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EEG Signal Processing and Feature Extraction

2019-10-12 Medical
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Release : 2019-10-12
Genre : Medical
Kind : eBook
Book Rating : 130/5 ( reviews)

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Book Synopsis EEG Signal Processing and Feature Extraction by : Li Hu

Download or read book EEG Signal Processing and Feature Extraction written by Li Hu. This book was released on 2019-10-12. Available in PDF, EPUB and Kindle. Book excerpt: 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.

EEG Signal Processing

2019-03 Technology & Engineering
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Release : 2019-03
Genre : Technology & Engineering
Kind : eBook
Book Rating : 708/5 ( reviews)

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Book Synopsis EEG Signal Processing by : Wai Yie Leong

Download or read book EEG Signal Processing written by Wai Yie Leong. This book was released on 2019-03. Available in PDF, EPUB and Kindle. Book excerpt: Electroencephalography (EEG) is an electrophysiological monitoring method used to record the brain activity in brain-computer interface (BCI) systems. It records the electrical activity of the brain, is typically non-invasive with electrodes placed along the scalp, requires relatively simple and inexpensive equipment, and is easier to use than other methods. EEG-based BCI methods provide modest speed and accuracy which is why multichannel systems and proper signal processing methods are used for feature extraction, feature selection and feature classification to discriminate among several mental tasks. This edited book presents state of the art aspects of EEG signal processing methods, with an emphasis on advanced strategies, case studies, clinical practices and applications such as EEG for meditation, auditory selective attention, sleep apnoea; person authentication; handedness detection, Parkinson's disease, motor imagery, smart air travel support and brain signal classification.

EEG Signal Processing

2013-05-28 Science
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Release : 2013-05-28
Genre : Science
Kind : eBook
Book Rating : 237/5 ( reviews)

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Book Synopsis EEG Signal Processing by : Saeid Sanei

Download or read book EEG Signal Processing written by Saeid Sanei. This book was released on 2013-05-28. Available in PDF, EPUB and Kindle. Book excerpt: 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.

EEG Signal Analysis and Classification

2017-01-03 Technology & Engineering
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Release : 2017-01-03
Genre : Technology & Engineering
Kind : eBook
Book Rating : 53X/5 ( reviews)

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Book Synopsis EEG Signal Analysis and Classification by : Siuly Siuly

Download or read book EEG Signal Analysis and Classification written by Siuly Siuly. This book was released on 2017-01-03. Available in PDF, EPUB and Kindle. Book excerpt: 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: A Machine Learning Based Framework

2022-01-31 Art
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Release : 2022-01-31
Genre : Art
Kind : eBook
Book Rating : 068/5 ( reviews)

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Book Synopsis EEG SIGNAL PROCESSING: A Machine Learning Based Framework by : R. John Martin

Download or read book EEG SIGNAL PROCESSING: A Machine Learning Based Framework written by R. John Martin. This book was released on 2022-01-31. Available in PDF, EPUB and Kindle. Book excerpt: 1.1 Motivation Analysis of non-stationary and non-linear nature of signal data is the prime talk in signal processing domain today. On employing biomedical equipments huge volume of physiological data is acquired for analysis and diagnostic purposes. Inferring certain decisions from these signals by manual observation is quite tedious due to artefacts and its time series nature. As large volume of data involved in biomedical signal processing, adopting suitable computational methods is important for analysis. Data Science provides space for processing these signals through machine learning approaches. Many more biomedical signal processing implementations are in place using machine learning methods. This is the inspiration in adopting machine learning approach for analysing EEG signal data for epileptic seizure detection.

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