Advanced Electroencephalography Analytical Methods - Naik, Ganesh; Santos, Wellington Pinheiro dos; Gargiulo, Gaetano; (ed.) - Prospero Internet Bookshop
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Advanced Electroencephalography Analytical Methods

Fundamentals, Acquisition, and Applications
 
Edition number: 1
Publisher: CRC Press
Date of Publication:
 
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Short description:

This book presents the theoretical basis and applications of electroencephalography (EEG) signals in neuroscience, involving signal analysis, processing, signal acquisition, representation, and applications of EEG signal analysis using non-linear approaches and machine learning.

Long description:

Advanced Electroencephalography Analytical Methods: Fundamentals, Acquisition, and Applications presents the theoretical basis and applications of electroencephalography (EEG) signals in neuroscience, involving signal analysis, processing, signal acquisition, representation, and applications of EEG signal analysis using non-linear approaches and machine learning. It explains principles of neurophysiology, linear signal processing, computational intelligence, and the nature of signals including machine learning. Applications involve computer-aided diagnosis, brain-computer interfaces, rehabilitation engineering, and applied neuroscience.


This book:




  • Includes a comprehensive review on biomedical signals nature and acquisition aspects.



  • Focuses on selected applications of neuroscience/cardiovascular/muscle-related biomedical areas.




  • Provides a machine learning update to a classical biomedical signal processing approach.



  • Explains deep learning and application to biomedical signal processing and analysis.



  • Explores relevant biomedical engineering and neuroscience state-of-the-art applications.


This book is intended for researchers and graduate students in biomedical signal processing, electrical engineering, neuroscience, and computer science.

Table of Contents:

1. Diagnostic applications of EEG signal patterns in Neuroscience.  2. Deep Learning Techniques for Automatic Sleep Pattern Identification and Disorder Evaluation using EEG Signals.  3. Recent Trends in EEG-Based MI and SSVEP Brain-Computer Interface Applications - A Review.  4. Recent Trends in EEG-Based P300, Neuromarketing and E-sports Brain-Computer Interface Applications - A Review.  5. Significance of Fourier Transform for Epileptic EEG Signals Analysis.  6. Alternative treatment with nonperiodic acoustic stimulation for pharmacoresistant epileptic patients: an exploratory study.  7. Artifacts removal in Electroencephalogram (EEG) signals.  8. Multi-channel and multi-label decision-making system (MCL-DMS) for sleep stage and sleep disorder recognition from EEG signals.  9. Analyzing and Decoding Natural Reach & Grasp Action Using Convolutional Neural Network.  10. Classification of motor imagery EEG signals based on sparse representations of Empirical Mode Decomposition features.  11. Prediction of Onset of Seizures from EEG signals using ML Techniques.