the three measures of spontaneous EEG signal diversity showed significantly increased values in the sub-anaesthetic ketamine condition. This increase in signal diversity also correlated with subjective assessment of altered states of consciousness. Moreover, spontaneous signal diversity was significantly higher when participants had eyes open compared to eyes closed, both during normal wakefulness and during influence of sub-anaesthetic ketamine doses.ConclusionThe results suggest that PCI and spontaneous signal diversity may be complementary and potentially measure different aspects of consciousness. Thus, our results seem compatible with PCI being indicative of the brain’s ability to sustain consciousness, as indicated by previous research, while it is possible that spontaneous EEG signal diversity may be indicative of the complexity of conscious content. The observed sensitivity of the latter measures to visual input seems to support such an interpretation. Thus, sub-anaesthetic ketamine may increase the complexity of both the conscious content (experience) and the brain activity underlying it, while the level, degree, or general capacity of consciousness remains largely unaffected.

Research on Simulation and Modeling of Split Double-Rotor Motor Based on Indirect Vector Control

2011 ◽  
Vol 317-319 ◽  
pp. 672-677
Author(s):  
Bin Wei ◽  
Ji Bin Hu ◽  
Zeng Xiong Peng
Keyword(s):  
Control System ◽  
Vector Control ◽  
Stable State ◽  
System Model ◽  
Dynamic Capability ◽  
Simulation And Modeling ◽  
Simulation Results ◽  
Set Up ◽  
Vector Control System ◽  
Control Principle

A novel vector control system of the split double-rotor motor based on indirect vector control principle has been proposed. The mathematic models of the primary machine and the secondary machine are set up respectively. Through the coupling of the two models, the system model of the split double-rotor motor is build. Matlab software is used for the simulation and analysis of this indirect vector control system. According to the simulation results, the validity of system model is proved with nicer static and dynamic capability, and without steady-state error in stable state. These are important to analyze and design the double-rotor motor. Furthermore, the results provide the basis for simulation and apply of double-rotor motor in HEV.

scholarly journals An Order-Independent Algorithm for Learning Chain Graphs

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Mohammad Ali Javidian ◽  
Marco Valtorta ◽  
Pooyan Jamshidi
Keyword(s):  
Directed Acyclic Graphs ◽  
High Dimensional ◽  
P Values ◽  
Order Dependence ◽  
Error Measures ◽  
Acyclic Graphs ◽  
Simulation Results ◽  
Improved Performance ◽  
Low Dimensional ◽  
Chain Graphs

LWF chain graphs combine directed acyclic graphs and undirected graphs. We propose a PC-like algorithm, called PC4LWF, that finds the structure of chain graphs under the faithfulness assumption to resolve the problem of scalability of the proposed algorithm by Studeny (1997). We prove that PC4LWF is order dependent, in the sense that the output can depend on the order in which the variables are given. This order dependence can be very pronounced in high-dimensional settings. We propose two modifications of the PC4LWF algorithm that remove part or all of this order dependence. Simulation results with different sample sizes, network sizes, and p-values demonstrate the competitive performance of the PC4LWF algorithms in comparison with the LCD algorithm proposed by Ma et al. (2008) in low-dimensional settings and improved performance (with regard to error measures) in high-dimensional settings.

scholarly journals EEG data processing in ADHD diagnosis and neurofeedback

2020 ◽  
Vol 40 (3) ◽  
pp. 116-123
Author(s):  
Zoran Šverko ◽  
Ivan Markovinović ◽  
Miroslav Vrankić ◽  
Saša Vlahinić
Keyword(s):  
Data Processing ◽  
Mean Value ◽  
Adhd Group ◽  
Eeg Signal ◽  
Statistical Features ◽  
Eeg Signals ◽  
New Approach ◽  
Mean Values ◽  
Adhd Diagnosis ◽  
Eeg Data

In this paper, EEG data processing was conducted in order to define the parameters for neurofeedback. A new survey was conducted based on a brief review of previous research. Two groups of participants were chosen: ADHD (3) and nonADHD (14). The main part of this study includes EEG signal data pre-processing and processing. We have outlined statistical features of observed EEG signals such as mean value, grand-mean value and their ratios. It can be concluded that an increase in grand-mean values of power theta-low beta ratio on Cz electrode gives confirmation of previous research. The value of alpha-delta power ratio higher than 1 on C3, Cz, P3, Pz, P4 in ADHD group is proposed as a new approach to classification. Based on these conclusions we will design a neurofeedback protocol as a continuation of this work.

A portable real-time EEG signal acquisition and tele-medicine system

2020 ◽  
pp. 1-11
Author(s):  
Qiang Zhang ◽  
Peng Wang ◽  
Shanshan Li ◽  
Yonghao Jing
Keyword(s):  
Real Time ◽  
Sql Server ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eeg Data ◽  
Sql Server Database ◽  
High Power Consumption ◽  
Machine Interface ◽  
Sd Card

Since electroencephalogram (EEG) signals contain a variety of physiological and pathological information, they are widely used in medical diagnosis, brain machine interface and other fields. The existing EEG apparatus are not perfect due to big size, high power consumption and using cables to transmit data. In this paper, a portable real-time EEG signal acquisition and tele-medicine system is developed in order to improve performance of EEG apparatus. The weak EEG signals are induced to the pre-processing circuits via a noninvasive method with bipolar leads. After multi-level amplifying and filtering, these signals are transmitted to DSP (TMS320C5509) to conduct digital filtering. Then, the EEG signals are displayed on the LCD screen and stored in the SD card so that they can be uploaded to the server through the internet. The server employs SQL Server database to manage patients’ information and to store data in disk. Doctors can download, look up and analyze patients’ EEG data using the doctor client. Experimental results demonstrate that the system can acquire weak EEG signals in real time, display the processed results, save data and carry out tele-medicine. The system can meet the requirement of the EEG signals’ quality, and are easy to use and carry.

scholarly journals QUANTIFICATION OF DEPTH OF ANESTHESIA BY MEANS OF ADAPTIVE CALCULATION OF CORRELATION DIMENSION PARAMETERS

Fractals ◽  
2009 ◽  
Vol 17 (04) ◽  
pp. 473-483
Author(s):  
BEHZAD AHMADI ◽  
BAHAREH ZAGHARI ◽  
RASSOUL AMIRFATTAHI ◽  
MOJTABA MANSOURI
Keyword(s):  
Correlation Dimension ◽  
Single Channel ◽  
Drug Effects ◽  
Eeg Signal ◽  
Depth Of Anesthesia ◽  
Anesthetic Drugs ◽  
Eeg Data ◽  
Prediction Probability ◽  
Analysis Application ◽  
Electroencephalogram Eeg

This paper proposes an approach for quantifying Depth of Anesthesia (DOA) based on correlation dimension (D2) of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room while different anesthetic drugs, including propofol and isoflurane, were used. Correlation dimension was computed using various optimized parameters in order to achieve the maximum sensitivity to anesthetic drug effects and to enable real time computation. For better analysis, application of adaptive segmentation on EEG signal for estimating DOA was evaluated and compared to fixed segmentation, too. Prediction probability (PK) was used as a measure of correlation between the predictors and BIS index to evaluate the proposed methods. Appropriate correlation between DOA and correlation dimension is achieved while choosing (D2) parameters adaptively in comparison to fixed parameters due to the nonstationary nature of EEG signal.

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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