scholarly journals Complexity Measures for Normal and Epileptic EEG Signals using ApEn, SampEn and SEN

2013 ◽  
pp. 95-101
Author(s):  
Yatindra Kumar ◽  
M. L. Dewal
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Time Series Data ◽  
Epileptic Seizures ◽  
Series Data ◽  
Eeg Signal ◽  
Complexity Measures ◽  
Non Stationary Signal ◽  
The Brain ◽  
Eeg Signal Processing

There are numerous applications of EEG signal processing such as monitoring alertness, coma, and brain death, controlling an aesthesia, investigating epilepsy and locating seizure origin, testing epilepsy drug effects, monitoring the brain development, and investigating mental disorders; where data size is too long and requires long time to observe the data by clinician or neurologist. EEG signal processing techniques can be used effectively in such applications. The configuration of the signal waveform may contain valuable and useful information about the different state of the brain since biological signal is highly random in both time and frequency domain. Thus computerized analysis is necessary. Being a non-stationary signal, suitable analysis is essential for EEG to differentiate the normal EEG and epileptic seizures. The importance of entropy based features to recognize the normal EEGs, and ictal as well as interictal epileptic seizures. Three features, such as, Approximate entropy, Sample entropy, and Spectral entropy are used to take out the quantitative entropy features from the given EEG time series data of various time frames of 0.88s, and 1s .Average value of entropies for epileptic time series is less than non epileptic time series.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

scholarly journals EEG-Based BCI System to Control Prosthesis's Finger Movements

2020 ◽  
Author(s):  
Sofien Gannouni ◽  
Kais Belwafi ◽  
Hatim Aboalsamh ◽  
Basel Alebdi ◽  
Yousef Almassad ◽  
...  
Keyword(s):  
Signal Processing ◽  
Learning Strategy ◽  
Binary Classification ◽  
Severe Disabilities ◽  
Assistive Technologies ◽  
Eeg Signal ◽  
Finger Movements ◽  
One Class Classifier ◽  
The Brain ◽  
Eeg Signal Processing

Abstract Background: The advances in assistive technologies will go a long way towards restoring the mobility of paralyzed and/or amputated limbs. In this paper, we propose a system that adopts the brain-computer interface ( BCI ) technology to control prosthetic fingers by thoughts. To predict the movements of each finger, a complex EEG signal processing algorithms should be applied in order to remove the outliers , to extract feature, to discriminate between the fingers and to control prosthesis's finger. The proposed method discriminates between the five human fingers. So a multi -classification problem based on ensemble of one class-classifier is applied where each classifier predicts the intention to move one finger. At the end, an adapted machine learning strategy is proposed to predict movements of multiple fingers at the same time. Results: The sensitive regions of the brain related to finger movements are identified and located. The proposed EEG signal processing chain, based on ensemble of one class-classifier, reach a classification accuracy of 81 \ % for five subjects according to the online approach. Unlike most of the existing prototypes that allow to control only one single finger and to perform only one movement at a time by the dedicated finger, our proposed system will enable multiple fingers to perform movements simultaneously. Despite that the proposed system classifies a five tasks, the obtained accuracy is too high compared to a binary classification system. Conclusion: The proposed system contributes to the advancement of a prosthetic allowing people with severe disabilities to do the daily tasks easily.

scholarly journals Demystifying signal processing techniques to extract resting-state EEG features for psychologists

2020 ◽  
Vol 6 (3) ◽  
pp. 189-209 ◽  
Author(s):  
Zhenjiang Li ◽  
Libo Zhang ◽  
Fengrui Zhang ◽  
Ruolei Gu ◽  
Weiwei Peng ◽  
...  
Keyword(s):  
Signal Processing ◽  
Resting State ◽  
Neural Dynamics ◽  
Complex Signal ◽  
Eeg Signal ◽  
Microstate Analysis ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques ◽  
Eeg Signal Processing

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

scholarly journals Bayesian Stochastic Neural Network Model for Turbomachinery Damage Prediction

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shuhua Yang ◽  
Xiaomo Jiang ◽  
Shengli Xu ◽  
Xiaofang Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Signal Processing ◽  
Network Model ◽  
Neural Network Model ◽  
Time Series Data ◽  
Series Data ◽  
Stochastic Neural Network ◽  
Damage Prediction ◽  
Multivariate Correlation

Turbomachinery often suffers various defects such as impeller cracking, resulting in forced outage, increased maintenance costs, and reduced productivity. Condition monitoring and damage prognostics has been widely used as an increasingly important and powerful tool to improve the system availability, reliability, performance, and maintainability, but still very challenging due to multiple sources of data uncertainties and the complexity of analytics modeling. This paper presents an intelligent probabilistic methodology for anomaly prediction of high-fidelity turbomachine, considering multiple data imperfections and multivariate correlation. The proposed method adeptly integrates several advanced state-of-the-art signal processing and artificial intelligence techniques: wavelet multi-resolution decomposition, Bayesian hypothesis testing, probabilistic principal component analysis, and fuzzy stochastic neural network modeling. The advanced signal processing is employed to reduce dimensionality and to address multivariate correlation and data uncertainty for damage prediction. Instead of conventionally using raw time series data, principal components are utilized in the establishment of stochastic neural network model and anomaly prediction. Bayesian interval hypothesis testing metric is then presented to quantitatively compare the predicted and measured data for model validation and anomaly evaluation, thus providing a confidence indicator to judge the model quality and evaluate the equipment status. Bayesian method is developed in this study for denoising the raw data with multiresolution wavelet decomposition, quantifying the model accuracy, and assessing the equipment status. The dynamic stochastic neural network model is established via the nonlinear autoregressive moving average with exogenous inputs approach. It seamlessly integrates the fuzzy clustering and independent Bernoulli random function into radial basis function neural network. A natural gradient method based on Kullback-Leibler distance criterion is employed to maximize the log-likelihood loss function. The effectiveness of the proposed methodology and procedure is demonstrated with the 11-variable time series data and the forced outage event of a real-world centrifugal compressor.

scholarly journals Putting the “dynamic” back into dynamic functional connectivity

2017 ◽  
Author(s):  
Stewart Heitmann ◽  
Michael Breakspear
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Large Scale ◽  
Time Series Data ◽  
Temporal Structure ◽  
Series Data ◽  
Dynamic Functional Connectivity ◽  
Time Resolved ◽  
Phase Amplitude ◽  
The Brain

AbstractThe study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term “dynamic functional connectivity” implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems; generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arise under different configurations of local dynamics and inter-system coupling: We show how they generate time series data with nonlinear and/or non-stationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase-amplitude coupling, complexity and flexibility. The code for simulating these dynamics is available in a freeware software platform, the “Brain Dynamics Toolbox”.

Data Preprocessing

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Signal Processing ◽  
Real World ◽  
Null Hypothesis ◽  
Time Series Data ◽  
Nonlinear Behavior ◽  
Surrogate Data ◽  
Series Data ◽  
Linear Behavior

Successful reconstruction of a shadow attractor provides preliminary empirical evidence that a signal isolated from observed time series data may be generated by deterministic dynamics. However, because we cannot reasonably expect signal processing to purge the signal of all noise in practice, and because noisy linear behavior can be visually indistinguishable from nonlinear behavior, the possibility remains that noticeable regularity detected in a shadow attractor may be fortuitously reconstructed from data generated by a linear-stochastic process. This chapter investigates how we can test this null hypothesis using surrogate data testing. The combination of a noticeably regular shadow attractor, along with strong statistical rejection of fortuitous regularity, increases the probability that observed data are generated by deterministic real-world dynamics.

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