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 Atten