Brainâ€“Computer Interfaces - Index of

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50 G. Pfurtscheller and C. Neuper Fig. 2 Relationship between movement type band power changes in the 8–10 Hz (right side) and the 10–12 Hz frequency bands (left side) over electrode locations C3, Cz and C4. A percentage band power increase marks ERS, and a decrease reflects ERD. The band power was calculated in a 500 ms window prior to movement-onset (modified from [26]) widespread over the entire sensorimotor cortex and probably reflects general motor preparation and attention. Upper mu desynchronization (in the range of about 10–12 Hz), in contrast, is topographically restricted and is rather related to taskspecific aspects. The lower mu frequency component displays a similar ERD with hand and foot movement, while the higher components display a different pattern with an ERD during hand and an ERS with foot movement (Fig. 2). This type of reactivity of different mu components suggests a functional dissociation between upper and lower mu components. The former displays a somatotopically specific organization, while the latter is somatotopically unspecific. Two patterns can develop during BCI training sessions with motor imagery: contralateral desynchronization of upper mu components and a concomitant power increase (ERS) over the ipsilateral side. In contrast to the bilaterally symmetrical lower mu ERD, only the upper mu displays an ipsilateral ERS (Fig. 3). These findings strongly indicate primary motor cortex activity during mental simulation of movement. Hence, we can assume that the pre-movement ERD and the ERD during motor imagery reflect a similar type of readiness or presetting of neural networks in sensorimotor areas. Functional brain imaging studies (e.g. [32–36]) and transcranial magnetic stimulation (TMS) show an increase of motor responses during mental imagination of movements [37], which further supports involvement of the primary sensorimotor cortex in motor imagery. The observation that movement imagination triggers a significant ipsilateral ERS along with the contralateral ERD supports the concept of antagonistic behavior of neural networks (“focal ERD/surrounding ERS”, [38]) described in the next section. In a recent study, several motor imagery tasks were investigated, such as cuebased left hand, right hand, foot, or tongue motor imagery [39]. Basically, hand motor imagery activates neural networks in the cortical hand representation area, which is manifested in the hand area mu ERD. Such a mu ERD was found in all able-bodied subjects with a clear contralateral dominance. The midcentral ERD, in
Dynamics of Sensorimotor Oscillations in a Motor Task 51 Fig. 3 Mean ERD/ERS values (i.e. mean and standard error) obtained for left hand (left side)versus right hand imagery (right side) for the 8–10 Hz (upper row) and 10–12 Hz frequency bands (lower row). Data were obtained from the left (C3, white bar) versus right (C4, black bar) sensorimotor derivation and are shown separately for 4 BCI training sessions (S1–S4) performed with each of the (N=10) participants. This figure is modified from [9] the case of foot motor imagery, was weak and not found in every subject. The reason for this may be that the foot representation area is hidden within the mesial wall. The mesial wall refers to the tissue between the left and right hemispheres of the brain. This area is difficult to detect with electrode caps outside the head, since electrode caps are best at measuring signals near the outer surface of the brain just underneath the skull. Noteworthily, both foot and tongue motor imagery enhanced the hand area mu rhythm. In addition, tongue motor imagery induced mu oscillations in or close to the foot representation area. This antagonistic pattern of a focal ERD accompanied by a lateral (surrounding) ERS will be described in more detail in the following sections. 4 Interpretation of ERD and ERS Connections between the thalamus and cortex are called thalamo-cortical systems. Increased cellular excitability (that is, an increase in the likelihood that neurons will fire) in thalamo-cortical systems results in a low amplitude desynchronized EEG [40]. Therefore, ERD can be understood as an electrophysiological correlate of activated cortical areas involved in processing sensory or cognitive information or producing motor behavior [5]. An increased and/or more widespread ERD could result from a larger neural network or more cell assemblies in information processing. Explicit learning of a movement sequence, e.g. key pressing with different fingers, is accompanied by an enhancement of the ERD over the contralateral central
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THE FRONTIERS COLLECTION
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Bernhard Graimann · Brendan Alliso
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Preface It’s an exciting time to
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Contents Brain-Computer Interfaces:
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Contributors Brendan Allison Instit
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Contributors xi Femke Nijboer Insti
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100 E.W. Sellers et al. Fig. 3 Two-
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102 E.W. Sellers et al. Fig. 5 Comp
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104 E.W. Sellers et al. Fig. 7 Mont
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106 E.W. Sellers et al. fixation wa
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108 E.W. Sellers et al. 5 SMR-Based
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110 E.W. Sellers et al. 22. D.J Kru
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Detecting Mental States by Machine
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138 Y. Wang et al. which has been e
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140 Y. Wang et al. After many studi
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142 Y. Wang et al. Left Hand Right
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144 Y. Wang et al. 0-degree 60-degr
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146 Y. Wang et al. 3.1.2 Stimulatio
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148 Y. Wang et al. Foot 1 0.5 Left
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150 Y. Wang et al. be summarized as
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152 Y. Wang et al. Fig. 10 A player
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154 Y. Wang et al. 22. Y. Wang, R.
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156 N. Birbaumer and P. Sauseng C A
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158 N. Birbaumer and P. Sauseng 3 B
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160 N. Birbaumer and P. Sauseng pat
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162 N. Birbaumer and P. Sauseng Fig
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164 N. Birbaumer and P. Sauseng of
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166 N. Birbaumer and P. Sauseng Neu
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168 N. Birbaumer and P. Sauseng 8.
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Non Invasive BCIs for Neuroprosthes
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Brain-Computer Interfaces for Commu
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204 D.M. Taylor and M.E. Stetner mo
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206 D.M. Taylor and M.E. Stetner el
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208 D.M. Taylor and M.E. Stetner ac
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BCIs Based on Signals from Between
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242 K.J. Miller and J.G. Ojemann 2
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258 K.J. Miller and J.G. Ojemann 26
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260 J. Mellinger and G. Schalk mapp
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262 J. Mellinger and G. Schalk 2 BC
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264 J. Mellinger and G. Schalk Fig.
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266 J. Mellinger and G. Schalk Filt
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268 J. Mellinger and G. Schalk proc
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278 J. Mellinger and G. Schalk 5. A
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The First Commercial Brain-Computer
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306 Y. Li et al. Fig. 1 Basic desig
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308 Y. Li et al. Fig. 2 A linear tr
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310 Y. Li et al. The large Laplacia
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312 Y. Li et al. components is larg
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314 Y. Li et al. P300-based, and ER
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316 Y. Li et al. 3.3.2 Autoregressi
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318 Y. Li et al. selection as follo
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320 Y. Li et al. 5.1.2 Support Vect
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322 Y. Li et al. is small and the n
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324 Y. Li et al. (ROC) analysis app
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326 Y. Li et al. Fig. 10 The stimul
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328 Y. Li et al. 6. M. Cheng, X. Ga
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330 Y. Li et al. 46. K. Crammer and
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332 A. Schlögl et al. Fig. 1 Schem
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334 A. Schlögl et al. For the rect
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336 A. Schlögl et al. whereby T in
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338 A. Schlögl et al. Fig. 2 State
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340 A. Schlögl et al. yk = a1 · y
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342 A. Schlögl et al. d{i}(x) = ((
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344 A. Schlögl et al. Accordingly,
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346 A. Schlögl et al. not exceed a
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348 A. Schlögl et al. Error [%] RL
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350 A. Schlögl et al. Table 3 Aver
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352 A. Schlögl et al. The extended
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354 A. Schlögl et al. 24. N.G. Pfu
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Toward Ubiquitous BCIs Brendan Z. A
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Toward Ubiquitous BCIs 359 BCI Chal
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Toward Ubiquitous BCIs 361 communic
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Toward Ubiquitous BCIs 363 facial m
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Toward Ubiquitous BCIs 365 The best
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Toward Ubiquitous BCIs 367 Across a
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Toward Ubiquitous BCIs 369 the ques
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Toward Ubiquitous BCIs 371 controll
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Toward Ubiquitous BCIs 373 controls
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Toward Ubiquitous BCIs 375 hair in
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Toward Ubiquitous BCIs 377 Table 1
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Toward Ubiquitous BCIs 379 conversa
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Toward Ubiquitous BCIs 381 may down
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Toward Ubiquitous BCIs 383 physicis
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Toward Ubiquitous BCIs 385 31. F.H.
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Toward Ubiquitous BCIs 387 67. G. S
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390 Index 65, 157, 163, 217, 221-23
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392 Index 208, 236, 243, 245, 260-2
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