Artificial Intelligence Applications for Brain–Computer Interfaces

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(Artificial Intelligence Applications in Healthcare and Medicine)

Editors: Abdulhamit Subasi, Saeed Mian Qaisar, Akash Kumar Bhoi, Parvathaneni Naga Srinivasu

Published: January, 2025
Pages: 348
ISBN: 9780443334146
Paperback
Publisher: Academic Press

Description:

Artificial Intelligence Applications for Brain-Computer Interfaces explores advancements, challenges, and future prospects of noninvasive brain-computer interfaces (BCIs). It covers multimodal signal processing, integrated computation-acquisition devices, and implantable neuro techniques.

This book provides cross-disciplinary research on BCI applications, including telerehabilitation, emotion recognition, neuro-rehabilitation, cognitive workload assessments, and ambient assisted living solutions. Each chapter explains how BCIs connect the brain with external devices, analyzing neural signals to extract insights using multiple noninvasive wearable sensors. The processed sensor data is interpreted using machine-intelligent models to draw meaningful inferences.

Each chapter begins with the importance, problem statement, and motivation. It describes the proposed methodology and discusses related work. The book is a valuable resource for researchers, health professionals, postgraduate students, postdoctoral researchers, and academicians in BCI, prosthetics, computer vision, and mental state estimation.

Table of Contents:

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

Key Features of Brain-Computer Interfaces and AI

• Discusses AI-driven advancements, challenges, and prospects of noninvasive BCIs.

• Covers multimodal signal processing, integrated computation-acquisition devices, and implantable technologies.

• Explores theories, algorithms, and applications shaping modern BCI designs.

• Highlights BCI applications in telerehabilitation, neuro-rehabilitation, and cognitive workload assessment.

• Enhances understanding of BCI technology in various real-world applications.

Who Should Read This Book?

• Researchers in biosciences, bioinformatics, and biomedical engineering.

• Computer scientists and engineers specializing in AI and BCI.

• Health professionals working in neuro-rehabilitation and prosthetics.

• Postgraduate students and postdoctoral researchers in neuroscience.

• Academicians exploring AI-driven brain-computer interface applications.

About the Editors

Abdulhamit Subasi

A specialist in Artificial Intelligence, Machine Learning, and Biomedical Signal Processing, Dr. Subasi has authored multiple book chapters and research papers. He has held positions at Georgia Institute of Technology and Effat University and is currently a Professor of Medical Physics at the University of Turku, Finland.

Saeed Mian Qaisar

Dr. Qaisar is the Research & Innovation Department Head at CESI LINEACT, France. A recipient of the Queen Effat Award, he holds two patents and has published extensively. His research interests include signal processing, AI, and biomedical applications.

Akash Kumar Bhoi

An Assistant Professor (Research) at Sikkim Manipal Institute of Technology, India, Dr. Bhoi specializes in biomedical technologies, IoT, and computational intelligence. He has worked at ISTI-CRN, Italy, and is involved in editing books with international publishers.

Parvathaneni Naga Srinivasu

Dr. Srinivasu, a faculty member at Amrita Vishwa Vidyapeetham, India, researches biomedical imaging, image segmentation, encryption, and AI-driven applications. He is a guest editor for leading scientific publications.

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