Brainâ€“Computer Interfaces - Index of

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Brain–Computer Interfaces for Communication and Control in Locked-in Patients Femke Nijboer and Ursula Broermann 1 Introduction If you really want to help somebody, first you must find out where he is. This is the secret of caring. If you cannot do that, it is only an illusion if you think you can help another human being. Helping somebody implies you understanding more than he does, but first of all you must understand what he understands. 1 Most Brain–Computer Interface (BCI) research aims at helping people who are severely paralyzed to regain control over their environment and to communicate with their social environment. There has been a tremendous increase in BCI research the last years, which might lead to the belief that we are close to a commercially available BCI applications to patients. However, studies with users from the future target group (those who are indeed paralyzed) are still outnumbered by studies on technical aspects of BCI applications and studies with healthy young participants. This might explain why the number of patients who use a BCI in daily life, without experts from a BCI group being present, can be counted on one hand. In this chapter we will focus on the feasibility and flaws of BCIs for lockedin and complete locked-in patients (the difference between these conditions will be explained in paragraph 2). Thus, we will speak a lot about problems BCI researchers face when testing a BCI or implementing a BCI at the home of patients. With this, we hope to stimulate further studies with paralyzed patients. We believe that patients F. Nijboer (B) Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany; Human-Media Interaction, University of Twente, Enschede, The Netherlands e-mail: femke.nijboer@utwente.nl 1Sören Kierkegaard: The point of view from my work as an author, 39. B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection, DOI 10.1007/978-3-642-02091-9_11, C○ Springer-Verlag Berlin Heidelberg 2010 185
186 F. Nijboer and U. Broermann become BCI experts themselves during BCI training and provide useful insights into the usability of different BCI systems. Thus, it only seemed logical that for this chapter a BCI expert and a BCI user should work together to describe the experiences with BCI applications in the field. First, we will describe the user group that might benefit from BCI applications and explain the difference between the locked-in syndrome and the completely locked-in syndrome (Sect. 2). These terms are often wrongfully used by BCI researchers and medical staff, and it is important to clarify them. Second, we review the studies that have shown that Brain–Computer interfaces might provide a tool to communicate or to control the environment for locked-in patients. Third, in a unique interview, Ursula Broermann comments on multiple questions the reader might have in his or her head after reading the description of the disease amyotrophic lateral sclerosis (Sect. 3). She explains her daily life, what is important in her life, and how technical tools improve her quality of life significantly. These personal words from a BCI user are even more impressive when one considers that she had to select every single letter by carefully manoeuvring a cursor over a virtual keyboard by a lower lip-controlled joystick. It took her several months to write the text provided in this chapter. In Sect. 4, we describe a typical BCI training process with a patient on the verge of being locked-in in a very non-scientific yet illustrative way. Through this description we can deduce simple requirements BCI applications should meet to become suitable for home use. 2 Locked-in the Body and Lock-Out of Society The future user group of BCI applications consists mainly of people with neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease. ALS is a fatal motor neuron disease of unknown etiology and cure. ALS is a neurodegenerative disorder of large motor neurons of the cerebral cortex, brain stem, and spinal cord that results in progressive paralysis and wasting of muscles [1]. ALS has an incidence of 2/100,000 and a prevalence of 6–8/100,000 [2]. Survival is limited by respiratory insufficiency. Most patients die within 3–5 years after onset of the disease [1], unless they choose life-sustaining treatment [3]. As the disease progresses, people get more and more paralyzed. The first symptoms most people initially experience include weakness in arms or legs, after which the paralysis spreads to other extremities and finally also neck and head areas. This form of ALS is called spinal ALS. On contrary, bulbar ALS starts with symptoms of weakness and paralysis in neck and mouth regions and then spreads to other extremities. The choice (written down in a living will) to accept or decline life-sustaining treatment, such as artificial nutrition and artificial ventilation, is probably the most difficult choice a patient has to make during his disease progress. In fact, most people find it so difficult that they do not make a decision at all and decisions are made
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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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List of Abbreviations ADHD Attentio
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Brain-Computer Interfaces: A Gentle
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30 J.R. Wolpaw and C.B. Boulay by b
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32 J.R. Wolpaw and C.B. Boulay 2 Br
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34 J.R. Wolpaw and C.B. Boulay Fig.
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36 J.R. Wolpaw and C.B. Boulay Sens
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38 J.R. Wolpaw and C.B. Boulay pote
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40 J.R. Wolpaw and C.B. Boulay EEG-
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42 J.R. Wolpaw and C.B. Boulay 29.
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80 G. Pfurtscheller et al. mode, th
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82 G. Pfurtscheller et al. Therefor
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84 G. Pfurtscheller et al. A B C 1
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86 G. Pfurtscheller et al. filters
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88 G. Pfurtscheller et al. differen
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90 G. Pfurtscheller et al. In this
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92 G. Pfurtscheller et al. Fig. 8 P
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94 G. Pfurtscheller et al. 10. D. F
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96 G. Pfurtscheller et al. 51. G. P
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98 E.W. Sellers et al. Fig. 1 Three
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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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BCIs Based on Signals from Between
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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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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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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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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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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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