You are here: HomeActivitiesBackground Information 

eChallenges e2013

23rd annual

eChallenge e-2013

Conference &

Exhibition

Malahide, Dublin, Ireland

October 09-11, 2013 

More information is available at http://www.echallenges.org/e2013/default.asp?page=themes

Call for Papers:

Related themes and suggestions for papers and sessions are outlined in the Call for Papers http://www.echallenges.org/e2013/default.asp?page=c4p

Background Information

  1. Boveroux et al., Breakdown of within- and between-network Resting State
    Functional Magnetic Resonance Imaging Connectivity during Propofol-induced Loss of Consciousness,
    Anaesthesiology 2010,113: p. 1038-1053
  2. Monti et al., Willful Modulation of Brain Activity in Disorders of Consciousness. The New England Journal of medicine, 2010, 10: 1056-1067
  3. Laureys et al., Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome. BMC Medicine 2010, 8:68
  4. Andrews, K., et al., Misdiagnosis of the vegetative state: retrospective study in a rehabilitation unit. Bmj, 1996. 313(7048): p. 13-6.
  5. Childs, N.L. and W.N. Mercer, Misdiagnosing the persistent vegetative state. Misdiagnosis certainly occurs. Bmj, 1996. 313(7062): p. 944.
  6. Jennett, B., The vegetative state. J Neurol Neurosurg Psychiatry, 2002. 73(4): p. 355-7.
  7. Persistent vegetative state: report of the American Neurological Association Committee on Ethical Affairs. ANA Committee on Ethical Affairs. Ann Neurol, 1993. 33(4): p. 386-90.
  8. Recommendations for use of uniform nomenclature pertinent to patients with severe alterations in consciousness. American Congress of Rehabilitation Medicine. Arch Phys Med Rehabil, 1995. 76(2): p. 205-9.
  9. Laureys, S., S. Majerus, and G. Moonen, Assessing consciousness in critically ill patients, in Yearbook of Intensive Care and Emergency Medicine, V.J. L., Editor. Springer-Verlag: Berlin,  2002. p. 715-727.
  10. Schnakers, C., S. Majerus, and S. Laureys, [Diagnosis and investigation of altered states of consciousness]. Reanimation, 2004. 13: p. 368-375.
  11. Marchand, Y., C.D. Lefebvre, and J.F. Connolly, Correlating digit span performance and event-related potentials to assess working memory. International Journal of Psychophysiology, 2006. 62(2): p. 280-9.
  12. Connolly, J.F., et al., Event-related brain potentials as a measure of performance on WISC-III and WAIS-R NI similarities sub-tests. Journal of Clinical and Experimental Neuropsychology, 2006. 28(8): p. 1327-1345.
  13. Connolly, J.F., C.C. Mate-Kole, and B.M. Joyce, Global aphasia: An innovative assessment approach. Journal of Physical Medicine and Rehabilitation, 1999. 80: p. 1309-1315.
  14. Marchand, Y., R.C.N. D'Arcy, and J.F. Connolly, Linking neurophysiological and neuropsychological measures for aphasia assessment. Clinical Neurophysiology, 2002. 113: p. 1715-1722.
  15. Owen, A.M., et al., Detecting awareness in the vegetative state. Science, 2006. 313(5792): p. 1402.
  16. Kübler, A. and N. Birbaumer, Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients‌ Clin Neurophysiol, 2008. 119(11): p. 2658-66.
  17. Kübler, A., et al., Brain-computer communication: unlocking the locked in. Psychol Bull, 2001. 127(3): p. 358-75.
  18. Birbaumer, N., et al., A spelling device for the paralysed. Nature, 1999. 398(6725): p. 297-8.
  19. Birbaumer, N., et al., The thought translation device (TTD) for completely paralyzed patients. IEEE Trans Rehabil Eng, 2000. 8(2): p. 190-3.
  20. Kübler, A., et al., The thought translation device: a neurophysiological approach to communication in total motor paralysis. Exp Brain Res, 1999. 124(2): p. 223-32.
  21. Kübler, A., et al., Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. Arch Phys Med Rehabil, 2001. 82(11): p. 1533-9.
  22. Kübler, A., et al., Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology, 2005. 64(10): p. 1775-7.
  23. Nijboer, F., et al., A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol, 2008. 119(8): p. 1909-16.
  24. Mugler, E., et al., Control of an Internet Browser Using the P300 Event Related Potential. International Journal for Bioelectromagnetism, 2008. 10(1): p. 56-63.
  25. Mugler, E., et al., Design and implementation of a P300-based brain-computer interface for controlling an internet browser. in preparation.
  26. Karim, A.A., et al., Neural internet: Web surfing with brain potentials for the completely paralyzed. Neurorehabil Neural Repair, 2006. 20(4): p. 508-15.
  27. Iturrate, I., M. Antelis, and J. Minguez, Non-Invasive Brain-Actuated Wheelchair based on a P300 Neurophysiological Protocol and Automated Navigation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. under review.
  28. Galan, F., et al., A brain-actuated wheelchair: asynchronous and non-invasive Brain-computer interfaces for continuous control of robots. Clin Neurophysiol, 2008. 119(9): p. 2159-69.
  29. Millan Jdel, R., et al., Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans Biomed Eng, 2004. 51(6): p. 1026-33.
  30. Pfurtscheller, G., et al., Brain oscillations control hand orthosis in a tetraplegic. Neurosci Lett, 2000. 292(3): p. 211-4.
  31. Neuper, C., et al., Motor imagery and EEG-based control of spelling devices and neuroprostheses. Prog Brain Res, 2006. 159: p. 393-409.
  32. Müller-Putz, G.R., et al., Brain-computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation. Biomed Tech (Berl), 2006. 51(2): p. 57-63.
  33. Müller-Putz, G.R., et al., EEG-based neuroprosthesis control: a step towards clinical practice. Neurosci Lett, 2005. 382(1-2): p. 169-74.
  34. Müller-Putz, G.R. and G. Pfurtscheller, Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans Biomed Eng, 2008. 55(1): p. 361-4.
  35. Buch, E., et al., Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke, 2008. 39(3): p. 910-7.
  36. Kotchoubey, B., et al., Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia, 2001. 42(3): p. 406-16.
  37. Strehl, U., et al., Predictors of seizure reduction after self-regulation of slow cortical potentials as a treatment of drug-resistant epilepsy. Epilepsy Behav, 2005. 6(2): p. 156-66.
  38. Leins, U., et al., [Neurofeedback for children with ADHD: a comparison of SCP- and theta/beta-protocols]. Prax Kinderpsychol Kinderpsychiatr, 2006. 55(5): p. 384-407.
  39. Strehl, U., et al., Self-regulation of slow cortical potentials: a new treatment for children with attention-deficit/hyperactivity disorder. Pediatrics, 2006. 118(5): p. 1530-40.
  40. Birbaumer, N. et al., Operant conditioning of the anterior insula in criminal psychopaths. Society for Neuroscience Abstracts, 2008. 595.9.
  41. Blankertz, B., et al., Neurophysiological Predictor of SMR-Based BCI Performance. submitted.
  42. Schalk, G., et al., BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng, 2004. 51(6): p. 1034-43.
  43. Scherer, R., et al. Inside the Graz-BCI: rtsBCI. in Proceedings of the 2nd International
  44. Brain-Computer Interface Workshop and Training Course. 2004. Graz, Austria: Schiele & Schön.
  45. Kleih, S., F. Nijboer, and A. Kübler, Motivation modulates the P300 Amplitude during BCI use. 2009. submitted.
  46. Lukito, S., et al. The Effect of Emotion on P300 Brain-Computer Interface (BCI) Performance in Proceedings of the Neuromath Workshop. 2009. KU Leuven, Belgium.
  47. Vögele, C., et al., Higher heart rate variability is associated with better performance in the P300-BCI. in preparation.
  48. Mühlberger, A., et al., Repeated exposure of flight phobics to flights in virtual reality. Behav Res Ther, 2001. 39(9): p. 1033-50.
  49. Mühlberger, A., et al., Virtual reality for the psychophysiological assessment of phobic fear: responses during virtual tunnel driving. Psychol Assess, 2007. 19(3): p. 340-6.
  50. Kübler, A., et al., Brain Painting - BCI meets art, in 4th International Brain-Computer Interface Workshop and Training Course, G.R. Müller-Putz, et al., Editors. 2008, Verlag der Technischen Universität Graz.: Graz University of Technology, Austria. p. 361-366.
  51. Halder, S., et al. Implementation of SMR based brain painting. in Proceedings of the NeuroMath Workshop. 2009. KU Leuven, Belgium.
  52. Furdea, A., et al., An auditory oddball (P300) spelling system for brain-computer interfaces. Psychophysiology, 2009.
  53. Nijboer, F., et al., An auditory brain-computer interface (BCI). J Neurosci Methods, 2008. 167(1): p. 43-50.
  54. Halder, S., et al., Back to basics: a simplified auditory P300 brain-computer interface paradigm. submitted.
  55. Kübler, A. and C. Neuper, How the Brain controls devices: The Neuropsychology and Neurophysiology of Neurofeedback driven Brain-Computer Interfaces. Progress in Brain Research, 2009. submitted.
  56. Obermaier, B., G.R. Muller, and G. Pfurtscheller, "Virtual keyboard" controlled by spontaneous EEG activity. IEEE Trans Neural Syst Rehabil Eng, 2003. 11(4): p. 422-6.
  57. Scherer, R., et al., An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans Biomed Eng, 2004. 51(6): p. 979-84.
  58. Pfurtscheller, G., et al., 'Thought'--control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett, 2003. 351(1): p. 33-6.
  59. Müller, G.R., C. Neuper, and G. Pfurtscheller, Implementation of a telemonitoring system for the control of an EEG-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2003. 11(1): p. 54-9.
  60. Müller-Putz, G., et al. EEG-basierende Kommunikation: Erfahrungen mit einem Telemonitoringsystem zum Patiententraining. in Beiträge zur 38. Jahrestagung der Deutschen Gesellschaft für Biomedizinische Technik im VDE - BMT. 2004. Ilmenau, Deutschland: Schiele & Schön.
  61. Neuper, C., et al., Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment. Clin Neurophysiol, 2003. 114(3): p. 399-409.
  62. Bianchi, L., et al., Developing wearable bio-feedback systems: a general-purpose platform. IEEE Trans Neural Syst Rehabil Eng, 2003. 11(2): p. 117-9.
  63. Cincotti, F., et al., Vibrotactile feedback for brain-computer interface operation. Comput Intell Neurosci, 2007: p. 48937.
  64. Cincotti, F., et al., Non-invasive brain-computer interface system: towards its application as assistive technology. Brain Res Bull, 2008. 75(6): p. 796-803.
  65. Quitadamo, L.R., M.G. Marciani, and L. Bianchi, Optimization of Brain-Computer Interface Systems by means of XML and BF++Toys. International Journal for Bioelectromagnetism, 2007. 9: p. 172-184.
  66. Quitadamo, L.R., et al., Describing different brain computer interface systems through a unique model: a UML implementation. Neuroinformatics, 2008. 6(2): p. 81-96.
  67. Bianchi, L., et al., Performances evaluation and optimization of brain computer interface systems in a copy spelling task. IEEE Trans Neural Syst Rehabil Eng, 2007. 15(2): p. 207-16.
  68. Monti, M.M., M.R. Coleman, and A.M. Owen, fMRI and the Vegetative State: Solving the behavioral dilemma‌ Annals of the New York Academy of Sciences. in press.
  69. Owen, A.M. and M.R. Coleman, Functional MRI in disorders of consciousness: advantages and limitations. Curr Opin Neurol, 2007. 20(6): p. 632-7.
  70. Owen, A.M. and M.R. Coleman, Functional neuroimaging of the vegetative state. Nat Rev Neurosci, 2008. 9(3): p. 235-43.
  71. Owen, A.M., et al., Using functional magnetic resonance imaging to detect covert awareness in the vegetative state. Arch Neurol, 2007. 64(8): p. 1098-102.
  72. Owen, A.M., When thoughts become actions: functional neuroimaging in the vegetative state. Future Neurology, 2006. 1(6): p. 693-696.
  73. Coleman, M.R., et al., Do vegetative patients retain aspects of language comprehension‌ Evidence from fMRI. Brain, 2007. 130(Pt 10): p. 2494-507.
  74. Davis, M.H., et al., Dissociating speech perception and comprehension at reduced levels of awareness. Proc Natl Acad Sci U S A, 2007. 104(41): p. 16032-7.
  75. Owen, A.M., et al., Residual auditory function in persistent vegetative state: a combined PET and fMRI study. Neuropsychol Rehabil, 2005. 15(3-4): p. 290-306.
  76. Owen, A.M., et al., Using a hierarchical approach to investigate residual auditory cognition in persistent vegetative state. Prog Brain Res, 2005. 150: p. 457-71.
  77. Owen, A.M., et al., Detecting residual cognitive function in persistent vegetative state. Neurocase, 2002. 8(5): p. 394-403.
  78. Menon, D.K., et al., Cortical processing in persistent vegetative state. Wolfson Brain Imaging Centre Team. Lancet, 1998. 352(9123): p. 200.
  79. De Martino, F., et al., Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuroimage, 2008. 43(1): p. 44-58.
  80. Kriegeskorte, N., R. Goebel, and P. Bandettini, Information-based functional brain mapping. Proc Natl Acad Sci U S A, 2006. 103(10): p. 3863-8.
  81. Formisano, E., et al., "Who" is saying "what"‌ Brain-based decoding of human voice and speech. Science, 2008. 322(5903): p. 970-3.
  82. Goebel, R., et al. BOLD brain pong: Self regulation of local brain activity during synchronously scanned, interacting subjects. in 34th Annual Meeting of the Society for Neuroscience. 2004. San Diego.
  83. Goebel, R., et al. Learning to play BOLD Brain Pong: From individual neurofeedback training to brain-brain interactions. in 11th Annual Meeting of the Organization for Human Brain Mapping. 2005. Toronto.
  84. Sorger, B., et al. Voluntary modulation of regional brain activity to different target levels based on real-time fMRI neurofeedback. in 34th Annual Meeting of the Society for Neuroscience. 2004. San Diego.
  85. Dahmen, B., et al. When the brain takes BOLD ’steps’: Controlling differential brain activation levels via real-time fMRI-based neurofeedback training. in Human Brain Mapping 14th. 2008. Melbourne, Australia.
  86. Sorger, B., et al. BOLD communication: When the brain speaks for itself. in 13th Annual Meeting of the Organization for Human Brain Mapping. 2007. Chicago: Elsevier.
  87. Schnakers, C., et al., Diagnostic and prognostic use of bispectral index in coma, vegetative state and related disorders. Brain Inj, 2008. 22(12): p. 926-31.
  88. Schnakers, C., et al., Does the FOUR score correctly diagnose the vegetative and minimally conscious states‌ Ann Neurol, 2006. 60(6): p. 744-5; author reply 745.
  89. Laureys, S., et al., The locked-in syndrome : what is it like to be conscious but paralyzed and voiceless‌ Prog Brain Res, 2005. 150: p. 495-511.
  90. Bruno, M., et al., Locked-in: don't judge a book by its cover. J Neurol Neurosurg Psychiatry, 2008. 79(1): p. 2.
  91. Demertzi, A., et al., Is there anybody in there‌ Detecting awareness in disorders of consciousness. Expert Rev Neurother, 2008. 8(11): p. 1719-30.
  92. Laureys, S., et al., How should functional imaging of patients with disorders of consciousness contribute to their clinical rehabilitation needs‌ Curr Opin Neurol, 2006. 19(6): p. 520-7.
  93. Laureys, S., P. Peigneux, and S. Goldman, Brain Imaging, in Biological Psychiatry, H. D'haenen, et al., Editors. 2002, John Wiley & Sons Inc.: New York. p. 155-166.
  94. Hinterberger, T., et al., A tool for detection of cognitive brain functions in severely brain injured patients integrated in the thought-translation-device. IEEE Transactions of Biomedical Engineering, 2005. 52(2): p. 211-220.
  95. Kotchoubey, B., et al., Reliability of brain responses to the own name in healthy subjects and patients with brain damage, in Brainwaves and Mind: Recent Advances, N.C. Moore and M.K. Arikan, Editors. 2004, Kjellberg, Inc.: New York. p. 75-80.
  96. Kotchoubey, B., et al., Cognitive processing in completely paralyzed patients with amyotrophic lateral sclerosis. European Journal of Neurology, 2003. 10: p. 551-558.
  97. Lang, S. and B. Kotchoubey, Brain responses to number sequences with and without active task requirement. Clinical Neurophysiology, 2002. 113: p. 1734-1741.
  98. Lang, S. and B. Kotchoubey, Learning effects on event-related brain potentials. Neuroreport, 2000. 11(15): p. 3327-3331.
  99. Neumann, N. and B. Kotchoubey, Assessment of cognitive functions in severely paralysed and severely brain-damaged patients: Neuropsychological and electrophysiological techniques. Brain Research Protocols, 2004. 14: p. 25-36.
  100. Kotchoubey, B., et al., Is there a mind‌ Electrophysiology of unconscious patients. News Physiol Sci, 2002. 17: p. 38-42.
  101. Kotchoubey, B., et al., Information processing in severe disorders of consciousness: Vegetative state and minimally conscious state. Clinical Neurophysiology, 2005. 116: p. 2441-2453.
  102. Kotchoubey, B., Apallic syndrome is not apallic: is vegetative state vegetative‌ Neuropsychol Rehabil, 2005. 15(3-4): p. 333-56.
  103. Kotchoubey, B., Editorial: Event-related potentials predict the outcome of the vegetative state. Clinical Neurophysiology, 2007. 118: p. 477-479.
  104. Daltrozzo, J., et al., Predicting coma and other low responsive patients outcome using event-related brain potentials: a meta-analysis. Clin Neurophysiol, 2007. 118(3): p. 606-14.
  105. Kotchoubey, B., et al., Cortical processing in Guillain-Barre syndrome after years of total immobility. Journal of Neurology, 2003. 250: p. 1121-1123.
  106. Kotchoubey, B., et al., Electrocortical and behavioral effects of chronic immobility on word processing. Cognitive Brain Research, 2003. 17: p. 188-199.
  107. Kotchoubey, B., et al., Evidence of cortical learning in vegetative state. J Neurol, 2006. 253(10): p. 1374-6.
  108. Daltrozzo, J., et al., Cortical information processing in coma. Cognitive and Behavioral Neurology, 2009. 22: p. 53-62.
  109. Kotchoubey, B., et al., Semantic processing in a coma patient. Grand Rounds, 2005. 5: p. 37-41.
  110. Matuz, T., et al., Components of quality of life in ALS patients – a qualitative longitudinal study in preparation.
  111. Pfurtscheller, G., et al., Graz-BCI: state of the art and clinical applications. IEEE Trans Neural Syst Rehabil Eng, 2003. 11(2): p. 177-80.
  112. Blankertz, B., et al., The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects. Neuroimage, 2007.
  113. Blankertz, B., et al., The Berlin Brain--Computer Interface: accurate performance from first-session in BCI-naive subjects. IEEE Trans Biomed Eng, 2008. 55(10): p. 2452-62.
  114. Farwell, L.A. and E. Donchin, Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol, 1988. 70(6): p. 510-23.
  115. Müller-Putz, G.R., et al., Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces‌ IEEE Trans Neural Syst Rehabil Eng, 2006. 14(1): p. 30-7.
  116. Allison, B.Z., et al., Towards an independent brain-computer interface using steady state visual evoked potentials. Clin Neurophysiol, 2008. 119(2): p. 399-408.
  117. Sellers, E.W. and E. Donchin, A P300-based brain-computer interface: initial tests by ALS patients. Clin Neurophysiol, 2006. 117(3): p. 538-48.
  118. Müller-Putz, G.R., et al., Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J Neural Eng, 2005. 2(4): p. 123-30.
  119. Gao, X., et al., A BCI-based environmental controller for the motion-disabled. IEEE Trans Neural Syst Rehabil Eng, 2003. 11(2): p. 137-40.
  120. Nielsen, K.D., A.F. Cabrera, and O.F. do Nascimento, EEG based BCI-towards a better control. Brain-computer interface research at Aalborg University. IEEE Trans Neural Syst Rehabil Eng, 2006. 14(2): p. 202-4.
  121. Kübler, A., et al., Predictability of brain-computer communication. Journal of Psychophysiology, 2004. 18: p. 121-129.
  122. Keith, M.W., et al., Implantable functional neuromuscular stimulation in the tetraplegic hand. J Hand Surg [Am], 1989. 14(3): p. 524-30.
  123. Hoffmann, U., et al., An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods, 2008. 167(1): p. 115-25.
  124. Lenhardt, A., M. Kaper, and H.J. Ritter, An adaptive P300-based online brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2008. 16(2): p. 121-30.
  125. Muller, K.R., et al., Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. J Neurosci Methods, 2008. 167(1): p. 82-90.
  126. Glass, I., L. Sazbon, and Z. Groswasser, Mapping "cognitive" event-related potentials in prolonged postcoma unawareness state. Clinical Electroencephalography, 1998. 29(1): p. 19-30.
  127. Becker, F. and I. Reinvang, Successful syllable detection in aphasia despite processing impairments as revealed by event-related potentials. Behav Brain Funct, 2007. 3: p. 6.
  128. Becker, F. and I. Reinvang, Mismatch negativity elicited by tones and speech sounds: Changed topographical distribution in aphasia. Brain and Language, 2007. 100: p. 69-78.
  129. Kotchoubey, B., et al., Stimulus complexity enhances auditory discrimination in patients with extremely severe brain injuries. Neuroscience Letters, 2003. 352: p. 129-132.
  130. Kotchoubey, B., Event-related potential measures of consciousness: two equations with three unknowns. Prog Brain Res, 2005. 150: p. 427-44.
  131. Knight, D.C., et al., Neural substrates mediating human delay and trace fear conditioning. Journal of Neuroscience, 2004. 24(1): p. 218-228.
  132. Boly, M., et al., Intrinsic brain activity in altered states of consciousness: how conscious is the default mode of brain function‌ Ann N Y Acad Sci, 2008. 1129: p. 119-29.
  133. Laureys, S., et al., Impaired effective cortical connectivity in vegetative state: preliminary investigation using PET. Neuroimage, 1999. 9(4): p. 377-82.
  134. Laureys, S., et al., Cerebral metabolism during vegetative state and after recovery to consciousness. J Neurol Neurosurg Psychiatry, 1999. 67(1): p. 121.
  135. Perrin, F., et al., Brain response to one's own name in vegetative state, minimally conscious state, and locked-in syndrome. Arch Neurol, 2006. 63(4): p. 562-9.
  136. Di, H.B., et al., Cerebral response to patient's own name in the vegetative and minimally conscious states. Neurology, 2007. 68(12): p. 895-9.
  137. Sorger, B., et al., Another kind of ‘BOLD response’: Answering multiple choice questions by generating differential single-trial fMRI responses. Prog Brain Res, in press.
  138. Sorger, B., et al., ‘BOLD’ conversations: Automated letter decoding from intentionally generated brain activation. in preparation.
  139. Wolpaw, J.R., et al., Brain-computer interfaces for communication and control. Clin Neurophysiol, 2002. 113(6): p. 767-91.
  140. Kotchoubey, B. and S. Lang, Event-related potentials in an auditory semantic oddball task in humans. Neurosci Lett, 2001. 310(2-3): p. 93-6.
  141. Schnakers, C., et al., Voluntary brain processing in disorders of consciousness. Neurology, 2008. 71(20): p. 1614-20.
  142. Boly, M., et al., When thoughts become action: an fMRI paradigm to study volitional brain activity in non-communicative brain injured patients. Neuroimage, 2007. 36(3): p. 979-92.
  143. Millan Jdel, R. and J. Mourino, Asynchronous BCI and local neural classifiers: an overview of the Adaptive Brain Interface project. IEEE Trans Neural Syst Rehabil Eng, 2003. 11(2): p. 159-61.
  144. Pfurtscheller, G. and A. Aranibar, Event-related cortical desynchronization detected by power measurements of scalp EEG. Electroencephalogr Clin Neurophysiol, 1977. 42(6): p. 817-26.
  145. Pfurtscheller, G. and F.H. Lopes da Silva, Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol, 1999. 110(11): p. 1842-57.
  146. Cincotti, F., et al., Classification of EEG mental patterns by using two scalp electrodes and Mahalanobis distance-based classifiers. Methods Inf Med, 2002. 41(4): p. 337-41.
  147. Cincotti, F., et al., The use of EEG modifications due to motor imagery for brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2003. 11(2): p. 131-3.
  148. Millan, J.d.R., et al., A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans Neural Netw, 2002. 13(3): p. 678-86.
  149. Scherer, R., et al., Sensorimotor EEG patterns during motor imagery in hemiparetic stroke patients. International Journal for Bioelectromagnetism, 2007. 9: p. 155-162.
  150. Bernat, J.L., Chronic disorders of consciousness. Lancet, 2006. 367(9517): p. 1181-92.
  151. Babiloni, F., et al., Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function. Neuroimage, 2005. 24(1): p. 118-31.
  152. Kobylarz, E.J. and N.D. Schiff, Neurophysiological correlates of persistent vegetative and minimally conscious states. Neuropsychol Rehabil, 2005. 15(3-4): p. 323-32.
  153. Haynes, J.D. and G. Rees, Decoding mental states from brain activity in humans. Nat Rev Neurosci, 2006. 7(7): p. 523-34.
  154. Kamitani, Y. and F. Tong, Decoding the visual and subjective contents of the human brain. Nat Neurosci, 2005. 8(5): p. 679-85.
  155. Haxby, J.V., et al., Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 2001. 293(5539): p. 2425-30.
  156. LaConte, S.M., S.J. Peltier, and X.P. Hu, Real-time fMRI using brain-state classification. Hum Brain Mapp, 2007. 28(10): p. 1033-44.
  157. Haynes, J.D., et al., Reading hidden intentions in the human brain. Curr Biol, 2007. 17(4): p. 323-8.
  158. Kotchoubey, B., et al., Brain potentials in human patients with severe diffuse brain damage. Neuroscience Letters, 2001. 301: p. 37-40.
  159. Tangermann, M., et al. Playing Pinball with non-invasive BCI. in NIPS.
  160. Vaughan, T.M., et al., The Wadsworth BCI Research and Development Program: at home with BCI. IEEE Trans Neural Syst Rehabil Eng, 2006. 14(2): p. 229-33.
  161. Allison, B.Z., E.W. Wolpaw, and J.R. Wolpaw, Brain-computer interface systems: progress and prospects. Expert Rev Med Devices, 2007. 4(4): p. 463-74.
  162. Lang, S., et al., [What are you doing when you are doing nothing‌ ERP components without a cognitive task]. Z Exp Psychol, 1997. 44(1): p. 138-62.
  163. Schalk, G., et al., EEG-based communication: presence of an error potential. Clin Neurophysiol, 2000. 111(12): p. 2138-44.
  164. Ferrez, P., Error-Related EEG Potentials in Brain-Computer Interfaces, in Electric and Electronic 2007, École Polytechnique Féderalé De Lausanne: Lausanne.
  165. Kreilinger, A., Combination of Motor Imagery and Error Potential Detection to Control an Artificial Limb: Development and Implementation of Basic Methodology 2008, TU Graz: Graz, Austria.
  166. Huppert, T.J., et al., A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. Neuroimage, 2006. 29(2): p. 368-82.
  167. Hans, P., H. Marechal, and V. Bonhomme, Effect of propofol and sevoflurane on coughing in smokers and non-smokers awakening from general anaesthesia at the end of a cervical spine surgery. Br J Anaesth, 2008. 101(5): p. 731-7.
  168. Bostanov, V. and B. Kotchoubey, Recognition of affective prosody: Continuous wavelet measures of event-related brain potentials to emotional exclamations. Psychophysiology, 2004. 41: p. 259-268.
  169. Bostanov, V. and B. Kotchoubey, The t-CWT: A new ERP detection and quantification method based on the continuous wavelet transform and Student's t-statistics. Clinical Neurophysiology, 2006. 117: p. 2627-2644.
  170. Charlton, E., Ethical guidelines for pain research in humans. Committee on Ethical Issues of the International Association for the Study of Pain. Pain, 1995. 63(3): p. 277-8.
  171. World Medical Association Declaration of Helsinki. Recommendations guiding physicians in biomedical research involving human subjects. Cardiovasc Res, 1997. 35(1): p. 2-3.
  172. Bonhomme, V., et al., The effect of clonidine infusion on distribution of regional cerebral blood flow in volunteers. Anesth Analg, 2008. 106(3): p. 899-909, table of contents.
  173. Bostanov, V., BCI competition 2003 - Data sets Ib and IIB: Feature extraction from event-related brain potentials with the Continuous Wavelet Transform and the t-value scalogram. IEEE Transactions of Biomedical Engineering, 2003. 51(6): p. 1057-1061.
  174. Leclercq, Y., et al., Rejection of pulse related artefact (PRA) from continuous electroencephalographic (EEG) time series recorded during functional magnetic resonance imaging (fMRI) using constraint independent component analysis (cICA). Neuroimage, 2009. 44(3): p. 679-91.
  175. Kotchoubey, B., Vegetative state, in Encyclopedia of Neuroscience, L. Squire, Editor. 2009, Elsevier: Amsterdam.
  176. Bauby, J.-D., The Diving Bell and the Butterfly. 1997, London: Fourth Estate.
  177. Birbaumer, N. and L.G. Cohen, Brain-computer interfaces: communication and restoration of movement in paralysis. J Physiol, 2007. 579(Pt 3): p. 621-36.
  178. Kübler, A. and B. Kotchoubey, Brain-computer interfaces in the continuum of consciousness. Curr Opin Neurol, 2007. 20(6): p. 643-9.
  179. Lakerveld, J., B. Kotchoubey, and A. Kubler, Cognitive function in patients with late stage amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry, 2008. 79(1): p. 25-9.
  180. Lulé, D., et al., Living inside: Quality of Life in patients with locked-in syndrome. Progress in Brain Research. submitted.
  181. Bruno, M.A., et al., [Blink and you live: the locked-in syndrome]. Rev Neurol (Paris), 2008. 164(4): p. 322-35.
  182. Bruno, M.A., et al., [Life with Locked-In syndrome]. Rev Med Liege, 2008. 63(5-6): p. 445-51.
  183. Birbaumer, N., Brain-computer-interface research: coming of age. Clin Neurophysiol, 2006. 117(3): p. 479-83.
  184. Laureys, S., A.M. Owen, and N.D. Schiff, Brain function in coma, vegetative state, and related disorders. Lancet Neurol, 2004. 3(9): p. 537-46.
  185. Bruno, M., et al., Overall quality of life in chronic locked-in syndrome. In preparation.
  186. Hammer, E.M., et al., Validity of the ALS-Depression-Inventory (ADI-12)--a new screening instrument for depressive disorders in patients with amyotrophic lateral sclerosis. J Affect Disord, 2008. 109(1-2): p. 213-9.
  187. Kurt, A., et al., Depression and Anxiety in Individuals with Amyotrophic Lateral Sclerosis: Epidemiology and Management. CNS Drugs. 21(4): p. 279-291.

 


Possible inaccuracies of information are under the responsability of the project team. The text reflects solely the authors´views. The European Commission is not liable for any use that may be made of the information contained therein.
http://cordis.europa.eu/fp7/ict/    http://ec.europa.eu/information society/index en/htm