1st Edition

Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing

Edited By Rajesh Kumar Tripathy, Ram Bilas Pachori Copyright 2024
    226 Pages 45 B/W Illustrations
    by CRC Press

    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.

    Chapter 1

    Introduction to EEG signal recording and processing

    Kritiprasanna Das, Vivek Kumar Singh, and Ram Bilas Pachori

     

    Chapter 2

    Artificial intelligence (AI)-enabled signal processing-based methods for detection of epileptic seizures using EEG signals

    Abdulhamit Subasi, Muhammed Enes Subasi, Emrah Hancer

     

    Chapter 3

    Classification of Normal and Alcoholic EEG signals using signal processing and machine learning models

    Fatima Faraz, Mohammad Ebad Ur Rehman, Gary Tse, Haipeng Liu

     

    Chapter 4

    Empirical Wavelet Transform and Gradient Boosted Learners for Automated Classification of Epileptic Seizures from EEG Signals

     Mohd Faizan Bari, Dilip Singh Sisodia, Arti Anuragi

     

    Chapter 5

    Automated Emotion recognition from EEG signal using machine learning algorithms and its application in the field of Ambient assisted living

    Rohan Mandal, Uday Maji and Saurabh Pal

     

    Chapter 6

    Automated emotion recognition from EEG signals using signal processing and machine learning techniques

    Abdulhamit Subasi, Tuba Nur Subasi, Oznur Ozaltin

     

     

    Chapter 7

    Automatic emotion detection by EEG analysis using Graph signal processing

    Ramnivas Sharma, Hemant Kumar Meena

     

    Chapter 8

    Automated detection of Alzheimer's disease from EEG signal using signal processing and machine learning-based method

     

    Mahbuba Ferdowsi, Haipeng Liu, Ban-Hoe Kwan , Choon-Hian Goh 

     

    Chapter 9

    Title: Regularized Riemannian based Intelligent System for Dementia Screening using MEG Signals

    Srikireddy Dhanunjay Reddy, Shubhangi Goyal, Tharun Kumar Reddy Bollu

     

     

    Chapter 10

    Detection of dementia from EEG signals using signal processing and machine learning-based techniques

    Mahbuba Ferdowsi , Choon-Hian Goh , Gary Tse , Haipeng Liu 

     

    Chapter 11

    Signal Processing driven Machine Learning for Cognitive task recognition using EEG

    Siran Wang, Brian Lee , Gary Tse, Haipeng Liu 

     

    Chapter 12

     Detection of Stress Levels during Stroop Color-Word Test using Multivariate Projection-based MUSIC Domain EWT of Multichannel EEG Signal and Machine Learning

    Shaswati Dash, Rajesh Kumar Tripathy, Satrujit Mishra, Ram Bilas Pachori

     

     

     

     

     

    Biography

    Rajesh Kumar Tripathy received a B.Tech degree in Electronics and Telecommunication Engineering from the Biju Patnaik University of Technology (BPUT), Odisha, India, in 2009; and the M.Tech degree in Biomedical Engineering from the National Institute of Technology (NIT) Rourkela, Rourkela, India, in 2013; and a Ph.D. degree in machine learning for biomedical signal processing from the Indian Institute of Technology (IIT) Guwahati, Guwahati, India in 2017. He worked as an Assistant Professor at the Faculty of Engineering and Technology (FET), Siksha `O' Anusandhan Deemed to be University from March 2017 to June 2018. Since July 2018, he has worked as an Assistant Professor in the Department of Electrical and Electronics Engineering (EEE), Birla Institute of Technology and Science (BITS), Pilani, Hyderabad Campus. His research interests are machine learning, deep learning, biomedical signal processing, sensor data processing, medical image processing, and the Internet of Things (IoT) for healthcare. He has published research papers in reputed international journals and conferences. He has served as a reviewer for more than 15 scientific journals and served as a technical program committee (TPC) member in various national and international conferences. He is an associate editor for IEEE Access and Frontier in Physiology journals.

    Ram Bilas Pachori received a B.E. degree with honours in electronics and communication engineering from Rajiv Gandhi Technological University, Bhopal, India, in 2001, and M.Tech. and Ph.D. degrees in electrical engineering from IIT Kanpur, India, in 2003 and 2008, respectively. Before joining the IIT Indore, India, faculty, he was a postdoctoral fellow at the Charles Delaunay Institute, University of Technology of Troyes, France (2007-2008) and an Assistant Professor at the Communication Research Center, International Institute of Information Technology, Hyderabad, India (2008-2009). He was an assistant professor (2009-2013) and an associate professor (2013-2017) at the Department of Electrical Engineering, IIT Indore, where he has now been a Professor since 2017. He is also associated with the Center for Advanced Electronics, IIT Indore. He was a visiting professor at the Department of Computer Engineering, Modeling, Electronics and Systems Engineering, University of Calabria, Rende, Italy, in July 2023; Faculty of Information & Communication Technology, University of Malta, Malta, from June 2023 to July 2023; Neural Dynamics of Visual Cognition Lab, Free University of Berlin, Germany, from July 2022 to September 2022; School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Malaysia, from 2018 to 2019. Previously, he was a Visiting Scholar at the Intelligent Systems Research Center, Ulster University, Londonderry, UK, in December 2014. His research interests include signal and image processing, biomedical signal processing, non-stationary signal processing, speech signal processing, brain-computer interface, machine learning, and artificial intelligence and the Internet of Things in health care. He is an Associate Editor of Electronics Letters, IEEE Transactions on Neural Systems and Rehabilitation Engineering, and Biomedical Signal Processing and Control, and an Editor of IETE Technical Review. He is a Fellow of IETE, IEI, and IET. He has 307 publications: journal articles (189), conference papers (82), books (10), and book chapters (26). He has also eight patents, including one Australian patent (granted) and seven Indian patents (published). His publications have been cited approximately 15,000 times with an h-index of 66 according to Google Scholar.