A Comprehensive Analysis On Eeg Signal Classification Using Advanced Computational Analysis
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A Comprehensive Analysis On Eeg Signal Classification Using Advanced Computational Analysis
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Author : Kaushik Bhimraj
language : en
Publisher:
Release Date : 2017
A Comprehensive Analysis On Eeg Signal Classification Using Advanced Computational Analysis written by Kaushik Bhimraj and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Electronic dissertations categories.
Author's abstract: Electroencephalogram (EEG) has been used in a wide array of applications to study mental disorders. Due to its non-invasive and low-cost features, EEG has become a viable instrument in Brain-Computer Interfaces (BCI). These BCI systems integrate user's neural features with robotic machines to perform tasks. However, due to EEG signals being highly dynamic in nature, BCI systems are still unstable and prone to unanticipated noise interference. An important application of this technology is to help facilitate the lives of the tetraplegic through assimilating human brain impulses and converting them into mechanical motion. However, BCI systems are remarkably challenging to implement as recorded brain signals can be unreliable and vary in pattern throughout time. In the initial work, a novel classifier structure is proposed to classify different types of imaginary motions (left hand, right hand, and imagination of words starting with the same letter) across multiple sessions using an optimized set of electrodes for each user. The proposed technique uses raw brain signals obtained utilizing 32 electrodes and classifies the imaginary motions using Artificial Neural Networks (ANN). To enhance the classification rate and optimize the set of electrodes of each subject, a majority voting system combining a set of simple ANNs is used. This electrode optimization technique achieved classification accuracies of 69.83%, 94.04% and 84.56% respectively for the three subjects considered in this work. In the second work, the signal variations are studied in detail for a large EEG dataset. Using the Independent Component Analysis (ICA) with a dynamic threshold model, noise features were filtered. The data was classified to a high precision of more than 94% using artificial neural networks. A decreased variance in classification validated both, the effectiveness of the proposed dynamic threshold systems and the presence of higher concentrations of noise in data for specific subjects. Using this variance and classification accuracy, subjects were separated into two groups. The lower accuracy group was found to have an increased variance in classification. To confirm these results, a Kaiser windowing technique was used to compute the signal-to-noise ratio (SNR) for all subjects and a low SNR was obtained for all EEG signals pertaining to the group with the poor data classification. This work not only establishes a direct relationship between high signal variance, low SNR, and poor signal classification but also presents classification results that are significantly higher than the accuracies reported by prior studies for the same EEG user dataset.
Eeg Signal Analysis And Classification
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Author : Siuly Siuly
language : en
Publisher: Springer
Release Date : 2017-01-03
Eeg Signal Analysis And Classification written by Siuly Siuly and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-03 with Technology & Engineering categories.
This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use. Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developed methodologies that have been tested on several real-time benchmark databases. This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals. /div
Eeg Signal Processing
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Author : Saeid Sanei
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-28
Eeg Signal Processing written by Saeid Sanei and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-28 with Science categories.
Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services. Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods. Additionally, expect to find: explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals; an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs; reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals; coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon; descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing. The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.
2021 9th International Conference On Reliability Infocom Technologies And Optimization Trends And Future Directions Icrito
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Author : IEEE Staff
language : en
Publisher:
Release Date : 2021-09-03
2021 9th International Conference On Reliability Infocom Technologies And Optimization Trends And Future Directions Icrito written by IEEE Staff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-03 with categories.
In this globally competitive environment scientific analysis of system under study is the key issues in attaining market leadership This competitive advantage through quality process, product and services in the market place is possible through the development of knowledge bases and easy access to structured databases on systems, processes and technology based on quantitative study Further due to ever emerging new trends of fashion and taste as well as technology, predicting future with certainty can be the daydream This theme is most appropriate in the current context as well as in the future The Conference will not only take stock of trends and developments at the globally competitive environment, but will also provide future directions to young researchers and practitioners
Advanced Signal Processing And Machine Learning Approaches For Eeg Analysis
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Author :
language : en
Publisher:
Release Date : 2010
Advanced Signal Processing And Machine Learning Approaches For Eeg Analysis written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Electroencephalography (EEG) offers a non-invasive brain-imaging technology with potential to extract user intent from brain signals. This can offer a potential method for dispersed soldiers to communicate silently with one another. The usual interface for acquiring EEG signals may house 128 or more electrodes. Each EEG signal may be sampled at KHz sampling rates and may last for a few seconds. Thus the number of samples used to represent each trial can be large. The goal of this short-term innovative research (STIR) project was to investigate innovative sample and channel (i.e., EEG electrode) selection methods to reduce the storage and computational complexity in analyzing EEG signals. In experiments aimed at determining the redundancy in imagined speech EEG signals, it was observed that EEG data has limited spatial redundancy, but large temporal redundancy. In another set of experiments, we investigated the classification of two imagined speech syllables (namely "Ba" and "Ku") from imagined speech EEG signals. Using all good channels, only one of the seven volunteer subjects produced "better than chance" classification accuracy of about 60%. By selecting specific electrodes, two subjects yielded better-than-chance results with recognition rates close to 60% for all trials. Overall classification rates appear to have improved slightly by the selection of electrodes, indicating that imagined speech classification performance can be improved by careful selection of EEG electrodes.
Comprehensive Analysis Of Extreme Learning Machine And Continuous Genetic Algorithm For Robust Classification Of Epilepsy From Eeg Signals
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Author : Harikumar Rajaguru
language : en
Publisher: Anchor Academic Publishing
Release Date : 2017
Comprehensive Analysis Of Extreme Learning Machine And Continuous Genetic Algorithm For Robust Classification Of Epilepsy From Eeg Signals written by Harikumar Rajaguru and has been published by Anchor Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Computers categories.
Epilepsy is a common and diverse set of chronic neurological disorders characterized by seizures. It is a paroxysmal behavioral spell generally caused by an excessive disorderly discharge of cortical nerve cells of the brain. Epilepsy is marked by the term “epileptic seizures”. Epileptic seizures result from abnormal, excessive or hyper-synchronous neuronal activity in the brain. About 50 million people worldwide have epilepsy, and nearly 80% of epilepsy occurs in developing countries. The most common way to interfere with epilepsy is to analyse the EEG (electroencephalogram) signal which is a non-invasive, multi channel recording of the brain’s electrical activity. It is also essential to classify the risk levels of epilepsy so that the diagnosis can be made easier. This study investigates the possibility of Extreme Learning Machine (ELM) and Continuous GA as a post classifier for detecting and classifying epilepsy of various risk levels from the EEG signals. Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used for dimensionality reduction.
Eeg Brain Signal Classification For Epileptic Seizure Disorder Detection
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Author : Sandeep Kumar Satapathy
language : en
Publisher: Academic Press
Release Date : 2019-02-10
Eeg Brain Signal Classification For Epileptic Seizure Disorder Detection written by Sandeep Kumar Satapathy and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-10 with Medical categories.
EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification. Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers Provides a number of experimental analyses, with their results discussed and appropriately validated
Analysis And Classification Of Eeg Signals For Brain Computer Interfaces Data Acquisition Methods For Human Brain Activity
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Author : Szczepan Paszkiel
language : en
Publisher:
Release Date : 2020
Analysis And Classification Of Eeg Signals For Brain Computer Interfaces Data Acquisition Methods For Human Brain Activity written by Szczepan Paszkiel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Brain-computer interfaces categories.
This book addresses the problem of EEG signal analysis and the need to classify it for practical use in many sample implementations of brain-computer interfaces. In addition, it offers a wealth of information, ranging from the description of data acquisition methods in the field of human brain work, to the use of Moore-Penrose pseudo inversion to reconstruct the EEG signal and the LORETA method to locate sources of EEG signal generation for the needs of BCI technology. In turn, the book explores the use of neural networks for the classification of changes in the EEG signal based on facial expressions. Further topics touch on machine learning, deep learning, and neural networks. The book also includes dedicated implementation chapters on the use of brain-computer technology in the field of mobile robot control based on Python and the LabVIEW environment. In closing, it discusses the problem of the correlation between brain-computer technology and virtual reality technology.
Eeg Signal Processing And Feature Extraction
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Author : Li Hu
language : en
Publisher: Springer Nature
Release Date : 2019-10-12
Eeg Signal Processing And Feature Extraction written by Li Hu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-12 with Medical categories.
This book presents the conceptual and mathematical basis and the implementation of both electroencephalogram (EEG) and EEG signal processing in a comprehensive, simple, and easy-to-understand manner. EEG records the electrical activity generated by the firing of neurons within human brain at the scalp. They are widely used in clinical neuroscience, psychology, and neural engineering, and a series of EEG signal-processing techniques have been developed. Intended for cognitive neuroscientists, psychologists and other interested readers, the book discusses a range of current mainstream EEG signal-processing and feature-extraction techniques in depth, and includes chapters on the principles and implementation strategies.
Analysis And Classification Of Electroencephalography Signals
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Author :
language : en
Publisher:
Release Date :
Analysis And Classification Of Electroencephalography Signals written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
EEG signal processing is one of the hottest areas of research in digital signal processing applications and biomedical research. Analysis of EEG signals provides a crucial tool for diagnosis of neurobiological diseases. The problem of EEG signal classification into healthy and pathological cases is primarily a pattern recognition problem using extracted features. Many methods of feature extraction have been applied to extract the relevant characteristics from a given EEG data. The EEG data was collected from a publicly available source. Three types of cases were classified viz. signals recorded from healthy volunteers having their eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures. The feature extraction was done by computing the discrete wavelet transform and spectral analysis using AR model. The wavelet transform coefficients compress the number of data points into few features. Various statistics were used to further reduce the dimensionality. The AR coefficients obtained from burg auto-regressive method provide important features of the EEG signals. Classification of the EEG data using committee neural network provides robust and improved performance over individual members of the committee. F-ratio based dimension reduction technique was used to reduce the number of features without affecting the accuracy much.