Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing

Rajesh Kumar Tripathy,Ram Bilas Pachori

• Verlag: CRC Press
• Jahr: 2024
• ISBN:
  9781040028803
• Auflage: 1
• Thema: Tecnología e ingeniería
• Thema: Ciencias biomédicas

The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book:

• Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signals
• Presents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interface
• Highlights the latest machine learning and deep learning methods for neural signal processing
• Discusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysis
• Showcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniques

It is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.

Inhaltsverzeichnis

• Cover
• Half Title
• Title
• Copyright
• Contents
• Preface
• Editor Biographies
• List of Contributors
• Chapter 1 Introduction to EEG Signal Recording and Processing
• Chapter 2 Artificial Intelligence-Enabled EEG Signal Processing-Based Detection of Epileptic Seizures
• Chapter 3 Classification of Normal and Alcoholic EEG Signals Using Signal Processing and Machine Learning
• Chapter 4 Empirical Wavelet Transform and Gradient Boosted Learners for Automated Classification of Epileptic Seizures from EEG Signals
• Chapter 5 Automated Emotion Recognition from EEG Signals Using Machine Learning Algorithms in the Field of Ambient Assisted Living
• Chapter 6 Automated Recognition of Human Emotions from EEG Signals Using Signal Processing and Machine Learning Techniques
• Chapter 7 Automatic Emotion Detection by EEG Analysis Using Graph Signal Processing
• Chapter 8 Automated Detection of Alzheimer’s Disease Using EEG Signal Processing and Machine Learning
• Chapter 9 A Regularized Riemannian Intelligent System for Dementia Screening Using Magnetoencephalography Signals
• Chapter 10 Detecting Dementia Using EEG Signal Processing and Machine Learning
• Chapter 11 EEG Signal Processing-Driven Machine Learning for Cognitive Task Recognition
• Chapter 12 Detection of Stress Levels During the Stroop Color-Word Test Using Multivariate Projection-Based MUSIC Domain EWT of Multichannel EEG Signal and Machine Learning
• Index