Artificial Intelligence Applications for Brain–Computer Interfaces

1st Edition - January 10, 2025
Editors: Abdulhamit Subasi, Saeed Mian Qaisar, Akash Kumar Bhoi, Parvathaneni Naga Srinivasu
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 3 3 4 1 4 - 6
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 3 3 4 1 5 - 3

Artificial Intelligence Applications for Brain-Computer Interfaces focuses on the advancements, challenges, and prospects of future technologies involving noninvasive brain-com… Read more

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Artificial Intelligence Applications for Brain-Computer Interfaces focuses on the advancements, challenges, and prospects of future technologies involving noninvasive brain-computer interfaces (BCIs). It includes the processing and analysis of multimodal signals, integrated computation-acquisition devices, and implantable neuro techniques.

This book not only provides cross-disciplinary research in BCI but also presents divergent applications on telerehabilitation, emotion recognition, neuro-rehabilitation, cognitive workload assessments, and ambient assisted living solutions.

In 15 chapters, this book describes how BCIs connect the brain with external devices like computers and electronic gadgets. It analyzes the neural signals from the brain to obtain insights from the brain patterns using multiple noninvasive wearable sensors. It gives insight into how sensor outcomes are processed through machine-intelligent models to draw inferences. Each chapter starts with the importance, problem statement, and motivation. A description of the proposed methodology is provided, and related works are also presented.

Each chapter can be read independently, and therefore, the book is a valuable resource for researchers, health professionals, postgraduate students, postdoc researchers, and academicians in the fields of BCI, prosthesis, computer vision, and mental state estimation, and all those who wish to broaden their knowledge in the allied field.

Title of Book
Cover image
Title page
Table of Contents
Copyright
List of contributors
Series preface
Preface
Acknowledgments
Chapter 1. Introduction to brain–computer interface: research trends and applications
Abstract
1.1 Introduction
1.2 The sensing techniques in brain–computer interface
1.3 The preprocessing and feature extraction techniques in brain–computer interfaces
1.4 The application of artificial intelligence in brain–computer interface
1.5 Applications of brain–computer interface
1.6 Conclusion
References
Chapter 2. Preprocessing and feature extraction techniques for brain–computer interface
Abstract
2.1 Introduction
2.2 The preprocessing techniques
2.3 The feature extraction techniques
2.4 Conclusion
References
Chapter 3. Emotional state monitoring and applications with brain–computer interfaces
Abstract
3.1 Introduction
3.2 Brain signal measurement techniques
3.3 Reflections of emotional states on brain activity
3.4 Emotional identification algorithms and models
3.5 Materials and methods
3.6 Brain–computer interface systems
3.7 Integration of emotional recognition abilities into brain–computer interface systems
3.8 Emotional feedback techniques and strategies
3.9 Clinical applications and potential role
3.10 Conclusion and future work
References
Chapter 4. Hand kinematics and decoding hindlimb kinematics using local field potentials using a deep neural network decoding framework
Abstract
4.1 Introduction
4.2 Hand kinematics and decoding hand limb kinematics using RNN
4.3 Hand kinematics and decoding hand limb kinematics using LFP and LSTM
4.4 Challenges
4.5 Conclusion
References
Chapter 5. Closed-loop brain–computer interfaces for musculoskeletal impulse prediction
Abstract
5.1 Introduction
5.2 Author contributions
5.3 Motivation
5.4 Closed-loop brain–computer interface
5.5 Electric/magnetic stimulation techniques
5.6 Optogenetics
5.7 Sonogenetics
5.8 Conclusion and future work
References
Chapter 6. Classification of motor imagery tasks in brain–computer interface using ensemble learning
Abstract
6.1 Introduction
6.2 Literature review
6.3 Materials and methods
6.4 Results
6.5 Discussion
6.6 Conclusion
Acknowledgment
References
Chapter 7. The application of brain–computer interface in Alzheimer’s disease studies based on machine learning algorithms
Abstract
7.1 Introduction
7.2 Alzheimer’s disease diagnosis methods
7.3 Machine learning algorithms-based approaches in Alzheimer’s disease
7.4 The effect of brain–computer interface models on Alzheimer’s disease studies based on electroencephalogram
7.5 Brain–computer interface-electroencephalogram signal processing procedure using machine learning algorithms
7.6 Toward the development of brain–computer interface models for Alzheimer’s disease patients
7.7 Discussion
7.8 Conclusion
References
Further reading
Chapter 8. Brain–computer interfaces and deep learning methods for cognitive impairments
Abstract
8.1 Introduction
8.2 Cognitive impairments
8.3 Traditional approaches in cognitive impairment management
8.4 Integration of brain–computer interface and deep learning for cognitive rehabilitation
8.5 Research methodology
8.6 Results
8.7 Discussion
8.8 Conclusion and future studies
References
Chapter 9. Prospects and challenges in decoding consumer behavior using neurotechnology
Abstract
9.1 Introduction
9.2 Cognitive processes in consumer behavior
9.3 Measurement techniques in consumer behavior analysis
9.4 Statistical learning in consumer behavior analysis
9.5 Transformative potential of artificial intelligence-enabled neurotechnology
9.6 Challenges in the application of neurotechnology for decoding consumer behavior
9.7 Roadmap and outlook for consumer behavior research
9.8 Conclusion
References
Chapter 10. Electroencephalography-based emotion recognition with empirical mode decomposition and ensemble machine learning methods
Abstract
10.1 Introduction
10.2 Literature review
10.3 Materials and methods
10.4 Results and discussion
10.5 Discussion
10.6 Conclusion
10.7 Funding
References
Chapter 11. Brain–computer interfaces for security and authentication
Abstract
11.1 Introduction
11.2 Literature review
11.3 Existing brain–computer interface electrophysiologic systems
11.4 Structure of brain–computer interface
11.5 Brain–computer interface technologies
11.6 Brain control signals
11.7 Brain–computer interface applications
11.8 Challenges
11.9 Solutions
11.10 Systematic reviews of proposed methodology for brain–computer interface technology
11.11 Conclusion
References
Chapter 12. A case study on artifical intelligence based data processing in passive brain–computer interface
Abstract
12.1 Introduction
12.2 Motivation
12.3 Contribution
12.4 Materials and methods
12.5 Results
12.6 Future work and limitations
12.7 Conclusions
Acknowledgments
References
Chapter 13. Analyzing eyewitness recognition accuracy using event-related potential and eye-tracking analysis: an experimental investigation
Abstract
13.1 Introduction
13.2 Method
13.3 Results and discussion
13.4 Conclusion
References
Chapter 14. Ambient assisted living through passive brain–computer interface technology for assisting paralyzed people
Abstract
14.1 Introduction
14.2 Literature survey
14.3 Applications
14.4 Environmental interaction
14.5 Ethical considerations
14.6 Mental health and well-being
14.7 Technical consideration
14.8 Interface design and user experience
14.9 Experimental inferences
14.10 Security and privacy
14.11 Innovations and hurdles ahead
14.12 Long-term effects and user adaptation
14.13 Accessibility and affordability
14.14 Future perspective
14.15 Conclusion
Acknowledgments
References
Chapter 15. Challenges and future directions in brain–computer interface research for exoskeletons usage
Abstract
15.1 Introduction to brain–computer interface and exoskeletons
15.2 Theoretical foundations of brain–computer interface
15.3 Exoskeleton design and functionality
15.4 Brain–computer interface-exoskeleton integration
15.5 Clinical applications and user experience
15.6 Technological challenges and solutions
15.7 Advances in machine learning and signal processing
15.8 Ethical and social implications
15.9 Regulatory and standards landscape
15.10 Interdisciplinary collaboration in brain–computer interface-exoskeleton research
15.11 Conclusion
References
Index

Abdulhamit Subasi

Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Processing. His extensive expertise in applying machine learning across diverse domains is evident in his numerous contributions, including the authorship of multiple book chapters, as well as the publication of a substantial body of research in esteemed journals and conferences. His career has spanned various prestigious institutions, including the Georgia Institute of Technology in Georgia, USA, where he served as a dedicated researcher. In recognition of his outstanding research contributions, Subasi received the prestigious Queen Effat Award for Excellence in Research in May 2018. His academic journey includes a tenure as a Professor of computer science at Effat University in Jeddah, Saudi Arabia, from 2015 to 2020. Since 2020, he has assumed the role of Professor of medical physics at the Faculty of Medicine, University of Turku in Turku, Finland

Affiliations and expertise

Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia

Saeed Mian Qaisar

Dr. Qaisar currently holds the position of Research & Innovation Department Head for the South-East Region at CESI LINEACT, located in France. In recognition of his teaching and learning excellence, he was honored with the Queen Effat Award in May 2016. Dr. Qaisar's accomplishments encompass two granted patents, as well as an extensive portfolio of published works spanning journal articles, book chapters, and conference papers. Furthermore, Dr. Qaisar contributes to the academic community as an editor for various international journals and is actively involved in the technical and review committees of several international journals and conferences. His current areas of research focus include signal processing, circuits and systems, artificial intelligence, event-driven systems, biomedical and bioinformatics applications, smart grid technology, energy storage, and sampling theory.

Affiliations and expertise

CESI LINEACT, Lyon, France College of Engineering, Effat University, Jeddah, Saudi Arabia

Akash Kumar Bhoi

Dr. Akash Kumar Bhoi, holds degrees in B.Tech, M.Tech, and Ph.D., and has been contributing to the field of computer science and engineering. He assumed the role of Assistant Professor (Research) at the Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology (SMIT), India, in 2012. In addition to his academic responsibilities, Dr. Bhoi extended his expertise during a research tenure as a Research Associate at the Wireless Networks (WN) Research Laboratory, Institute of Information Science and Technologies, National Research Council (ISTI-CRN) in Pisa, Italy, from January 20, 2021, to January 19, 2022. Dr. Bhoi further serves as the University Ph.D. Course Coordinator for "Research & Publication Ethics (RPE)." He is an active member of professional organizations such as IEEE, ISEIS, and IAENG, and holds associate membership with IEI and UACEE. He plays a significant role as an editorial board member and reviewer for esteemed Indian and international journals and regularly contributes as a reviewer. His research expertise encompasses a wide array of domains, including Biomedical Technologies, the Internet of Things, Computational Intelligence, Antenna technology, and Renewable Energy. Dr. Bhoi has a notable publication record, with multiple papers featured in national and international journals and conferences. Dr. Bhoi has played a pivotal role in the organization of international conferences and workshops, offering his expertise as a key contributor. Currently, he is involved in editing several books in collaboration with international publishers

Affiliations and expertise

Directorate of Research, Gangtok, Sikkim Manipal University, Sikkim, India

Parvathaneni Naga Srinivasu

Parvathaneni Naga Srinivasu has earned his Ph.D. degree at GITAM (Deemed to be University) and his areas of research include Biomedical Imaging, Image Enhancement, Image Segmentation, Object Recognition, Image Encryption, Optimization Algorithms, Soft computing, and Natural Language Processing. He is working as an Assistant Professor at the Department of Computer Science and Engineering, GIT, GITAM (Deemed to be University), Visakhapatnam. He is a member of CSI, IAENG, IARA and a regular reviewer for Scopus indexed journals like JCS and IJAIP, Inderscience. He is a guest editor for the special issues and books that are published by reputed publishers like Bentham Science, Springer, and Elsevier. He is a passionate researcher and his articles have been published in national and international journals alongside conferences.

Affiliations and expertise

Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, India Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Brazil INTI International University, Nilai, Malaysia

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Artificial Intelligence Applications for Brain–Computer Interfaces

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Institutional subscription on ScienceDirect

Abdulhamit Subasi

Saeed Mian Qaisar

Akash Kumar Bhoi

Parvathaneni Naga Srinivasu