Artificial Intelligence-Based Brain-Computer Interface

  • 7h 24m
  • G. R. Sinha, Varun Bajaj
  • Elsevier Science and Technology Books, Inc.
  • 2022

Artificial Intelligence-Based Brain Computer Interface provides concepts of AI for the modeling of non-invasive modalities of medical signals such as EEG, MRI and FMRI. These modalities and their AI-based analysis are employed in BCI and related applications. The book emphasizes the real challenges in non-invasive input due to the complex nature of the human brain and for a variety of applications for analysis, classification and identification of different mental states. Each chapter starts with a description of a non-invasive input example and the need and motivation of the associated AI methods, along with discussions to connect the technology through BCI.

Major topics include different AI methods/techniques such as Deep Neural Networks and Machine Learning algorithms for different non-invasive modalities such as EEG, MRI, FMRI for improving the diagnosis and prognosis of numerous disorders of the nervous system, cardiovascular system, musculoskeletal system, respiratory system and various organs of the body. The book also covers applications of AI in the management of chronic conditions, databases, and in the delivery of health services.atient health conditions can be gathered anytime and anywhere outside of traditional clinical settings, hence saving time, money and even lives.

  • Provides readers with an understanding of key applications of Artificial Intelligence to Brain-Computer Interface for acquisition and modelling of non-invasive biomedical signal and image modalities for various conditions and disorders
  • Integrates recent advancements of Artificial Intelligence to the evaluation of large amounts of clinical data for the early detection of disorders such as Epilepsy, Alcoholism, Sleep Apnea, motor-imagery tasks classification, and others
  • Includes illustrative examples on how Artificial Intelligence can be applied to the Brain-Computer Interface, including a wide range of case studies in predicting and classification of neurological disorders

About the Author

Dr. Varun Bajaj is an Assistant Professor in the Discipline of Electronics and Communication, PDPM, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India. His main areas of research are Signal Processing Applications in Biomedical Engineering, Time-Frequency Analysis, Artificial Intelligence, and Brain-Computer Interface. Dr. Bajaj is the author of Analysis ofMedical Modalities for Improved Diagnosis in Modern Healthcare from CRC Press, Modelling and Analysis of Active Biopotential Signals in Healthcare, Volumes 1 and 2from Iop Publishing Ltd, and Computer-Aided Design and Diagnosis Methods for Biomedical Applications from CRC Press.

Dr. G R Sinha is a Professor at Myanmar Institute of Information Technology (MIIT) Mandalay, Myanmar. To his credit are 255 research papers, book chapters, and books, including Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare, Biomedical Signal Processing for Healthcare Applications, Brain and Behavior Computing, and Data Science and Its Applications from Chapman and Hall/CRC Press, Advances in Biometrics from Springer, and Cognitive Informatics, Volumes 1 and 2, AI-Based Brain Computer Interfaces, and Data Deduplication Approaches from Elsevier Academic Press. He was Dean of Faculty and an Executive Council Member of CSVTU and has served as Distinguished Speaker in the field of Digital Image Processing for the Computer Society of India. His research interests include Applications of Machine Learning and Artificial Intelligence in Medical Image Analysis, Biomedical Signal Analysis, Computer Aided Diagnosis, Computer Vision, and Cognitive Science.

In this Book

  • Multiclass Sleep Stage Classification using Artificial Intelligence based Time-Frequency Distribution and CNN
  • A Comprehensive Review of the Movement Imaginary Brain-Computer Interface Methods: Challenges and Future Directions
  • A New Approach to Feature Extraction in MI-Based BCI Systems
  • Evaluation of Power Spectral and Machine Learning Techniques for the Development of Subject-Specific BCI
  • Concept of AI for Acquisition and Modeling of Noninvasive Modalities for BCI
  • Bi-LSTM-Deep CNN for Schizophrenia Detection using MSST-Spectral Images of EEG Signals
  • Detection of Epileptic Seizure Disorder using EEG Signals
  • Customized Deep Learning Algorithm for Drowsiness Detection using Single-Channel EEG Signal
  • EEG-Based Deep Learning Neural Net for Apnea Detection
  • Classification of Mental States from Rational Dilation Wavelet Transform and Bagged Tree Classifier using EEG Signals
  • A Novel Metaheuristic optimization Method for Robust Spatial Filter Designation and Classification of Speech Imagery Tasks in EEG Brain-Computer Interface
  • Variational Mode Decomposition-Based Finger Flexion Detection using ECoG Signals
  • An Insight into the Hardware and Software Aspects of a BCI System with Focus on Ultra-Low Power Bulk Driven OTA and Gm-C Based Filter Design, and a Detailed Review of the Recent AI/ML Techniques
  • Deep Autoencoder-Based Automated Brain Tumor Detection from MRI Data
  • Measure the Superior Functionality of Machine Intelligence in Brain Tumor Disease Prediction