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