Brain-Computer Interfaces : An Educational and Research Textbook.

Format:

Book

Author/Creator:

Vourvopoulos, Athanasios.

Series:

IOP Ebooks Series

Language:

English

Physical Description:

1 online resource (380 pages)

Edition:

1st ed.

Place of Publication:

Bristol : Institute of Physics Publishing, 2025.

Summary:

This book offers a clear, comprehensive introduction to brain-computer interfaces (BCIs), covering the principles, methods, paradigms, and applications that enable direct interaction between brain activity and technology. It is designed to guide students and newcomers from foundational concepts to modern, human-centered BCI systems used in research and real-world settings.

Contents:

Intro

Editor biographies

Athanasios Vourvopoulos

Serafeim Perdikis

List of contributors

List of abbreviations

Chapter Introduction

1.1 Overview of brain-computer interfaces

1.1.1 Definition

1.1.2 Brief History

1.1.3 Users and applications

1.2 Purpose of this book

References

Chapter Experimental apparatus and design

2.1 BCI hardware

2.1.1 Understanding brain signal acquisition

2.1.2 Hardware workflow

2.1.3 One size doesn't fit all

2.2 BCI software

2.2.1 Data acquisition and middleware

2.2.2 Python-based frameworks for BCI development

2.2.3 MATLAB-based BCI toolboxes

2.2.4 Interaction layer: BCI software for VR and robotics

2.2.5 Software availability and educational disclaimer

2.2.6 Trends and outlook

2.3 Experiment design in BCI

2.3.1 Designing experiments

2.3.2 Research ethics

2.3.3 Data collection

2.3.4 Statistical analysis

2.3.5 Concluding remarks

2.4 End of chapter exercises

Chapter Brain-computer interface paradigms

3.1 Categorization of brain-computer interface paradigms

3.1.1 Definition and categorization of BCI paradigms

3.1.2 Main BCI paradigms

3.2 Sensorimotor rhythms

3.2.1 Definition and functional principle of SMR BCI

3.2.2 Neuroscience of SMR BCI

3.3 P300

3.3.1 Introduction to P300 BCIs

3.3.2 Principal components of a P300 brain-computer interface

3.3.3 Signal processing for ERP detection in BCIs

3.4 Evoked and steady-state potentials

3.4.1 VEPs

3.4.2 SSVEPs

3.4.3 cVEPs

3.4.4 Auditory equivalents

3.4.5 Somatosensory equivalents

3.4.6 Conclusion

3.5 Slow cortical potentials, movement-related cortical potentials and contingent negative variation

3.5.1 SCP

3.5.2 Contingent negative variation

3.5.3 MRCP and Bereitschaftspotential

3.6 Passive BCIs.

3.6.1 Introduction and definition

3.6.2 Methodological framework

3.6.3 Applications in real contexts

3.6.4 Critical elements and development challenges

3.6.5 Challenges and future perspectives

3.6.6 Conclusion

3.7 Affective BCIs

3.7.1 Affective frameworks

3.7.2 The neuroscience of emotion

3.7.3 Neural correlates of affect

3.7.4 Affective decoding

3.7.5 Affective BCI interfaces

3.7.6 Affective BCI decoding

3.7.7 Conclusion

3.8 Error potentials

3.8.1 Insights from the neuroscientific literature

3.8.2 Decoding error potentials

3.8.3 Applications for error potentials

3.8.4 Error potentials for continuous control

3.9 Implanted

3.9.1 Brain-machine interfaces

3.9.2 Neural signals recorded with implanted electrodes

3.9.3 Decoding hand movement for BMI control

3.9.4 Decoding hand movement for BCI control

3.10 Speech decoding

3.10.1 Word classification

3.10.2 Sentence decoding

3.10.3 Acoustic decoding

3.10.4 Challenges and future work

3.11 Emerging BCI paradigms

3.11.1 Novel BCI paradigms

3.11.2 Novel interaction concepts

3.12 End of chapter exercises

Bibliography

Chapter Methods for brain-computer interfacing

4.1 Overview of BCI data analysis

4.1.1 Online (ad-hoc) analysis

4.1.2 Offline (post-hoc) analysis

4.1.3 Visualization approaches in BCI

4.2 Pre-processing techniques

4.2.1 Frequency filtering

4.2.2 Spatial filtering

4.2.3 Time-domain smoothing

4.2.4 Downsampling

4.2.5 Epoching

4.2.6 Baselining

4.2.7 Data normalization

4.2.8 Spike detection and sorting

4.2.9 Conclusion

4.3 Feature extraction and selection

4.3.1 Feature extraction

4.3.2 Feature selection and ranking

4.4 Decoding methods

4.4.1 An introduction to machine learning in BCI

4.4.2 Main machine learning decoding methods in BCI.

4.4.3 Pros and cons of each ML pipeline

4.4.4 Necessary cautions when using ML in BCI

4.4.5 Conclusion

4.5 Artifact detection and removal

4.5.1 Rejection versus removal

4.5.2 Artifact types

4.5.3 Artifact removal methods

4.5.4 Validation

4.5.5 Applications of artifact removal

4.5.6 Automated pipelines

4.5.7 Conclusion

4.6 Inverse methods

4.6.1 EEG brain sources

4.6.2 Forward modeling

4.6.3 Inverse methods

4.6.4 Advantages and drawbacks for BCI applications

4.7 End of chapter exercises

Chapter Principles and techniques of brain-computer interaction

5.1 Feedback design in brain-computer interfaces

5.1.1 Feedback modalities

5.1.2 Feedback strategies

5.1.3 Challenges and future directions

5.1.4 Conclusion

5.2 Human-robot interaction in BCI

5.2.1 Classical taxonomy of HRI

5.2.2 Traditional sense-plan-act loop in HRI

5.2.3 HRI and assistive technology

5.2.4 Fusing user and robot intentions

5.3 Hybrid BCI

5.3.1 Principles of hybrid BCI

5.3.2 Application of hybrid BCIs

5.3.3 Technical aspects and challenges of hybrid BCIs

5.3.4 Conclusion and outlook

5.4 End of chapter exercises

Chapter Applications of brain-computer interface

6.1 Brain-computer interface application categories

6.1.1 Categories of BCI applications

6.1.2 End-user groups of BCI

6.2 Communication and accessibility

6.2.1 Enabling communication with assistive technologies

6.2.2 Toward accessible communication with BCI for individuals with severe disabilities

6.2.3 BCI as assistive technology: Integration into AT services

6.2.4 EEG-BCI in disorders of consciousness: assessment, communication, and the detection of covert awareness

6.2.5 Conclusion

6.3 Assistive mobility

6.3.1 Brain-controlled powered wheelchairs.

6.3.2 Other brain-actuated assistive mobility prototypes

6.3.3 Shared-control for BCI-driven mobile robots

6.4 Motor substitution

6.4.1 Motor substitution via neurocontrolled robotic devices

6.4.2 Assistive robotic arms

6.4.3 Supernumerary robotic limbs

6.4.4 Rehabilitative end-effectors

6.4.5 Prosthetics

6.4.6 Conclusion

6.5 BCI-based rehabilitation

6.5.1 Neuroplasticity and implications for BCI-based rehabilitation

6.5.2 BCI-based rehabilitation for post-stroke motor recovery

6.5.3 BCI-based rehabilitation after spinal cord injury

6.5.4 BCI-based cognitive rehabilitation

6.6 Mental state monitoring

6.6.1 Applications of passive BCI for mental state monitoring

6.6.2 Discussion

6.6.3 Conclusion

6.7 End of chapter exercises

Chapter The human in the loop

7.1 Technological potential and ethical challenges

7.1.1 Ethical issue 1: the challenge of cyborgisation

7.1.2 Ethical issue 2: brain data governance

7.1.3 Ethical issue 3: the challenge of agency

7.1.4 Conclusion

7.2 Human-centered design in brain-computer interfaces

7.2.1 Human-centred design in BCI research

7.2.2 Analyzing the context of use and user requirements

7.2.3 Creating design concepts

7.2.4 Evaluation

7.2.5 Iterative design as a core process

7.2.6 The importance of agency, acceptance, and the whole experience

7.2.7 Challenges in human-centred BCI design

7.2.8 Conclusion

7.3 Mutual human-machine learning and co-adaptation in BCI

7.3.1 Mutual learning in BCI

7.3.2 Literature of BCI and BMI training

7.4 Embodied virtual reality in BCI training

7.4.1 Closed-loop systems and multisensory feedback

7.4.2 Sense of embodiment in virtual environments

7.4.3 Conclusion

7.5 End of chapter exercises

Chapter Outlook

8.1 Outlook.

8.1.1 Past: from concept to prototype

8.1.2 Current: integration and translation

8.1.3 Speculative future: emerging neurotechnologies

References.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

0-7503-5916-1