Brain-Computer Interfaces - Revolutionizing Human-Computer Interaction
von: Bernhard Graimann, Brendan Z. Allison, Gert Pfurtscheller
Springer-Verlag, 2010
ISBN: 9783642020919
Sprache: Englisch
397 Seiten, Download: 11697 KB
Format: PDF, auch als Online-Lesen
Preface | 6 | ||
Contents | 8 | ||
Contributors | 10 | ||
List of Abbreviations | 14 | ||
BrainComputer Interfaces: A Gentle Introduction | 16 | ||
1 What is a BCI? | 17 | ||
2 How Do BCIs Work? | 20 | ||
2.1 Measuring Brain Activity (Without Surgery) | 21 | ||
2.2 Measuring Brain Activity (With Surgery) | 22 | ||
2.3 Mental Strategies and Brain Patterns | 24 | ||
2.3.1 Selective Attention | 25 | ||
2.3.2 Motor Imagery | 26 | ||
2.4 Signal Processing | 28 | ||
3 BCI Performance | 29 | ||
4 Applications | 31 | ||
5 Summary | 37 | ||
References | 39 | ||
Brain Signals for BrainComputer Interfaces | 43 | ||
1 Introduction | 43 | ||
1.1 The Need for BCIs | 43 | ||
1.2 Key Principles | 43 | ||
1.3 The Origin of Brain Signals Used in BCIs | 44 | ||
2 Brain Signals for BCIs and Their Neurophysiological Origins | 45 | ||
2.1 Brain Signal Features Measured Noninvasively | 46 | ||
2.1.1 Event-related Potentials (ERPs) | 46 | ||
2.1.2 Cortical Oscillations | 49 | ||
2.2 Brain Signal Features Measured from the Cortical Surface | 51 | ||
2.3 Brain Signal Features Measured Within the Cortex | 51 | ||
2.3.1 Local Field Potentials (LFPs) in the Time Domain | 52 | ||
2.3.2 Local Field Potentials in the Frequency Domain | 52 | ||
2.3.3 Single-Neuron Activity | 52 | ||
3 Requirements for Continued Progress | 53 | ||
References | 54 | ||
Dynamics of Sensorimotor Oscillations in a Motor Task | 61 | ||
1 Introduction | 61 | ||
2 EventRelated Potentials Versus ERD/ERS | 62 | ||
3 Mu and Beta ERD in a Motor Task | 62 | ||
4 Interpretation of ERD and ERS | 65 | ||
5 Focal ERD/Surround ERS | 66 | ||
6 Induced Beta Oscillations after Termination of a Motor Task | 67 | ||
7 Short-Lived Brain States | 69 | ||
8 Observation of Movement and Sensorimotor Rhythms | 71 | ||
9 Conclusion | 73 | ||
References | 73 | ||
Neurofeedback Training for BCI Control | 79 | ||
1 Introduction | 79 | ||
2 Principles of Neurofeedback | 80 | ||
2.1 Training of Sensorimotor Rhythms | 81 | ||
2.2 How Neurofeedback Works | 82 | ||
3 Training Paradigms for BCI Control | 82 | ||
3.1 Training with the Graz-BCI | 83 | ||
3.2 Impact of Feedback Stimuli | 85 | ||
4 Final Considerations | 87 | ||
References | 89 | ||
The Graz Brain-Computer Interface | 93 | ||
1 Introduction | 93 | ||
2 The Graz BCI | 93 | ||
3 Motor Imagery as Mental Strategy | 95 | ||
3.1 Induced Oscillations in Non-attended Cortical Body Part Areas | 96 | ||
3.2 Induced Beta Oscillations in Attended Cortical Body Part Areas | 97 | ||
3.3 The Beta Rebound (ERS) and its Importance for BCI | 98 | ||
4 Feature Extraction and Selection | 99 | ||
5 Frequency Band and Electrode Selection | 101 | ||
6 Special Applications of the Graz BCI | 102 | ||
6.1 Self-Paced Exploration of the Austrian National Library | 102 | ||
6.2 Simulation of Self-Paced Wheel Chair Movement in a Virtual Environment | 103 | ||
6.3 Control of Google Earth | 105 | ||
7 Future Aspects | 106 | ||
References | 107 | ||
BCIs in the Laboratory and at Home: The WadsworthResearch Program | 111 | ||
1 Introduction | 111 | ||
2 Sensorimotor Rhythm-Based Cursor Control | 112 | ||
3 P300-Based Item Selection | 116 | ||
4 A BCI System for Home Use | 120 | ||
5 SMR-Based Versus P300-Based BCIs | 121 | ||
References | 123 | ||
Detecting Mental States by Machine Learning Techniques: The Berlin BrainComputer Interface | 126 | ||
1 Introduction | 126 | ||
1.1 The Machine Learning Approach | 126 | ||
1.2 Neurophysiological Features | 127 | ||
1.2.1 Readiness Potential | 128 | ||
1.2.2 Sensorimotor Rhythms | 129 | ||
2 Processing and Machine Learning Techniques | 129 | ||
2.1 Common Spatial Patterns Analysis | 130 | ||
2.2 Regularized Linear Classification | 131 | ||
2.2.1 Mathematical Part | 131 | ||
3 BBCI Control Using Motor Paradigms | 133 | ||
3.1 High Information Transfer Rates | 133 | ||
3.2 Good Performance Without Subject Training | 135 | ||
3.3 BCI Illiteracy | 136 | ||
4 Applications of BBCI Technology | 138 | ||
4.1 Prosthetic Control | 138 | ||
4.2 Time-Critical Applications: Prediction of Upcoming Movements | 139 | ||
4.3 Neuro Usability | 140 | ||
4.4 Mental State Monitoring | 141 | ||
4.4.1 Experimental Setup for Attention Monitoring | 142 | ||
4.4.2 Results | 143 | ||
5 Conclusion | 143 | ||
References | 145 | ||
Practical Designs of BrainComputer Interfaces Based on the Modulation of EEG Rhythms | 149 | ||
1 Introduction | 149 | ||
1.1 BCIs Based on the Modulation of Brain Rhythms | 149 | ||
1.2 Challenges Confronting Practical System Designs | 151 | ||
2 Modulation and Demodulation Methods for Brain Rhythms | 152 | ||
2.1 Power Modulation/Demodulation of Mu Rhythm | 153 | ||
2.2 Frequency Modulation/Demodulation of SSVEPs | 154 | ||
2.3 Phase Modulation/Demodulation of SSVEPs | 155 | ||
3 Designs of Practical BCIs | 156 | ||
3.1 Designs of a Practical SSVEP-based BCI | 157 | ||
3.1.1 Lead Position | 157 | ||
3.1.2 Stimulation Frequency | 158 | ||
3.1.3 Frequency Feature | 158 | ||
3.2 Designs of a Practical Motor Imagery Based BCI | 159 | ||
3.2.1 Phase Synchrony Measurement | 160 | ||
3.2.2 Electrode Layout | 161 | ||
4 Potential Applications | 162 | ||
4.1 Communication and Control | 162 | ||
4.2 Rehabilitation Training | 163 | ||
4.3 Computer Games | 164 | ||
5 Conclusion | 164 | ||
References | 165 | ||
BrainComputer Interface in Neurorehabilitation | 167 | ||
1 Introduction | 167 | ||
2 Basic Research | 169 | ||
3 BrainComputer Interfaces for Communication in Complete Paralysis | 169 | ||
4 BrainComputer Interfaces in Stroke and Spinal Cord Lesions | 172 | ||
5 The Emotional BCI | 175 | ||
6 Future of BCI in Neurorehabilitation | 178 | ||
References | 179 | ||
Non Invasive BCIs for Neuroprostheses Control of the Paralysed Hand | 182 | ||
1 Introduction | 182 | ||
1.1 Spinal Cord Injury | 182 | ||
1.2 Neuroprostheses for the Upper Extremity | 182 | ||
2 Brain-Computer Interface for Control of Grasping Neuroprostheses | 185 | ||
2.1 Patients | 186 | ||
2.2 EEG Recording and Signal Processing | 188 | ||
2.3 Setup Procedures for BCI Control | 188 | ||
2.3.1 BCI-Training of Patient TS Using a Neuroprosthesis with Surface Electrodes | 189 | ||
2.3.2 BCI-Training of Patient HK Using an Implanted Neuroprosthesis | 190 | ||
2.4 Interferences of Electrical Stimulation with the BCI | 190 | ||
2.5 Evaluation of the Overall Performance of the BCI Controlled Neuroprostheses | 191 | ||
3 Conclusion | 191 | ||
References | 193 | ||
BrainComputer Interfaces for Communication and Control in Locked-in Patients | 196 | ||
1 Introduction | 196 | ||
2 Locked-in the Body and Lock-Out of Society | 197 | ||
3 BCI Applications for Locked-in Patients | 199 | ||
4 Experiences of a BCI User | 202 | ||
5 BCI Training with Patients | 205 | ||
6 Conclusion | 208 | ||
References | 210 | ||
Intracortical BCIs: A Brief History of Neural Timing | 213 | ||
1 Introduction | 213 | ||
2 Why Penetrate the Brain? | 213 | ||
3 Neurons, Electricity, and Spikes | 215 | ||
4 The Road to Imperfection | 217 | ||
5 A Brief History of Intracortical BCIs | 219 | ||
6 The Holy Grail: Continuous Natural Movement Control | 223 | ||
7 What Else Can We Get from Intracortical Microelectrodes? | 226 | ||
References | 228 | ||
BCIs Based on Signals from Between the Brain and Skull | 230 | ||
1 Introduction | 230 | ||
2 Electrocorticogram: Signals from Between the Brain and Skull | 230 | ||
3 Advantages of ECoG | 231 | ||
3.1 Advantages of ECoG Versus EEG | 232 | ||
3.2 Advantages over Microelectrodes | 233 | ||
3.3 Everything Affects the Brain | 235 | ||
4 Disadvantages of ECoG | 235 | ||
5 Successful ECoG-Based BCI Research | 237 | ||
6 Past and Present ECoG Research for BCI | 238 | ||
6.1 ECoG Animal Research | 239 | ||
6.2 Human ECoG Studies | 239 | ||
6.2.1 Smith-Kettlewell Eye Research Institute | 239 | ||
6.2.2 The University of Michigan -- Ann Arbor (Levine and Huggins) | 239 | ||
6.2.3 The University of Washington in St. Louis | 242 | ||
6.2.4 University of Wisconsin -- Madison | 243 | ||
6.2.5 Tuebingen, Germany | 244 | ||
6.2.6 University Hospital of Utrecht | 244 | ||
6.2.7 The University of Michigan -- Ann Arbor (Kipke) | 244 | ||
6.2.8 University of Florida -- Gainesville | 245 | ||
6.2.9 Albert-Ludwigs-University, Freiburg, Germany | 245 | ||
7 Discussion | 245 | ||
References | 246 | ||
A Simple, Spectral-Change Based, Electrocorticographic BrainComputer Interface | 249 | ||
1 Introduction | 249 | ||
2 Signal Acquisition | 249 | ||
3 Feature Selection | 254 | ||
4 Feedback | 258 | ||
5 Learning | 261 | ||
6 Case Study | 262 | ||
7 Conclusion | 264 | ||
References | 264 | ||
Using BCI2000 in BCI Research | 267 | ||
1 Introduction | 267 | ||
1.1 Proven Components | 268 | ||
1.2 Documentation | 269 | ||
1.3 Adaptability | 269 | ||
1.4 Access | 269 | ||
1.5 Deployment | 269 | ||
2 BCI2000 Design | 269 | ||
2.1 System Model | 270 | ||
2.2 Software Components | 273 | ||
2.3 Interfacing Components | 274 | ||
2.3.1 Data Formats | 274 | ||
2.3.2 Data Exchange | 275 | ||
2.3.3 Matlab Filter Scripts | 275 | ||
2.3.4 Online Data Exchange | 276 | ||
2.3.5 Operator Module Scripting | 276 | ||
2.4 Important Characteristics of BCI2000 | 276 | ||
2.5 Getting Started with BCI2000 | 277 | ||
3 Research Scenarios | 277 | ||
3.1 BCI Classroom | 277 | ||
3.1.1 EEG Hardware | 278 | ||
3.1.2 Software | 278 | ||
3.1.3 Getting Acquainted | 278 | ||
3.1.4 Tutorial Experiments | 279 | ||
3.2 Performing Psychophysiological Experiments | 279 | ||
3.3 Patient Communication System | 280 | ||
3.4 Multi-Site Research | 282 | ||
4 Research Trajectories | 284 | ||
5 Dissemination and Availability | 284 | ||
References | 285 | ||
The First Commercial BrainComputer Interface Environment | 288 | ||
1 Introduction | 288 | ||
2 Rapid Prototyping Environment | 290 | ||
2.1 Biosignal Amplifier Concepts | 290 | ||
2.2 Electrode Caps | 296 | ||
2.3 Programming Environment | 296 | ||
2.4 BCI System Architectures | 299 | ||
3 BCI Training | 300 | ||
3.1 Training for a Motor Imagery BCI Approach | 300 | ||
3.2 Training with a P300 Spelling Device | 302 | ||
4 BCI Applications | 304 | ||
4.1 IntendiX | 304 | ||
4.2 Virtual Reality Smart Home Control with the BCI | 305 | ||
4.3 Avatar Control | 308 | ||
References | 309 | ||
Digital Signal Processing and Machine Learning | 311 | ||
1 Architecture of BCI systems | 311 | ||
2 Preprocessing | 313 | ||
2.1 Spatial Filtering | 313 | ||
2.1.1 Linear Transformations | 313 | ||
2.1.2 Common Average Reference (CAR) | 314 | ||
2.1.3 Laplacian Reference | 315 | ||
2.1.4 Principal Component Analysis (PCA) | 316 | ||
2.1.5 Independent Component Analysis (ICA) | 317 | ||
2.1.6 Common Spatial Patterns (CSP) | 318 | ||
2.2 Temporal Filtering | 319 | ||
3 Feature Extraction | 319 | ||
3.1 SSVEP-based BCIs | 320 | ||
3.2 The P300-based BCI | 320 | ||
3.3 ERD/ERS-based BCI | 321 | ||
3.3.1 Power Feature Extraction Based on Band-Pass Filter | 321 | ||
3.3.2 Autoregressive Model Coefficients | 322 | ||
4 Feature Selection | 322 | ||
4.1 Channel Selection | 323 | ||
4.2 Frequency Band Selection | 323 | ||
5 Translation Methods | 324 | ||
5.1 Classification Methods | 324 | ||
5.1.1 Fisher Linear Discriminant | 324 | ||
5.1.2 Support Vector Machine | 326 | ||
5.2 Regression Method | 327 | ||
6 Parameter Setting and Performance Evaluation for a BCI System | 327 | ||
6.1 K--folds Cross-Validation | 328 | ||
6.2 Performance Evaluation of a BCI System | 329 | ||
6.2.1 Speed and Accuracy | 329 | ||
6.2.2 Information Transfer Rate | 329 | ||
6.2.3 ROC Curve | 329 | ||
7 An Example of BCI Applications: A P300 BCI Speller | 331 | ||
8 Summary | 333 | ||
References | 333 | ||
Adaptive Methods in BCI Research - An Introductory Tutorial | 337 | ||
1 Introduction | 337 | ||
1.1 Why We Need Adaptive Methods | 337 | ||
1.2 Basic Adaptive Estimators | 339 | ||
1.2.1 Mean Estimation | 339 | ||
1.2.2 Variance Estimation | 341 | ||
1.2.3 Variance-Covariance Estimation | 341 | ||
1.2.4 Adaptive Inverse Covariance Matrix Estimation | 342 | ||
Kalman Filtering and the State Space Model | 343 | ||
1.3 Feature Extraction | 345 | ||
1.3.1 Adaptive Autoregressive Modeling | 345 | ||
1.4 Adaptive Classifiers | 347 | ||
1.4.1 Adaptive QDA Estimator | 347 | ||
1.4.2 Adaptive LDA Estimator | 348 | ||
1.5 Selection of Initial Values, Update Coefficient and Model Order | 350 | ||
1.6 Experiments with Adaptive QDA and LDA | 352 | ||
1.7 Discussion | 357 | ||
References | 358 | ||
Toward Ubiquitous BCIs | 362 | ||
1 Introduction | 362 | ||
2 Key Factors in BCI Adoption | 363 | ||
2.1 BCI Catalysts | 364 | ||
2.2 Cost | 367 | ||
2.3 Information Transfer Rate (ITR) | 369 | ||
2.4 Utility | 370 | ||
2.5 Integration | 373 | ||
2.6 Appearance | 378 | ||
3 Other Incipient BCI Revolutions | 380 | ||
3.1 Funding | 380 | ||
3.2 User Groups Today | 381 | ||
3.3 User Groups Tomorrow | 382 | ||
4 BCI Ethics Today and Tomorrow | 384 | ||
References | 388 | ||
Index | 393 |