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Artificial Intelligence-Based Brain-Computer Interface
- 1st Edition - February 4, 2022
- Editors: Varun Bajaj, G. R. Sinha
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 1 9 7 - 9
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 4 1 2 - 3
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 mod… Read more
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Request a sales quoteArtificial 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.
- 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
Academics (scientists, researchers, MSc. PhD. students) from the fields of Computer Science, Artificial Intelligence, Neural Engineering, and Information Technology. The audience also includes Neurologists who are interested in Brain-Computer Interface
- Cover
- Title page
- Table of Contents
- Copyright
- Contributors
- 1: Multiclass sleep stage classification using artificial intelligence based time-frequency distribution and CNN
- Abstract
- 1.1: Introduction
- 1.2: Materials and methods
- 1.3: Results
- 1.4: Discussion
- 1.5: Conclusions
- References
- 2: A comprehensive review of the movement imaginary brain-computer interface methods: Challenges and future directions
- Abstract
- 2.1: Introduction
- 2.2: PRISMA guideline
- 2.3: Results
- 2.4: Discussion
- 2.5: Conclusion and future scope
- References
- 3: A new approach to feature extraction in MI-based BCI systems
- Abstract
- 3.1: Introduction
- 3.2: Types and applications
- 3.3: BSS and its application in BCI
- 3.4: Related work
- 3.5: Proposed method
- 3.6: Computer simulation and result
- 3.7: Discussion
- 3.8: Conclusion
- References
- 4: Evaluation of power spectral and machine learning techniques for the development of subject-specific BCI
- Abstract
- 4.1: Introduction
- 4.2: Materials
- 4.3: Methods
- 4.4: Performance verification
- 4.5: Parameters selection
- 4.6: Results
- 4.7: Discussions
- 4.8: Conclusion
- Conflicts of interest
- References
- 5: Concept of AI for acquisition and modeling of noninvasive modalities for BCI
- Abstract
- 5.1: Introduction
- 5.2: Electroencephalogram
- 5.3: Artificial intelligence for signal analysis
- 5.4: Communication interface between brain and machine
- 5.5: Methodology
- 5.6: Results
- 5.7: Discussion and future scope
- 5.8: Conclusion
- References
- 6: Bi-LSTM-deep CNN for schizophrenia detection using MSST-spectral images of EEG signals
- Abstract
- 6.1: Introduction
- 6.2: Methods and materials
- 6.3: Results and discussion
- 6.4: Conclusions and recommendations
- References
- 7: Detection of epileptic seizure disorder using EEG signals
- Abstract
- 7.1: Introduction
- 7.2: Background on EEG signals
- 7.3: Machine learning approaches for epilepsy
- 7.4: Deep learning approaches for epilepsy
- 7.5: Related work and comparative analysis
- 7.6: Conclusion
- References
- 8: Customized deep learning algorithm for drowsiness detection using single-channel EEG signal
- Abstract
- 8.1: Introduction
- 8.2: Drowsiness detection (DD)
- 8.3: Database
- 8.4: Proposed methodology
- 8.5: Experimental results
- 8.6: Conclusion
- References
- 9: EEG-based deep learning neural net for apnea detection
- Abstract
- 9.1: Introduction
- 9.2: Database
- 9.3: Proposed methodology
- 9.4: Experimental results
- 9.5: Conclusion
- References
- 10: Classification of mental states from rational dilation wavelet transform and bagged tree classifier using EEG signals
- Abstract
- 10.1: Introduction
- 10.2: Methodology
- 10.3: Results and discussions
- 10.4: Conclusions
- References
- 11: A novel metaheuristic optimization method for robust spatial filter designation and classification of speech imagery tasks in EEG Brain-Computer Interface
- Abstract
- Acknowledgments
- 11.1: Introduction
- 11.2: Experiments
- 11.3: Robust spatial filter designation in multisession, multitrial EEG data
- 11.4: Adaptive network-based fuzzy inference system
- 11.5: Experimental results
- 11.6: Conclusion and future work
- References
- 12: Variational mode decomposition-based finger flexion detection using ECoG signals
- Abstract
- 12.1: Introduction
- 12.2: Variational mode decomposition
- 12.3: Features studied
- 12.4: Support vector machine
- 12.5: Methodology
- 12.6: Proposed method
- 12.7: Results and discussion
- 12.8: Conclusion
- References
- 13: 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
- Abstract
- Acknowledgments
- 13.1: Introduction
- 13.2: Literature survey of the BCI system
- 13.3: Review of the signal preprocessing, feature extraction and selection, and classification techniques for a BCI system
- 13.4: Hardware analysis of BCI focusing on the design of low power OTA and its Gm-C based filter for EEG signal processing
- 13.5: Conclusion
- References
- 14: Deep autoencoder-based automated brain tumor detection from MRI data
- Abstract
- 14.1: Introduction
- 14.2: Methodology, dataset, and techniques
- 14.3: Classification
- 14.4: Performance metrics
- 14.5: Experimental studies
- 14.6: Conclusion
- References
- 15: Measure the superior functionality of machine intelligence in brain tumor disease prediction
- Abstract
- Acknowledgments
- 15.1: Introduction
- 15.2: Related work
- 15.3: Materials and methods
- 15.4: Results and discussion
- 15.5: Conclusion
- References
- Index
- No. of pages: 392
- Language: English
- Edition: 1
- Published: February 4, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323911979
- eBook ISBN: 9780323914123
VB
Varun Bajaj
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.
Affiliations and expertise
Assistant Professor, Indian Institute of Technology, Design, and Manufacturing, Jabalpur, IndiaGS
G. R. Sinha
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.
Affiliations and expertise
Adjunct Professor, International Institute of Information Technology Bengaluru (IIITB), Bangalore, Karnataka, IndiaRead Artificial Intelligence-Based Brain-Computer Interface on ScienceDirect