Brainâ€“Computer Interfaces - Index of

More documents

Recommendations

Info

72 C. Neuper and G. Pfurtscheller be superior to auditory feedback in most BCIs. Recently, Chatterjee et al. [46] presented a mu-rhythm based BCI using a motor imagery paradigm and haptic (touch) feedback provided by vibrotactile stimuli to the upper limb. Further work will be needed to determine how the neural correlates of vibrotactile feedback affect the modulation of the mu rhythm. However, haptic information may become a critical component of BCIs designed to control an advanced neuroprosthetic device [47]. Cincotti et al. [48] documented interesting benefits of vibrotactile feedback, particularly when visual feedback was not practical because the subjects were performing complex visual tasks. Despite the success of non-visual BCI systems, most BCIs present feedback visually [1]. Typical visual feedback stimuli comprise cursor movement [6, 7], a moving bar of varying size [40, 46], and the trajectory of a moving object like in the basket game [7, 41]. Other interesting variants include colour signalling [49] and complex virtual reality environments [42, 50, 51]. There is some evidence that a rich visual representation of the feedback signal, such as a 3-dimensional video game or virtual reality environment, may facilitate learning to use a BCI [42, 50–53]. Combining BCI and virtual reality (VR) technologies may lead to highly realistic and immersive BCI feedback scenarios. [51] was as an important step in this direction. This article showed that EEG recording and single trial processing with adequate classification results are possible in a CAVE system (a virtual reality environment that our group used during BCI research), and that subjects could even control events within a virtual environment in real-time (see chapter “The Graz Brain–Computer Interface” for details). Figure 3 shows that simple bar feedback in a cued hand vs. feet imagery task on a monitor was accompanied by a moderate ERD at electrode position Cz and a heart rate deceleration. In contrast, feedback in form of a virtual street resulted in induced beta oscillations (beta ERS) at Cz and a heart rate acceleration. The mutual interactions between brain and heart activity during complex virtual feedback are especially interesting. These results suggest that improving the visual display in a BCI could improve a person’s control over his/her brain activity. In particular, the studies mentioned above support realistic and engaging feedback scenarios, which are closely related to the specific target application. For example, observing a realistic moving hand should have a greater effect on sensorimotor rhythms than watching abstract feedback in the form of a moving bar [55]. However, the processing of such a realistic feedback stimulus may interfere with the mental motor imagery task, and thus might impair the development of EEG control. Because similar brain signals, namely sensorimotor rhythms in the mu and beta frequency bands, react to both motor imagery [25] and observation of biological movement (e.g. [55–60]; for a review see [61]), a realistic feedback presentation showing (for instance) a moving visual scene may interfere with the motor imagery related brain signals used by the BCI. The mutual interaction between a mentally simulated movement and simultaneous watching of a moving limb should be further investigated. In a recent study, we explored how different types of visual feedback affect EEG activity (i.e. ERD/ERS patterns) during BCI control [62]. We showed two different
Neurofeedback Training for BCI Control 73 Fig. 3 Effect of different BCI feedback displays on oscillatory activity and heart rate. The top images depict a horizontal bar feedback condition, and the bottom images present a virtual street feedback condition. The two left panels each show the monitor or VR display. The middle panels display the corresponding ERD maps computed at electrode position Cz for hand motor imagery during bar and VR feedback. The right panels show corresponding changes in the heart rate (modified from Pfurtscheller et al. [54]) presentation types, namely abstract versus realistic feedback, to two experimental groups, while keeping the amount of information provided by the feedback equivalent. The “abstract feedback” group was trained to use left or right hand motor imagery to control a moving bar (varying in size) on a computer monitor. This is the standard protocol of the Graz-BCI, cf. [37, 40]. The “realistic feedback group”, in contrast, used their online EEG parameters to control a video presentation showing an object-directed grasp from the actor’s perspective. The results suggest that, when feedback provides comparable information on the continuous and final outcomes of mental actions, the type of feedback (abstract vs. realistic) does not influence performance. Considering the task-related EEG changes (i.e. ERD/ERS patterns), there was a significant difference between screening and feedback sessions in both groups, but the “realistic feedback” group showed more significant effects due to the feedback. Another important question is whether VR can reduce the number of training sessions [50]. 4 Final Considerations The BCI paradigm (see Fig. 4) basically corresponds to an operant conditioning neurofeedback paradigm. However, important differences exist between the training (e.g. with the basket paradigm) and the application (e.g. controlling a hand orthosis)
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: Brain-Computer Interfaces: A Gentle
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 62 and 63: 48 G. Pfurtscheller and C. Neuper F
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 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 88 and 89: 74 C. Neuper and G. Pfurtscheller E
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 136 and 137:
Detecting Mental States by Machine
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:
Page 240 and 241:
Page 242 and 243:
Page 244 and 245:
Page 246 and 247:
Page 248 and 249:
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 288 and 289:
The First Commercial Brain-Computer
Page 290 and 291:
Page 292 and 293:
Page 294 and 295:
Page 296 and 297:
Page 298 and 299:
Page 300 and 301:
Page 302 and 303:
Page 304 and 305:
Page 306 and 307:
Page 308 and 309:
Page 310 and 311:
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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?