Full 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.
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.
