Toward Brain-Computer Interfacing

Overview

Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain-computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only. BCI research may someday provide a communication channel for patients with severe physical disabilities but intact cognitive functions, a working tool in computational neuroscience that contributes to a better understanding of the brain, and a novel independent interface for human-machine communication that offers new options for monitoring and control. This volume presents a timely overview of the latest BCI research, with contributions from many of the important research groups in the field. The book covers a broad range of topics, describing work on both noninvasive (that is, without the implantation of electrodes) and invasive approaches. Other chapters discuss relevant techniques from machine learning and signal processing, existing software for BCI, and possible applications of BCI research in the real world.

Table of Contents

  1. Foreward
  2. Preface
  3. 1. An Introduction to Brain-Computer Interfacing
  4. I. BCI Systems and Approaches
  5. 2. Noninvasive Brain-Computer Interface Research at the Wadsworth Center
  6. 3. Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach
  7. 4. Graz-Brain-Computer Interface: State of Research
  8. 5. The Berlin Brain-Computer Interface: Machine Learning-Based Detection of User Specific Brain States
  9. 6. The IDIAP Brain-Computer Interface: An Asynchronous Multiclass Approach
  10. 7. Brain Interface Design for Asynchronous Control
  11. II. Invasive BCI Approaches
  12. 8. Electrocorticogram as a Brain-Computer Interface Signal Source
  13. 9. Probabilistically Modeling and Decoding Neural Population Activity in Motor Cortex
  14. 10. The Importance of Online Error Correction and Feed-Forward Adjustments in Brain-Machine Interfaces for Restoration of Movement
  15. 11. Advances in Cognitive Neural Prosthesis: Recognition of Neural Data with an Information-Theoretic Objective
  16. 12. A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities
  17. III. BCI Techniques
  18. 13. General Signal Processing and Machine Learning Tools for BCI Analysis
  19. 14. Classifying Event-Related Desynchronization in EEG, ECoG, and MEG Signals
  20. 15. Classification of Time-Embedded EEG Using Short-Time Principal Component Analysis
  21. 16. Noninvasive Estimates of Local Field Potentials for Brain-Computer Interfaces
  22. 17. Error-Related EEG Potentials in Brain-Computer Interfaces
  23. 18. Adaptation in Brain-Computer Interfaces
  24. 19. Evaluation Criteria for BCI Research
  25. IV. BCI Software
  26. 20. BioSig: An Open-Source Software Library for BCI Research
  27. 21. BCI2000: A General-Purpose Software Platform for BCI
  28. V. Applications
  29. 22. Brain-Computer Interfaces for Communication and Motor Control--Perspectives on Clinical Applications
  30. 23. Combining BCI and Virtual Reality: Scouting Virtual Worlds
  31. 24. Improving Human Performance in a Real Operating Environment through Real-Time Mental Workload Detection
  32. 25. Single-Trial Analysis of EEG during Rapid Visual Discrimination: Enabling Cortically Coupled Computer Vision
  33. References
  34. Contributors
  35. Index