Graimann / Allison / Pfurtscheller Brain-Computer Interfaces

Revolutionizing Human-Computer Interaction
1. Auflage 2010
ISBN: 978-3-642-02091-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 397 Seiten

Reihe: The Frontiers Collection

A brain-computer interface (BCI) establishes a direct output channel between the human brain and external devices. BCIs infer user intent via direct measures of brain activity and thus enable communication and control without movement. This book, authored by experts in the field, provides an accessible introduction to the neurophysiological and signal-processing background required for BCI, presents state-of-the-art non-invasive and invasive approaches, gives an overview of current hardware and software solutions, and reviews the most interesting as well as new, emerging BCI applications. The book is intended not only for students and young researchers, but also for newcomers and other readers from diverse backgrounds keen to learn about this vital scientific endeavour.

Autoren/Hrsg.

Graimann, Bernhard

Pfurtscheller, Gert

Allison, Brendan

Allison, Brendan Z.

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Contributors;10
4;List of Abbreviations;14
5;BrainComputer Interfaces: A Gentle Introduction;16
5.1;1 What is a BCI?;17
5.2;2 How Do BCIs Work?;20
5.2.1;2.1 Measuring Brain Activity (Without Surgery);21
5.2.2;2.2 Measuring Brain Activity (With Surgery);22
5.2.3;2.3 Mental Strategies and Brain Patterns;24
5.2.3.1;2.3.1 Selective Attention;25
5.2.3.2;2.3.2 Motor Imagery;26
5.2.4;2.4 Signal Processing;28
5.3;3 BCI Performance;29
5.4;4 Applications;31
5.5;5 Summary;37
5.6;References;39
6;Brain Signals for BrainComputer Interfaces;43
6.1;1 Introduction;43
6.1.1;1.1 The Need for BCIs;43
6.1.2;1.2 Key Principles;43
6.1.3;1.3 The Origin of Brain Signals Used in BCIs;44
6.2;2 Brain Signals for BCIs and Their Neurophysiological Origins;45
6.2.1;2.1 Brain Signal Features Measured Noninvasively;46
6.2.1.1;2.1.1 Event-related Potentials (ERPs);46
6.2.1.2;2.1.2 Cortical Oscillations;49
6.2.2;2.2 Brain Signal Features Measured from the Cortical Surface;51
6.2.3;2.3 Brain Signal Features Measured Within the Cortex;51
6.2.3.1;2.3.1 Local Field Potentials (LFPs) in the Time Domain;52
6.2.3.2;2.3.2 Local Field Potentials in the Frequency Domain;52
6.2.3.3;2.3.3 Single-Neuron Activity;52
6.3;3 Requirements for Continued Progress;53
6.4;References;54
7;Dynamics of Sensorimotor Oscillations in a Motor Task;61
7.1;1 Introduction;61
7.2;2 EventRelated Potentials Versus ERD/ERS;62
7.3;3 Mu and Beta ERD in a Motor Task;62
7.4;4 Interpretation of ERD and ERS;65
7.5;5 Focal ERD/Surround ERS;66
7.6;6 Induced Beta Oscillations after Termination of a Motor Task;67
7.7;7 Short-Lived Brain States;69
7.8;8 Observation of Movement and Sensorimotor Rhythms;71
7.9;9 Conclusion;73
7.10;References;73
8;Neurofeedback Training for BCI Control;79
8.1;1 Introduction;79
8.2;2 Principles of Neurofeedback;80
8.2.1;2.1 Training of Sensorimotor Rhythms;81
8.2.2;2.2 How Neurofeedback Works;82
8.3;3 Training Paradigms for BCI Control;82
8.3.1;3.1 Training with the Graz-BCI;83
8.3.2;3.2 Impact of Feedback Stimuli;85
8.4;4 Final Considerations;87
8.5;References;89
9;The Graz Brain-Computer Interface;93
9.1;1 Introduction;93
9.2;2 The Graz BCI;93
9.3;3 Motor Imagery as Mental Strategy;95
9.3.1;3.1 Induced Oscillations in Non-attended Cortical Body Part Areas;96
9.3.2;3.2 Induced Beta Oscillations in Attended Cortical Body Part Areas;97
9.3.3;3.3 The Beta Rebound (ERS) and its Importance for BCI;98
9.4;4 Feature Extraction and Selection;99
9.5;5 Frequency Band and Electrode Selection;101
9.6;6 Special Applications of the Graz BCI;102
9.6.1;6.1 Self-Paced Exploration of the Austrian National Library;102
9.6.2;6.2 Simulation of Self-Paced Wheel Chair Movement in a Virtual Environment;103
9.6.3;6.3 Control of Google Earth;105
9.7;7 Future Aspects;106
9.8;References;107
10;BCIs in the Laboratory and at Home: The WadsworthResearch Program;111
10.1;1 Introduction;111
10.2;2 Sensorimotor Rhythm-Based Cursor Control;112
10.3;3 P300-Based Item Selection;116
10.4;4 A BCI System for Home Use;120
10.5;5 SMR-Based Versus P300-Based BCIs;121
10.6;References;123
11;Detecting Mental States by Machine Learning Techniques: The Berlin BrainComputer Interface;126
11.1;1 Introduction;126
11.1.1;1.1 The Machine Learning Approach;126
11.1.2;1.2 Neurophysiological Features;127
11.1.2.1;1.2.1 Readiness Potential;128
11.1.2.2;1.2.2 Sensorimotor Rhythms;129
11.2;2 Processing and Machine Learning Techniques;129
11.2.1;2.1 Common Spatial Patterns Analysis;130
11.2.2;2.2 Regularized Linear Classification;131
11.2.2.1;2.2.1 Mathematical Part;131
11.3;3 BBCI Control Using Motor Paradigms;133
11.3.1;3.1 High Information Transfer Rates;133
11.3.2;3.2 Good Performance Without Subject Training;135
11.3.3;3.3 BCI Illiteracy;136
11.4;4 Applications of BBCI Technology;138
11.4.1;4.1 Prosthetic Control;138
11.4.2;4.2 Time-Critical Applications: Prediction of Upcoming Movements;139
11.4.3;4.3 Neuro Usability;140
11.4.4;4.4 Mental State Monitoring;141
11.4.4.1;4.4.1 Experimental Setup for Attention Monitoring;142
11.4.4.2;4.4.2 Results;143
11.5;5 Conclusion;143
11.6;References;145
12;Practical Designs of BrainComputer Interfaces Based on the Modulation of EEG Rhythms;149
12.1;1 Introduction;149
12.1.1;1.1 BCIs Based on the Modulation of Brain Rhythms;149
12.1.2;1.2 Challenges Confronting Practical System Designs;151
12.2;2 Modulation and Demodulation Methods for Brain Rhythms;152
12.2.1;2.1 Power Modulation/Demodulation of Mu Rhythm;153
12.2.2;2.2 Frequency Modulation/Demodulation of SSVEPs;154
12.2.3;2.3 Phase Modulation/Demodulation of SSVEPs;155
12.3;3 Designs of Practical BCIs;156
12.3.1;3.1 Designs of a Practical SSVEP-based BCI;157
12.3.1.1;3.1.1 Lead Position;157
12.3.1.2;3.1.2 Stimulation Frequency;158
12.3.1.3;3.1.3 Frequency Feature;158
12.3.2;3.2 Designs of a Practical Motor Imagery Based BCI;159
12.3.2.1;3.2.1 Phase Synchrony Measurement;160
12.3.2.2;3.2.2 Electrode Layout;161
12.4;4 Potential Applications;162
12.4.1;4.1 Communication and Control;162
12.4.2;4.2 Rehabilitation Training;163
12.4.3;4.3 Computer Games;164
12.5;5 Conclusion;164
12.6;References;165
13;BrainComputer Interface in Neurorehabilitation;167
13.1;1 Introduction;167
13.2;2 Basic Research;169
13.3;3 BrainComputer Interfaces for Communication in Complete Paralysis;169
13.4;4 BrainComputer Interfaces in Stroke and Spinal Cord Lesions;172
13.5;5 The Emotional BCI;175
13.6;6 Future of BCI in Neurorehabilitation;178
13.7;References;179
14;Non Invasive BCIs for Neuroprostheses Control of the Paralysed Hand;182
14.1;1 Introduction;182
14.1.1;1.1 Spinal Cord Injury;182
14.1.2;1.2 Neuroprostheses for the Upper Extremity;182
14.2;2 Brain-Computer Interface for Control of Grasping Neuroprostheses;185
14.2.1;2.1 Patients;186
14.2.2;2.2 EEG Recording and Signal Processing;188
14.2.3;2.3 Setup Procedures for BCI Control;188
14.2.3.1;2.3.1 BCI-Training of Patient TS Using a Neuroprosthesis with Surface Electrodes;189
14.2.3.2;2.3.2 BCI-Training of Patient HK Using an Implanted Neuroprosthesis;190
14.2.4;2.4 Interferences of Electrical Stimulation with the BCI;190
14.2.5;2.5 Evaluation of the Overall Performance of the BCI Controlled Neuroprostheses;191
14.3;3 Conclusion;191
14.4;References;193
15;BrainComputer Interfaces for Communication and Control in Locked-in Patients;196
15.1;1 Introduction;196
15.2;2 Locked-in the Body and Lock-Out of Society;197
15.3;3 BCI Applications for Locked-in Patients;199
15.4;4 Experiences of a BCI User;202
15.5;5 BCI Training with Patients;205
15.6;6 Conclusion;208
15.7;References;210
16;Intracortical BCIs: A Brief History of Neural Timing;213
16.1;1 Introduction;213
16.2;2 Why Penetrate the Brain?;213
16.3;3 Neurons, Electricity, and Spikes;215
16.4;4 The Road to Imperfection;217
16.5;5 A Brief History of Intracortical BCIs;219
16.6;6 The Holy Grail: Continuous Natural Movement Control;223
16.7;7 What Else Can We Get from Intracortical Microelectrodes?;226
16.8;References;228
17;BCIs Based on Signals from Between the Brain and Skull;230
17.1;1 Introduction;230
17.2;2 Electrocorticogram: Signals from Between the Brain and Skull;230
17.3;3 Advantages of ECoG;231
17.3.1;3.1 Advantages of ECoG Versus EEG;232
17.3.2;3.2 Advantages over Microelectrodes;233
17.3.3;3.3 Everything Affects the Brain;235
17.4;4 Disadvantages of ECoG;235
17.5;5 Successful ECoG-Based BCI Research;237
17.6;6 Past and Present ECoG Research for BCI;238
17.6.1;6.1 ECoG Animal Research;239
17.6.2;6.2 Human ECoG Studies;239
17.6.2.1;6.2.1 Smith-Kettlewell Eye Research Institute;239
17.6.2.2;6.2.2 The University of Michigan -- Ann Arbor (Levine and Huggins);239
17.6.2.3;6.2.3 The University of Washington in St. Louis;242
17.6.2.4;6.2.4 University of Wisconsin -- Madison;243
17.6.2.5;6.2.5 Tuebingen, Germany;244
17.6.2.6;6.2.6 University Hospital of Utrecht;244
17.6.2.7;6.2.7 The University of Michigan -- Ann Arbor (Kipke);244
17.6.2.8;6.2.8 University of Florida -- Gainesville;245
17.6.2.9;6.2.9 Albert-Ludwigs-University, Freiburg, Germany;245
17.7;7 Discussion;245
17.8;References;246
18;A Simple, Spectral-Change Based, Electrocorticographic BrainComputer Interface;249
18.1;1 Introduction;249
18.2;2 Signal Acquisition;249
18.3;3 Feature Selection;254
18.4;4 Feedback;258
18.5;5 Learning;261
18.6;6 Case Study;262
18.7;7 Conclusion;264
18.8;References;264
19;Using BCI2000 in BCI Research;267
19.1;1 Introduction;267
19.1.1;1.1 Proven Components;268
19.1.2;1.2 Documentation;269
19.1.3;1.3 Adaptability;269
19.1.4;1.4 Access;269
19.1.5;1.5 Deployment;269
19.2;2 BCI2000 Design;269
19.2.1;2.1 System Model;270
19.2.2;2.2 Software Components;273
19.2.3;2.3 Interfacing Components;274
19.2.3.1;2.3.1 Data Formats;274
19.2.3.2;2.3.2 Data Exchange;275
19.2.3.3;2.3.3 Matlab Filter Scripts;275
19.2.3.4;2.3.4 Online Data Exchange;276
19.2.3.5;2.3.5 Operator Module Scripting;276
19.2.4;2.4 Important Characteristics of BCI2000;276
19.2.5;2.5 Getting Started with BCI2000;277
19.3;3 Research Scenarios;277
19.3.1;3.1 BCI Classroom;277
19.3.1.1;3.1.1 EEG Hardware;278
19.3.1.2;3.1.2 Software;278
19.3.1.3;3.1.3 Getting Acquainted;278
19.3.1.4;3.1.4 Tutorial Experiments;279
19.3.2;3.2 Performing Psychophysiological Experiments;279
19.3.3;3.3 Patient Communication System;280
19.3.4;3.4 Multi-Site Research;282
19.4;4 Research Trajectories;284
19.5;5 Dissemination and Availability;284
19.6;References;285
20;The First Commercial BrainComputer Interface Environment;288
20.1;1 Introduction;288
20.2;2 Rapid Prototyping Environment;290
20.2.1;2.1 Biosignal Amplifier Concepts;290
20.2.2;2.2 Electrode Caps;296
20.2.3;2.3 Programming Environment;296
20.2.4;2.4 BCI System Architectures;299
20.3;3 BCI Training;300
20.3.1;3.1 Training for a Motor Imagery BCI Approach;300
20.3.2;3.2 Training with a P300 Spelling Device;302
20.4;4 BCI Applications;304
20.4.1;4.1 IntendiX;304
20.4.2;4.2 Virtual Reality Smart Home Control with the BCI;305
20.4.3;4.3 Avatar Control;308
20.5;References;309
21;Digital Signal Processing and Machine Learning;311
21.1;1 Architecture of BCI systems;311
21.2;2 Preprocessing;313
21.2.1;2.1 Spatial Filtering;313
21.2.1.1;2.1.1 Linear Transformations;313
21.2.1.2;2.1.2 Common Average Reference (CAR);314
21.2.1.3;2.1.3 Laplacian Reference;315
21.2.1.4;2.1.4 Principal Component Analysis (PCA);316
21.2.1.5;2.1.5 Independent Component Analysis (ICA);317
21.2.1.6;2.1.6 Common Spatial Patterns (CSP);318
21.2.2;2.2 Temporal Filtering;319
21.3;3 Feature Extraction;319
21.3.1;3.1 SSVEP-based BCIs;320
21.3.2;3.2 The P300-based BCI;320
21.3.3;3.3 ERD/ERS-based BCI;321
21.3.3.1;3.3.1 Power Feature Extraction Based on Band-Pass Filter;321
21.3.3.2;3.3.2 Autoregressive Model Coefficients;322
21.4;4 Feature Selection;322
21.4.1;4.1 Channel Selection;323
21.4.2;4.2 Frequency Band Selection;323
21.5;5 Translation Methods;324
21.5.1;5.1 Classification Methods;324
21.5.1.1;5.1.1 Fisher Linear Discriminant;324
21.5.1.2;5.1.2 Support Vector Machine;326
21.5.2;5.2 Regression Method;327
21.6;6 Parameter Setting and Performance Evaluation for a BCI System;327
21.6.1;6.1 K--folds Cross-Validation;328
21.6.2;6.2 Performance Evaluation of a BCI System;329
21.6.2.1;6.2.1 Speed and Accuracy;329
21.6.2.2;6.2.2 Information Transfer Rate;329
21.6.2.3;6.2.3 ROC Curve;329
21.7;7 An Example of BCI Applications: A P300 BCI Speller;331
21.8;8 Summary;333
21.9;References;333
22;Adaptive Methods in BCI Research - An Introductory Tutorial;337
22.1;1 Introduction;337
22.1.1;1.1 Why We Need Adaptive Methods;337
22.1.2;1.2 Basic Adaptive Estimators;339
22.1.2.1;1.2.1 Mean Estimation;339
22.1.2.2;1.2.2 Variance Estimation;341
22.1.2.3;1.2.3 Variance-Covariance Estimation;341
22.1.2.4;1.2.4 Adaptive Inverse Covariance Matrix Estimation;342
22.1.2.5;Kalman Filtering and the State Space Model;343
22.1.3;1.3 Feature Extraction;345
22.1.3.1;1.3.1 Adaptive Autoregressive Modeling;345
22.1.4;1.4 Adaptive Classifiers;347
22.1.4.1;1.4.1 Adaptive QDA Estimator;347
22.1.4.2;1.4.2 Adaptive LDA Estimator;348
22.1.5;1.5 Selection of Initial Values, Update Coefficient and Model Order;350
22.1.6;1.6 Experiments with Adaptive QDA and LDA;352
22.1.7;1.7 Discussion;357
22.1.8;References;358
23;Toward Ubiquitous BCIs;362
23.1;1 Introduction;362
23.2;2 Key Factors in BCI Adoption;363
23.2.1;2.1 BCI Catalysts;364
23.2.2;2.2 Cost;367
23.2.3;2.3 Information Transfer Rate (ITR);369
23.2.4;2.4 Utility;370
23.2.5;2.5 Integration;373
23.2.6;2.6 Appearance;378
23.3;3 Other Incipient BCI Revolutions;380
23.3.1;3.1 Funding;380
23.3.2;3.2 User Groups Today;381
23.3.3;3.3 User Groups Tomorrow;382
23.4;4 BCI Ethics Today and Tomorrow;384
23.5;References;388
24;Index;393