
Artificial Intelligence Applications for Brain–Computer Interfaces
- 1st Edition - January 10, 2025
- Imprint: Academic Press
- 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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Request a sales quoteArtificial 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.
- Focuses on the advancements, challenges, and prospects for future technologies over noninvasive brain computer interfaces (BCIs), including the processing and analysis of multimodal signals, integrated calculation-acquisition devices, and implantable technologies.
- Presents theories, algorithms, realizations, applications, approaches, and challenges that will have their impact and contribution in the design and development of modern and effective BCIs.
- Assists in understanding the predominance of BCI technology in various applications.
- 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
- Edition: 1
- Published: January 10, 2025
- Imprint: Academic Press
- No. of pages: 348
- Language: English
- Paperback ISBN: 9780443334146
- eBook ISBN: 9780443334153
AS
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
SQ
Saeed Mian Qaisar
AB
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
PS
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.