Brainâ€“Computer Interfaces - Index of

More documents

Recommendations

Info

The First Commercial Brain–Computer Interface Environment Christoph Guger and Günter Edlinger 1 Introduction The first commercial brain–computer interface environment has been developed so research centers could easily and quickly run BCI experiments to test algorithms and different strategies. A first BCI system was available on the market in 1999, and was continuously improved to the system available today, which is now used in more than 60 countries worldwide. Many factors influence the design of a BCI system, as shown in Fig. 1. There are technical issues and issues concerning the individual subject that must be addressed. Different brain signals are used in BCIs, such as the Electroencephalogram (EEG) recorded non-invasively from the scalp, or the Electrocorticogram (ECoG), which requires invasive electrodes. Therefore, different safety issues, sampling frequencies and electrode types are required. The applied signal processing algorithms have to work in on-line mode to provide real-time capabilities and decision making in real life situations. However, the algorithms must also work in off-line mode to support in depth analysis of already acquired EEG data. The signal acquisition and processing unit has to ensure high data quality using e.g. over-sampling techniques providing a high signal to noise ratio (SNR). One type of BCI approach is based on motor imagery [1] (see also Chapters “Brain Signals for Brain–Computer Interfaces” and “Dynamics of Sensorimotor Oscillations in a Motor Task” in this book). This approach can be realized with only a few electrodes over the sensorimotor cortex. Some other approaches that use spatial filtering techniques [2] use about 16–128 electrodes. A BCI based on ECoG signals also requires many channels (64– 128) because dense electrode grids overlaying parts of the cortex [3] are utilized. C. Guger (B) Guger Technologies OG/g.tec medical engineering GmbH, Herbersteinstrasse 60, 8020 Graz, Austria e-mail: guger@gtec.at B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection, DOI 10.1007/978-3-642-02091-9_16, C○ Springer-Verlag Berlin Heidelberg 2010 281
282 C. Guger and G. Edlinger Fig. 1 Components that influence BCI development For higher numbers of channels, the real-time capabilities of the BCI system must be also guaranteed for accurately timed generation of the desired control signals for feedback to e.g. external devices. On the subject side, it is very important that the BCI system adapts itself to the learning process of the user. This can either be done in on-line mode, where the classification algorithm is retrained after each decision, or in a quasi on-line mode, where e.g. 40 decisions are made and the classifier is then retrained to adapt to the new control signals [4]. However, it is also important that the subject adapts to the BCI system and learns how to control the system. Hence, the provided feedback strategy is very important for a successful learning process. Immediate feedback of a cursor movement, fast control in computer games or correct responses of a spelling device are therefore vital requirements. Very important for a subject’s success in BCI control is the choice of the optimal BCI approach which can be based on: (i) oscillations in the alpha and beta range, (ii) slow waves, (iii) P300 components or (iv) steady-state visual evoked potentials (SSVEP) [5–8]. For some subjects, oscillations in the alpha and beta band during motor imagery work better than the evoked potential approach and vice versa. Therefore, it is very important that the BCI system enables testing the subject with all these different approaches to find the optimal one. A traditional development approach is shown in Fig. 2, left side. Here, feature extraction algorithms (e.g. recursive least square (RLS) estimation of adaptive autoregressive parameters – AAR) and classification algorithms (e.g. linear discriminant analysis – LDA) are typically implemented in a first step for the off-line analysis of already acquired EEG data. After adaptation and optimization of the algorithms, the hardware for the EEG acquisition and the real-time processing environment is developed, leading to a new implementation process of the off-line algorithms and to many iterations between the algorithm and hardware design. On the other side, the rapid prototyping process combines the algorithm design with the real-time tests on the hardware (see Fig. 2, right side). Typically, an already proven hardware and software environment is used for such an approach. The diagrams in
Page 2 and 3:
THE FRONTIERS COLLECTION
Page 4 and 5:
Bernhard Graimann · Brendan Alliso
Page 6 and 7:
Preface It’s an exciting time to
Page 8 and 9:
Contents Brain-Computer Interfaces:
Page 10 and 11:
Contributors Brendan Allison Instit
Page 12 and 13:
Contributors xi Femke Nijboer Insti
Page 14 and 15:
List of Abbreviations ADHD Attentio
Page 16 and 17:
Brain-Computer Interfaces: A Gentle
Page 18 and 19:
Page 20 and 21:
Page 22 and 23:
Page 24 and 25:
Page 26 and 27:
Page 28 and 29:
Page 30 and 31:
Page 32 and 33:
Page 34 and 35:
Page 36 and 37:
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
Page 44 and 45:
30 J.R. Wolpaw and C.B. Boulay by b
Page 46 and 47:
32 J.R. Wolpaw and C.B. Boulay 2 Br
Page 48 and 49:
34 J.R. Wolpaw and C.B. Boulay Fig.
Page 50 and 51:
36 J.R. Wolpaw and C.B. Boulay Sens
Page 52 and 53:
38 J.R. Wolpaw and C.B. Boulay pote
Page 54 and 55:
40 J.R. Wolpaw and C.B. Boulay EEG-
Page 56 and 57:
42 J.R. Wolpaw and C.B. Boulay 29.
Page 58 and 59:
Page 60 and 61:
Page 62 and 63:
48 G. Pfurtscheller and C. Neuper F
Page 64 and 65:
Page 66 and 67:
52 G. Pfurtscheller and C. Neuper r
Page 68 and 69:
54 G. Pfurtscheller and C. Neuper 6
Page 70 and 71:
56 G. Pfurtscheller and C. Neuper B
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
66 C. Neuper and G. Pfurtscheller s
Page 82 and 83:
68 C. Neuper and G. Pfurtscheller 2
Page 84 and 85:
70 C. Neuper and G. Pfurtscheller F
Page 86 and 87:
72 C. Neuper and G. Pfurtscheller b
Page 88 and 89:
74 C. Neuper and G. Pfurtscheller E
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
80 G. Pfurtscheller et al. mode, th
Page 96 and 97:
82 G. Pfurtscheller et al. Therefor
Page 98 and 99:
84 G. Pfurtscheller et al. A B C 1
Page 100 and 101:
86 G. Pfurtscheller et al. filters
Page 102 and 103:
88 G. Pfurtscheller et al. differen
Page 104 and 105:
90 G. Pfurtscheller et al. In this
Page 106 and 107:
92 G. Pfurtscheller et al. Fig. 8 P
Page 108 and 109:
94 G. Pfurtscheller et al. 10. D. F
Page 110 and 111:
96 G. Pfurtscheller et al. 51. G. P
Page 112 and 113:
98 E.W. Sellers et al. Fig. 1 Three
Page 114 and 115:
100 E.W. Sellers et al. Fig. 3 Two-
Page 116 and 117:
102 E.W. Sellers et al. Fig. 5 Comp
Page 118 and 119:
104 E.W. Sellers et al. Fig. 7 Mont
Page 120 and 121:
106 E.W. Sellers et al. fixation wa
Page 122 and 123:
108 E.W. Sellers et al. 5 SMR-Based
Page 124 and 125:
110 E.W. Sellers et al. 22. D.J Kru
Page 126 and 127:
Detecting Mental States by Machine
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
138 Y. Wang et al. which has been e
Page 152 and 153:
140 Y. Wang et al. After many studi
Page 154 and 155:
142 Y. Wang et al. Left Hand Right
Page 156 and 157:
144 Y. Wang et al. 0-degree 60-degr
Page 158 and 159:
146 Y. Wang et al. 3.1.2 Stimulatio
Page 160 and 161:
148 Y. Wang et al. Foot 1 0.5 Left
Page 162 and 163:
150 Y. Wang et al. be summarized as
Page 164 and 165:
152 Y. Wang et al. Fig. 10 A player
Page 166 and 167:
154 Y. Wang et al. 22. Y. Wang, R.
Page 168 and 169:
156 N. Birbaumer and P. Sauseng C A
Page 170 and 171:
158 N. Birbaumer and P. Sauseng 3 B
Page 172 and 173:
160 N. Birbaumer and P. Sauseng pat
Page 174 and 175:
162 N. Birbaumer and P. Sauseng Fig
Page 176 and 177:
164 N. Birbaumer and P. Sauseng of
Page 178 and 179:
166 N. Birbaumer and P. Sauseng Neu
Page 180 and 181:
168 N. Birbaumer and P. Sauseng 8.
Page 182 and 183:
Non Invasive BCIs for Neuroprosthes
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Page 194 and 195:
Page 196 and 197:
Brain-Computer Interfaces for Commu
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
Page 214 and 215:
204 D.M. Taylor and M.E. Stetner mo
Page 216 and 217:
206 D.M. Taylor and M.E. Stetner el
Page 218 and 219:
208 D.M. Taylor and M.E. Stetner ac
Page 220 and 221:
210 D.M. Taylor and M.E. Stetner de
Page 222 and 223:
212 D.M. Taylor and M.E. Stetner Ma
Page 224 and 225:
214 D.M. Taylor and M.E. Stetner pe
Page 226 and 227:
216 D.M. Taylor and M.E. Stetner ev
Page 228 and 229:
218 D.M. Taylor and M.E. Stetner fo
Page 230 and 231:
BCIs Based on Signals from Between
Page 232 and 233:
Page 234 and 235:
Page 236 and 237:
Page 238 and 239: BCIs Based on Signals from Between
Page 250 and 251: 242 K.J. Miller and J.G. Ojemann 2
Page 252 and 253: 244 K.J. Miller and J.G. Ojemann ha
Page 254 and 255: 246 K.J. Miller and J.G. Ojemann Fi
Page 256 and 257: 248 K.J. Miller and J.G. Ojemann Fi
Page 258 and 259: 250 K.J. Miller and J.G. Ojemann fo
Page 260 and 261: 252 K.J. Miller and J.G. Ojemann me
Page 262 and 263: 254 K.J. Miller and J.G. Ojemann In
Page 264 and 265: 256 K.J. Miller and J.G. Ojemann ar
Page 266 and 267: 258 K.J. Miller and J.G. Ojemann 26
Page 268 and 269: 260 J. Mellinger and G. Schalk mapp
Page 270 and 271: 262 J. Mellinger and G. Schalk 2 BC
Page 272 and 273: 264 J. Mellinger and G. Schalk Fig.
Page 274 and 275: 266 J. Mellinger and G. Schalk Filt
Page 276 and 277: 268 J. Mellinger and G. Schalk proc
Page 278 and 279: 270 J. Mellinger and G. Schalk exis
Page 280 and 281: 272 J. Mellinger and G. Schalk reco
Page 282 and 283: 274 J. Mellinger and G. Schalk Fig.
Page 284 and 285: 276 J. Mellinger and G. Schalk numb
Page 286 and 287: 278 J. Mellinger and G. Schalk 5. A
Page 290 and 291: The First Commercial Brain-Computer
Page 312 and 313: 306 Y. Li et al. Fig. 1 Basic desig
Page 314 and 315: 308 Y. Li et al. Fig. 2 A linear tr
Page 316 and 317: 310 Y. Li et al. The large Laplacia
Page 318 and 319: 312 Y. Li et al. components is larg
Page 320 and 321: 314 Y. Li et al. P300-based, and ER
Page 322 and 323: 316 Y. Li et al. 3.3.2 Autoregressi
Page 324 and 325: 318 Y. Li et al. selection as follo
Page 326 and 327: 320 Y. Li et al. 5.1.2 Support Vect
Page 328 and 329: 322 Y. Li et al. is small and the n
Page 330 and 331: 324 Y. Li et al. (ROC) analysis app
Page 332 and 333: 326 Y. Li et al. Fig. 10 The stimul
Page 334 and 335: 328 Y. Li et al. 6. M. Cheng, X. Ga
Page 336 and 337: 330 Y. Li et al. 46. K. Crammer and
Page 338 and 339:
332 A. Schlögl et al. Fig. 1 Schem
Page 340 and 341:
334 A. Schlögl et al. For the rect
Page 342 and 343:
336 A. Schlögl et al. whereby T in
Page 344 and 345:
338 A. Schlögl et al. Fig. 2 State
Page 346 and 347:
340 A. Schlögl et al. yk = a1 · y
Page 348 and 349:
342 A. Schlögl et al. d{i}(x) = ((
Page 350 and 351:
344 A. Schlögl et al. Accordingly,
Page 352 and 353:
346 A. Schlögl et al. not exceed a
Page 354 and 355:
348 A. Schlögl et al. Error [%] RL
Page 356 and 357:
350 A. Schlögl et al. Table 3 Aver
Page 358 and 359:
352 A. Schlögl et al. The extended
Page 360 and 361:
354 A. Schlögl et al. 24. N.G. Pfu
Page 362 and 363:
Toward Ubiquitous BCIs Brendan Z. A
Page 364 and 365:
Toward Ubiquitous BCIs 359 BCI Chal
Page 366 and 367:
Toward Ubiquitous BCIs 361 communic
Page 368 and 369:
Toward Ubiquitous BCIs 363 facial m
Page 370 and 371:
Toward Ubiquitous BCIs 365 The best
Page 372 and 373:
Toward Ubiquitous BCIs 367 Across a
Page 374 and 375:
Toward Ubiquitous BCIs 369 the ques
Page 376 and 377:
Toward Ubiquitous BCIs 371 controll
Page 378 and 379:
Toward Ubiquitous BCIs 373 controls
Page 380 and 381:
Toward Ubiquitous BCIs 375 hair in
Page 382 and 383:
Toward Ubiquitous BCIs 377 Table 1
Page 384 and 385:
Toward Ubiquitous BCIs 379 conversa
Page 386 and 387:
Toward Ubiquitous BCIs 381 may down
Page 388 and 389:
Toward Ubiquitous BCIs 383 physicis
Page 390 and 391:
Toward Ubiquitous BCIs 385 31. F.H.
Page 392 and 393:
Toward Ubiquitous BCIs 387 67. G. S
Page 394 and 395:
390 Index 65, 157, 163, 217, 221-23
Page 396 and 397:
392 Index 208, 236, 243, 245, 260-2
show all

Brainâ€“Computer Interfaces - Index of

Create successful ePaper yourself

Delete template?

Save as template?