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Abramson, L.Y. and H.A. Sackeim, A paradox in depression: uncontrollability and self-blame. Psychol Bull, 1977. 84(5): p. 838-51.

Albinet, C.T., et al., Increased heart rate variability and executive performance after aerobic training in the elderly. Eur J Appl Physiol, 2010. 109(4): p. 617-24.

Allison, B.Z. and C. Neuper, Could anyone use a BCI? , in Brain-Computer Interfaces. Applying our Minds to Human-Computer Interaction, D.S. Tan and A. Nijholt, Editors. 2010, Springer: Heidelberg. p. 35-54.

Angelakis, E., Lubar, J. F., Stathopoulou, S., & Kounios, J. (2004). Peak alpha frequency: an electroencephalographic measure of cognitive preparedness. Clinical neurophysiology, 115(4), 887-97. doi:10.1016/j.clinph.2003.11.034

Angelakis, E., Stathopoulou, S., Frymiare, J. L., Green, D. L., Lubar, J. F., & Kounios, J. (2007). EEG neurofeedback: a brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. The Clinical neuropsychologist, 21(1), 110-29. doi:10.1080/13854040600744839

Antonovsky, A., Health, Stress and Coping: New Perspectives on Mental and Physical Well-Being. . 1979, San Francisco: Jossey-Bass.

Appelhans, B.M. and L.J. Luecken, Heart rate variability as an index of regulated emotional responding. Rev Gen Psychol, 2006. 10: p. 229-240.

Astolfi, L., et al., Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum Brain Mapp, 2007. 28(2): p. 143-157.

Astolfi, L., et al., Imaging functional brain connectivity patterns from high-resolution EEG and fMRI via graph theory. Psychophysiology, 2007. 44(6): p. 880-893.

Astolfi, L., et al., Study of the time-varying cortical connectivity changes during the attempt of foot movements by spinal cord injured and healthy subjects. Conf Proc IEEE Eng Med Biol Soc, 2009. 2009: p. 2208-11.

Astolfi, L., et al., Time-varying cortical connectivity estimation from non-invasive high-resolution EEG recordings. Journal of Psychophysiology, 2010. 24(2): p. 83-90.

Astolfi, L., et al., Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators. IEEE Trans Biomed Eng, 2008. 55(3): p. 902-913.

Astolfi, L., et al., Time-varying cortical connectivity by high resolution EEG and directed transfer function: simulations and application to finger tapping data. Conf Proc IEEE Eng Med Biol Soc, 2004. 6: p. 4405-8.

Astolfi L., et al, Estimation of the cortical connectivity patterns during the intention of limb movements. IEEE Eng Med Biol Mag. 2006 Jul-Aug;25(4):32-8.

Astolfi, L., et al., Assessing cortical functional connectivity by partial directed coherence: simulations and application to real data. IEEE Trans Biomed Eng, 2006. 53(9): p. 1802-1812.

Astolfi, L., et al., Estimation of the cortical connectivity by high-resolution EEG and structural equation modeling: simulations and application to finger tapping data. IEEE Trans Biomed Eng, 2005. 52(5): p. 757-768.

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. 118-131.

Baccala, L.A. and K. Sameshima, Partial directed coherence: a new concept in neural structure determination. Biol Cybern, 2001. 84(6): p. 463-474.

Barker-Collo, S. and V. Feigin, The impact of neuropsychological deficits on functional stroke outcomes. Neuropsychol Rev, 2006. 16(2): p. 53-64.

Barker-Collo, S., Feigin, V. L., Parag, V., Lawes, C. M. M., & Senior, H. (2010). Auckland Stroke Outcomes Study. Part 2: Cognition and functional outcomes 5 years poststroke. Neurology, 75(18), 1608-16. doi:10.1212/WNL.0b013e3181fb44c8

Bauernfeind, G., et al., Development, set-up and first results for a one-channel near-infrared spectroscopy system. Biomed Tech (Berl), 2008. 53(1): p. 36-43.

Bauernfeind, G., et al. Cortical effects of BCI training measured with fNIRS. in TOBI Workshop 2010. 2010.

Berner, I., Schabus, M, Wienerroither, T., & Klimesch, W. (2006). The significance of sigma neurofeedback training on sleep spindles and aspects of declarative memory. Applied psychophysiology and biofeedback, 31(2), 97-114. doi:10.1007/s10484-006-9013-7

Bernheim, J. and M. Buyse, The anamnestic comparative self assessment for measuring the subjective quality of life of cancer patients. J Psychosoc Oncol 1983. 1: p. 25-38.

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.

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.

Birbaumer, N., et al., A spelling device for the paralysed. Nature, 1999. 398(6725): p. 297-298.

Birbaumer, N., et al., Slow potentials of the cerebral cortex and behavior. Physiol Rev, 1990. 70(1): p. 1-41.

Blankertz, B., et al., Neurophysiological predictor of SMR-based BCI performance. Neuroimage, 2010. 51(4): p. 1303-9.

Bortfeld, H., E. Wruck, and D.A. Boas, Assessing infants' cortical response to speech using near-infrared spectroscopy. Neuroimage, 2007. 34(1): p. 407-415.

Braddock, C.H., 3rd, et al., Informed decision making in outpatient practice: time to get back to basics. JAMA, 1999. 282(24): p. 2313-20.

Brainin, M., et al., Acute neurological stroke care in Europe: results of the European Stroke Care Inventory. Eur J Neurol, 2000. 7(1): p. 5-10.

Brainin, M., A. Dachenhausen, and M. Steiner, Epidemiology of stroke. Wien Med Wochenschr, 2003. 153(1-2): p. 3-5.

Brennan, D.M., S. Mawson, and S. Brownsell, Telerehabilitation: enabling the remote delivery of healthcare, rehabilitation, and self management. Stud Health Technol Inform, 2009. 145: p. 231-48.

Brownsell, S., H. Aldred, and M.S. Hawley, The role of telecare in supporting the needs of elderly people. Journal of Telemedicine and Telecare, 2007. 13: p. 293-297.

Brunner, C., et al., Improved signal processing approaches in an offline simulation of a hybrid brain-computer interface. J Neurosci Methods, 2010. 188(1): p. 165-173.

Carter, A.R., et al., Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke. Ann Neurol, 2010. 67(3): p. 365-375.

Charles, C., A. Gafni, and T. Whelan, Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med, 1997. 44(5): p. 681-92.

Cherniack, E.P., Not just fun and games: applications of virtual reality in the identification and rehabilitation of cognitive disorders of the elderly. Disabil Rehabil Assist Technol. , 2010. Epub ahead of print.

Cicerone, K.D., et al., Evidence-based cognitive rehabilitation: updated review of the literature from 1998 through 2002. Arch Phys Med Rehabil, 2005. 86(8): p. 1681-1692.

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.

Cincotti, F., et al., Vibrotactile feedback for brain-computer interface operation. Comput Intell Neurosci, 2007: p. 48937.

Coyle, S.M., T.E. Ward, and C.M. Markham, Brain-computer interface using a simplified functional near-infrared spectroscopy system. J Neural Eng, 2007. 4(3): p. 219-226.

Crossmann, A., et al., A randomized controlled trial of secondary prevention of anxiety and distress in a German sample of patients with an implantable cardioverter defibrillator. Psychosom Med, 2010. 72(5): p. 434-441.

Csikszentmihalyi, M., Creativity: Flow and the Psychology of Discovery and Invention. 1996, New York: HarperCollins.

Dankel, D.D. and M.Ó. Kristmundsdóttir. REPS: A Rehabilitation Expert System For Post Stroke Patients. in Artificial Intelligence in Medicine Europe (AIME-05). 2005. Aberdeen, Scotland.

De Vico Fallani, F., et al., Evaluation of the brain network organization from EEG signals: a preliminary evidence in stroke patient. Anat Rec (Hoboken), 2009. 292(12): p. 2023-2031.

Diener, H.C. and N. Putzki, Leitlinien für Diagnostik und Therapie in der Neurologie. 4. ed. 2008, Stuttgart: Thieme.

Dobel, C., et al., Slow event-related brain activity of aphasic patients and controls in word comprehension and rhyming tasks. Psychophysiology, 2002. 39(6): p. 747-758.

Doppelmayr, M., et al., An attempt to increase cognitive performance after stroke with neurofeedback. Biofeedback 2007. 35 (4): p. 126-130.

Düzel, E., Penny, W. D., & Burgess, N. (2010). Brain oscillations and memory. Current opinion in neurobiology, 20(2), 143-9. doi:10.1016/j.conb.2010.01.004

Egner, T. and J.H. Gruzelier, Learned self-regulation of EEG frequency components affects attention and event-related brain potentials in humans. Neuroreport, 2001. 12(18): p. 4155-4159.

Egner, T. and J.H. Gruzelier, EEG biofeedback of low beta band components: frequency-specific effects on variables of attention and event-related brain potentials. Clin Neurophysiol, 2004. 115(1): p. 131-9.

Enzinger, C., et al. 5000 finger grip movements with a new robotic hand rehabilitation device – effects on grip abilities and functional MRI. in European Stroke Conference. 2008. Nice, France.

Enzinger, C., et al., Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. Exp Brain Res, 2008. 190(2): p. 215-223.

Faller, H., [Shared decision making: an approach to strengthening patient participation in rehabilitation]. Rehabilitation (Stuttg), 2003. 42(3): p. 129-35.

Farin, E., P. Follert, and W.H. Jäckel, Die Therapiezielfestlegung bei Patienten mit psychischen Belastungen in der orthopädischen und kardiologischen Rehabilitation. Die Rehabilitation, 2002. 41: p. 389-400.

Fell, J., Klaver, P., Elfadil, H., Schaller, C., Elger, C. E., & Fernandez, G. (2003). Rhinal-hippocampal theta coherence during declarative memory formation: interaction with gamma synchronization? European Journal of Neuroscience, 17(5), 1082-1088. doi:10.1046/j.1460-9568.2003.02522.x

Fink, A., et al., EEG alpha band dissociation with increasing task demands. Brain Res Cogn Brain Res, 2005. 24(2): p. 252-9.

Forkmann, T., et al., Psychometric evaluation of the Rasch-based depression screening in patients with neurologic disorders. Arch Phys Med Rehabil, 2010. 91(8): p. 1188-93.

Friedrich, E.V., et al., A scanning protocol for a sensorimotor rhythm-based brain-computer interface. Biol Psychol, 2009. 80(2): p. 169-75.

Gauggel, S., The theoretical and empirical foundation of neuropsychological treatment: Neuropsychotherapy or brain jogging? Zeitschrift für Neuropsychologie, 2003. 14(4): p. 217-246.

Gauggel, S., et al., Patient-staff agreement in the Barthel Index at admission and discharge in a sample of elderly stroke patients. Rehabilitation Psychology, 2004. 49(1): p. 21-27.

Gevensleben, H., et al., Is neurofeedback an efficacious treatment for ADHD? A randomised controlled clinical trial. J Child Psychol Psychiatry, 2009. 50(7): p. 780-9.

Gevensleben, H., et al., Neurofeedback training in children with ADHD: 6-month follow-up of a randomised controlled trial. Eur Child Adolesc Psychiatry, 2010. 19(9): p. 715-24.

Giardino, N.D., L. Chan, and S. Borson, Combined heart rate variability and pulse oximetry biofeedback for chronic obstructive pulmonary disease: preliminary findings. Appl Psychophysiol Biofeedback, 2004. 29(2): p. 121-33.

Grabner, R.H., et al., Event-related EEG theta and alpha band oscillatory responses during language translation. Brain Res Bull, 2007. 72(1): p. 57-65.

Granger, C.W.J., Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 1969. 37: p. 424-438.

Gani, C., N. Birbaumer, and U. Strehl, Long term effects after feedback of slow coritcal potentials and of theta-beta-amplitudes in children with attention-deficit/hyperactivity disorder. International Journal of Bioelectromagnetism, 2008. 10(4): p. 209 - 232.

Grieshofer, P., A. Kollreider, and R. Scherer. The lokomat as a possibility in the rehabilitation of patients with neurological disorders. in 16th Meeting of the European Neurological Society. 2006. Lausanne, Switzerland.

Grieshofer, P., et al. Robotic in hand rehabilitation - an innovation in hand therapy. in XV European Stroke Conference. 2006. Brussels, Belgium.

Grunewald-Zuberbier, E., et al., Contingent negative variation and alpha attenuation responses in children with different abilities to concentrate. Electroencephalogr Clin Neurophysiol, 1978. 44(1): p. 37-47.

Gruzelier, J., A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cogn Process, 2009. 10 Suppl 1: p. S101-9.

Gruzelier, J. and T. Egner, Critical validation studies of neurofeedback. Child Adolesc Psychiatr Clin N Am, 2005. 14(1): p. 83-104, vi.

Gruzelier, J., et al., Acting performance and flow state enhanced with sensory-motor rhythm neurofeedback comparing ecologically valid immersive VR and training screen scenarios. Neurosci Lett, 2010. 480(2): p. 112-6.

Gruzelier, J., Egner, T., & Vernon, D. (2006). Validating the efficacy of neurofeedback for optimising performance. Progress in brain research, 159, 421-31. doi:10.1016/S0079-6123(06)59027-2

Hachinski, V., et al., Stroke: working toward a prioritized world agenda. Int J Stroke, 2010. 5(4): p. 238-56.

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.

Hansen, A.L., B.H. Johnsen, and J.F. Thayer, Vagal influence on working memory and attention. Int J Psychophysiol, 2003. 48(3): p. 263-74.

Hanslmayr, S., et al., Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Appl Psychophysiol Biofeedback, 2005. 30(1): p. 1-10.

Hawley, M.S., et al. Speech technology for e-inclusion of people with physical disability and disordered speech. in Interspeech. 2005. Lisbon, Portugal.

Heinrich, H., H. Gevensleben, and U. Strehl, Annotation: neurofeedback - train your brain to train behaviour. J Child Psychol Psychiatry, 2007. 48(1): p. 3-16.

Herrmann, C.S. and T. Demiralp, Human EEG gamma oscillations in neuropsychiatric disorders. Clin Neurophysiol, 2005. 116(12): p. 2719-33.

Herrmann, M.J., A.C. Ehlis, and A.J. Fallgatter, Frontal activation during a verbal-fluency task as measured by near-infrared spectroscopy. Brain Res Bull, 2003. 61(1): p. 51-6.

Hickey, A.M., et al., A new short form individual quality of life measure (SEIQoL-DW): Application in a cohort of individuals with HIV/AIDS. British Medical Journal, 1996. 313: p. 29–33.

Hirth, C., et al., Simultaneous assessment of cerebral oxygenation and hemodynamics during a motor task. A combined near infrared and transcranial Doppler sonography study. Adv. Exp. Med. Biol. , 1997. 411: p. 471–480.

Hoedlmoser, K., Pecherstorfer, T., Gruber, G., Anderer, P., Doppelmayr, Michael, Klimesch, Wolfgang, & Schabus, Manuel. (2008). Instrumental conditioning of human sensorimotor rhythm (12-15 Hz) and its impact on sleep as well as declarative learning. Sleep, 31(10), 1401-8.

Hoshi, Y. and M. Tamura, Near-infrared optical detection of sequential brain activation in the prefrontal cortex during mental tasks. Neuroimage, 1997. 5(4 Pt 1): p. 292-7.

Iversen, I.H., et al., A brain-computer interface tool to assess cognitive functions in completely paralyzed patients with amyotrophic lateral sclerosis. Clin Neurophysiol, 2008. 119(10): p. 2214-23.

James, G.A., et al., Changes in resting state effective connectivity in the motor network following rehabilitation of upper extremity poststroke paresis. Top Stroke Rehabil, 2009. 16(4): p. 270-81.

Jipp, M., et al. Individual Ability-based System Configuration. in IEEE International Conference on Systems, Man and Cybernetics. 2008.

Jobsis, F.F., Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science, 1977. 198(4323): p. 1264-7.

Johansson, T. and C. Wild, Telerehabilitation in stroke care – a systematic review. Journal of Telemedicine and Telecare 2010. 1–6

Juhász, C., A. Kamondi, and I. Szirmai, Spectral EEG analysis following hemispheric stroke: evidences of transhemispheric diaschisis. Acta Neurol Scand, 1997. 96(6): p. 397-400.

Juhász, C., Kamondi, a, & Szirmai, I. (1997). Spectral EEG analysis following hemispheric stroke: evidences of transhemispheric diaschisis. Acta neurologica Scandinavica, 96(6), 397-400.

Kang, Y.J., et al., Development and clinical trial of virtual reality-based cognitive assessment in people with stroke: preliminary study. Cyberpsychol Behav, 2008. 11(3): p. 329-39.

Karavidas, M.K., et al., Preliminary results of an open label study of heart rate variability biofeedback for the treatment of major depression. Appl Psychophysiol Biofeedback, 2007. 32(1): p. 19-30.

Kaufmann, T., et al., ARTiiFACT – A tool for heart rate artifact processing and heart rate variability analysis. Behavior Research Methods, under revision.

Kleih, S.C., et al., Motivation modulates the P300 amplitude during brain-computer interface use. Clin Neurophysiol, 2010. 121(7): p. 1023-31.

Kleih, S.C., et al., Out of the frying pan into the fire - the P300 based BCI faces real world challenges, in Progress in Brain Research: Brain Machine Interfaces - Implications For Science, Clinical Practice And Society, Schouenborg, Editor. submitted, Elsevier: New York.

Klimesch, W., P. Sauseng, and C. Gerloff, Enhancing cognitive performance with repetitive transcranial magnetic stimulation at human individual alpha frequency. Eur J Neurosci, 2003. 17(5): p. 1129-33.

Klimesch, W, Doppelmayr, M., Schimke, H., & Ripper, B. (1997). Theta synchronization and alpha desynchronization in a memory task.pdf. Psychophysiology, 34, 169-176.

Klimesch, W. (1996). Memory processes, brain oscillations and EEG synchronization. International journal of psychophysiology, 24(1-2), 61-100.

Klimesch, W. (1997). EEG-alpha rhythms and memory processes. International journal of psychophysiology, 26(1-3), 319-40.

Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain research. Brain research reviews, 29(2-3), 169-95. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10209231

Klimesch, Wolfgang, Schack, B., & Sauseng, P. (2005). The Functional Significance of Theta and Upper Alpha Oscillations. Experimental Psychology (formerly “Zeitschrift für Experimentelle Psychologie”), 52(2), 99-108. doi:10.1027/1618-3169.52.2.99

Knyazev, G.G., Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neurosci Biobehav Rev, 2007. 31(3): p. 377-95.

Kofler, B., G. Harrer, and G. Ladurner, Relations between amplitude reduction of event related potentials (CNV) and concentration performance in vascular dementia. Nervenarzt, 1987. 58(11): p. 700-4.

Kolominsky-Rabas, P.L. and P.U. Heuschmann, Incidence, etiology and long-term prognosis of stroke. Fortschr Neurol Psychiatr, 2002. 70(12): p. 657-62.

Kramer, A.F., et al., Environmental influences on cognitive and brain plasticity during aging. J Gerontol A Biol Sci Med Sci, 2004. 59(9): p. M940-57.

Kronenberg, G., J. Katchanov, and M. Endres, Post-stroke depression: clinical aspects, epidemiology, therapy, and pathophysiology. Nervenarzt, 2006. 77(10): p. 1176, 1179-1182, 1184-1185.

Kubler, 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.

Kubler, 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.

Kubler, 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.

Kubler, A., et al., Brain-computer communication: unlocking the locked-in. Psychological Bulletin, 2001. 127: p. 358-375.

Kubler, A., et al., Severity of depressive symptoms and quality of life in patients with amyotrophic lateral sclerosis. Neurorehabil Neural Repair, 2005. 19(3): p. 182-93.

Kubler, A., et al., Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology, 2005. 64: p. 1775-1777.

Kubler, A. and C. Neuper, Gehirn-Computer Schnittstellen (brain-computer interfaces): Anwendungen und Perspektiven [Brain-computer interfaces: applications and prospects]. Neuroforum, 2008. 2: p. 204-210.

Kubler, A., et al. How much learning is involved in BCI control? in BCI Meeting 2010 - Fourth international Meeting. 2010. Asilomar, Californien.

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.

Lecomte, G. (2011). The Effects of Neurofeedback Training on Memory Performance in Elderly Subjects. Psychology, 02(08), 846-852. doi:10.4236/psych.2011.28129

Lehrer, P., et al., Voluntarily produced increases in heart rate variability modulate autonomic effects of endotoxin induced systemic inflammation: an exploratory study. Appl Psychophysiol Biofeedback, 2010. 35(4): p. 303-15.

Leins, U., et al., Neurofeedback for children with ADHD: a comparison of SCP and Theta/Beta protocols. Appl Psychophysiol Biofeedback, 2007. 32(2): p. 73-88.

Lezak, M.D., Assessment of rehabilitation planning, in Neuropsychological rehabilitation, R.J. Meier, A.C. Benton, and L. Diller, Editors. 1987, Livingstone: Edinburgh.

Longoni, F., et al., Functional reorganisation after training of alertness in two patients with right hemisphere lesions. Zeitschrift für Neuropsychologie, 2000. 11: p. 250-261.

Lukito, S., et al., Depressed mood, emotion and communication using an oddball (P300) brain-computer interface (BCI). Psychophysiology, submitted.

Lutzenberger, W., L.E. Roberts, and N. Birbaumer, Memory performance and area-specific self-regulation of slow cortical potentials: dual-task interference. Int J Psychophysiol, 1993. 15(3): p. 217-26.

Maguire, M.C., User-Centred Requirements Handbook, H.R. Institute, Editor. 1998, RESPECT Consortium 1998.

Martin, P.I., et al., Transcranial magnetic stimulation as a complementary treatment for aphasia. Semin Speech Lang, 2004. 25(2): p. 181-91.

Matuz, T., et al., Coping with amyotrophic lateral sclerosis: an integrative view. J Neurol Neurosurg Psychiatry, 2010. 81(8): p. 893-8.

McIntosh, A.R. and F. Gonzalez-Lima, Structural equation modelling and its application to network analysis in functional brain imaging. Hum. Brain Mapp, 1994. 2: p. 2–22.

Meule, A., et al., Heart rate variability biofeedback reduces food cravings in high food cravers. in preparation.

Meule, A., C. Vögele, and A. Kübler, Psychometrische Evaluation der deutschen Barratt Impulsiveness Scale - Kurzversion (BIS-15) [Psychometric evaluation of the German Barratt Impulsiveness Scale - Short Version (BIS-15)]. Diagnostica, in press.

Millan, J.D., et al., Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. Front Neurosci, 2010. 4.

Mitchell, D.J., et al., Frontal-midline theta from the perspective of hippocampal "theta". Prog Neurobiol, 2008. 86(3): p. 156-85.

Muhlberger, 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.

Muhlberger, A., et al., Repeated exposure of flight phobics to flights in virtual reality. Behav Res Ther, 2001. 39(9): p. 1033-50.

Muller, 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.

Munssinger, J.I., et al., Brain Painting: First Evaluation of a New Brain-Computer Interface Application with ALS-Patients and Healthy Volunteers. Front Neurosci, 2010. 4: p. 182.

Nair, R.D. and N.B. Lincoln, Cognitive rehabilitation for memory deficits following stroke. Cochrane Database Syst Rev, 2007(3): p. CD002293.

Neuper, C., et al., Clinical application of an EEG - based brain - computer interface: a case study in a patient with serve motor impairment. Clinical neurophysiology 2003. 114(3): p. 399 - 409.

Neuper, C., et al., Long-term stability and consistency of EEG event-related (de-)synchronization across different cognitive tasks. Clin Neurophysiol, 2005. 116(7): p. 1681-94.

Neuper, C., et al., Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. Clin Neurophysiol, 2009. 120(2): p. 239-47.

Nijboer, F., N. Birbaumer, and A. Kubler, The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study. Front Neurosci, 2010. 4.

O'Connor, A.M., et al., Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev, 2003(2): p. CD001431.

Opitz, P.J., Weltprobleme. 4. ed. 1995, Bonn: Bundeszentrale für politische Bildung.

Owen, A.M., et al., Putting brain training to the test. Nature, 2010. 465(7299): p. 775-8.

Palva, S. and J.M. Palva, New vistas for alpha-frequency band oscillations. Trends Neurosci, 2007. 30(4): p. 150-8.

Pfurtscheller, G., et al., Focal frontal (de)oxyhemoglobin responses during simple arithmetic. Int J Psychophysiol, 2010. 76(3): p. 186-92.

Pfurtscheller, G. and C. Neuper, Dynamics of sensorimotor ocillations in a mtor tsk in Brain-computer interfaces: Revolutionizing human-computer interaction, G. Graimann, B. Allison, and G. Pfurtscheller, Editors. 2010, Springer: Heidelberg.

Pfurtscheller, G., et al., Brain oscillations control hand orthosis in a tetraplegic. Neuroscience letters, 2000. 292 (2): p. 211 - 214.

Pfurtscheller, G., et al., Viewing moving objects in virtual reality can change dynamics of sensorimotor EEG rhythms. Presence, 2007. 16(1): p. 111-118.

Pfurtscheller, G., et al., Walking from thought. Brain Res, 2006. 1071(1): p. 145-52.

Pichiorri F, et al, Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness. J Neural Eng. 2011 Apr;8(2):025020.

Prigatano, G.P., Principles of neuropsychological rehabilitation. 1999, New York: Oxford University Press.

Prigatano, G.P., Neuropsychologische Rehabilitation - Grundlagen und Praxis. 2004, Berlin: Springer.

Proot, I.M., et al., Stroke patients' needs and experiences regarding autonomy at discharge from nursing home. Patient Educ Couns, 2000. 41(3): p. 275-83.

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.

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.

Radloff, L.S., The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement 1977. 1: p. 385-401.

Real, R.G.L., et al., Controllability and living in the present are important for well-being in ALS: an ESM study. submitted.

Raymond, J., et al., The effects of alpha/theta neurofeedback on personality and mood. Brain Res Cogn Brain Res, 2005. 23(2-3): p. 287-92.

Riva, G., et al., Virtual-reality-based multidimensional therapy for the treatment of body image disturbances in binge eating disorders: a preliminary controlled study. IEEE Trans Inf Technol Biomed, 2002. 6(3): p. 224-34.

Riva, G., F. Mantovani, and A. Gaggioli, Presence and rehabilitation: toward second-generation virtual reality applications in neuropsychology. J Neuroeng Rehabil, 2004. 1(1): p. 9.

Robertson, I., Does computerized cognitive rehabilitation work? A review. Aphasiology, 1990. 4: p. 381-405.

Roelands, M., et al., Clinical practice guidelines to improve shared decision-making about assistive device use in home care: a pilot intervention study. Patient Educ Couns, 2004. 55(2): p. 252-64.

Rockstroh, B., et al., Slow cortical potentials and response speed. Prog Brain Res, 1980. 54: p. 431-4.

Ros, T., et al., Endogenous control of waking brain rhythms induces neuroplasticity in humans. Eur J Neurosci, 2010. 31(4): p. 770-8.

Rosahl, S.K. and R.T. Knight, Role of prefrontal cortex in generation of the contingent negative variation. Cereb Cortex, 1995. 5(2): p. 123-34.

Rosler, F., M. Heil, and B. Roder, Slow negative brain potentials as reflections of specific modular resources of cognition. Biol Psychol, 1997. 45(1-3): p. 109-41.

Sauseng, P., et al., Brain oscillatory substrates of visual short-term memory capacity. Curr Biol, 2009. 19(21): p. 1846-52.

Schabus, Manuel, Gruber, G., Parapatics, S., Sauter, C., Klösch, G., Anderer, P., Klimesch, Wolfgang, et al. (2004). Sleep spindles and their significance for declarative memory consolidation. Sleep, 27(8), 1479-85.

Schalk, G., et al., BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng, 2004. 51(6): p. 1034-43.

Schedlbauer, A., P. Davies, and T. Fahey, Interventions to improve adherence to lipid lowering medication. Cochrane Database Syst Rev, 2010(3): p. CD004371.

Scherer, R., et al. Roboterunterstützte Rehabilitation der Hand nach Schlaganfall. in Gemeinsame Jahrestagung der Deutschen, Österreichischen und Schweizerischen Gesellschaften für Biomedizinishce Technik. 2006. ETH Zürich, Schweiz.

Scherer, R., et al., Sensorimotor EEG patterns during motor imagery in hemiparetic stroke patients. International Journal of Bioelectromagnetism, 2007. 9(3): p. 155-162.

Scheibler, F., et al., Shared decision-making. GGW, 2005. 1: p. 23-31.

Scherer, R., et al. Characterization of multi-finger twist motion toward robotic rehabilitation. in IEEE 11th International Conference on Rehabilitation Robotics, Kyoto Internatiaonal Conference Center. 2009. Japan.

Scherer, R., Afferentes Sprach Therapie System. 2001, University of Technology: Graz.

Serruya, M. D., & Kahana, M. J. (2008). Techniques and devices to restore cognition. Behavioural brain research, 192(2), 149-65. doi:10.1016/j.bbr.2008.04.007

Sharp, D.J., et al., Increased frontoparietal integration after stroke and cognitive recovery. Ann Neurol, 2010. 68(5): p. 753-6.

Sheorajpanday, R. V. a, Nagels, G., Weeren, A. J. T. M., van Putten, M. J. a M., & De Deyn, P. P. (2011). Quantitative EEG in ischemic stroke: correlation with functional status after 6 months. Clinical neurophysiology, 122(5), 874-83. International Federation of Clinical Neurophysiology. doi:10.1016/j.clinph.2010.07.028

Siepmann, M., et al., A pilot study on the effects of heart rate variability biofeedback in patients with depression and in healthy subjects. Appl Psychophysiol Biofeedback, 2008. 33(4): p. 195-201.

Slot, K.B. and E. Berge, Thrombolytic treatment for stroke: patient preferences for treatment, information, and involvement. J Stroke Cerebrovasc Dis, 2009. 18(1): p. 17-22.

Stępień, M., Conradi, J., Waterstraat, G., Hohlefeld, F. U., Curio, G., & Nikulin, V. V. (2011). Event-related desynchronization of sensorimotor EEG rhythms in hemiparetic patients with acute stroke. Neuroscience letters, 488(1), 17-21. doi:10.1016/j.neulet.2010.10.072

Stipacek, A., et al., Sensitivity of human EEG alpha band desynchronization to different working memory components and increasing levels of memory load. Neurosci Lett, 2003. 353(3): p. 193-6.

Sturm, W., Aufmerksamkeitsstörungen. 2005, Göttingen: Hogrefe.

Sturm, W., Evidenzbasierte Verfahren in der neuropsychologischen Rehabilitation: Therapie von Aufmerksamkeitsstörungen. Neuro Rehabil, 2010. 16(2): p. 55-62.

Sturm, W., et al., Functional reorganisation in patients with right hemisphere stroke after training of alertness: a longitudinal PET and fMRI study in eight cases. Neuropsychologia, 2004. 42(4): p. 434-50.

Sutterlin, S., et al., Frames, decisions, and cardiac-autonomic control. Soc Neurosci, 2010: p. 1-9.

Swanson, K.S., et al., The effect of biofeedback on function in patients with heart failure. Appl Psychophysiol Biofeedback, 2009. 34(2): p. 71-91.

Tanida, M., et al., Relation between asymmetry of prefrontal cortex activities and the autonomic nervous system during a mental arithmetic task: near infrared spectroscopy study. Neurosci Lett, 2004. 369(1): p. 69-74.

Thayer, J.F. and R.D. Lane, Claude Bernard and the heart-brain connection: further elaboration of a model of neurovisceral integration. Neurosci Biobehav Rev, 2009. 33(2): p. 81-8.

Thayer, J.F., et al., Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health. Ann Behav Med, 2009. 37(2): p. 141-53.

The WHOQOL Group, The World Health Organization quality of life assessment (WHOQOL): Position paper from the World Health Organization. Social Science and Medicine, 1995. 41: p. 1403–1409.

Thompson, L., M. Thompson, and A. Reid, Functional neuroanatomy and the rationale for using EEG biofeedback for clients with Asperger's syndrome. Appl Psychophysiol Biofeedback, 2010. 35(1): p. 39-61.

Thompson, L., M. Thompson, and A. Reid, Neurofeedback outcomes in clients with Asperger's syndrome. Appl Psychophysiol Biofeedback, 2010. 35(1): p. 63-81.

Thornton, K. (2000). Improvement/rehabilitation of memory functioning with neurotherapy/QEEG biofeedback. The Journal of head trauma rehabilitation, 15(6), 1285-96.

Thornton, K. E., & Carmody, D. P. (2008). Efficacy of traumatic brain injury rehabilitation: interventions of QEEG-guided biofeedback, computers, strategies, and medications. Applied psychophysiology and biofeedback, 33(2), 101-24. doi:10.1007/s10484-008-9056-z

Tyson, S.F., et al., The influence of objective measurement tools on communication and clinical decision making in neurological rehabilitation. J Eval Clin Pract, 2010.

Truelsen, T., et al., Stroke incidence and prevalence in Europe: a review of available data. Eur J Neurol, 2006. 13(6): p. 581-98.

Tsirlin, I., et al., Uses of virtual reality for diagnosis, rehabilitation and study of unilateral spatial neglect: review and analysis. Cyberpsychol Behav, 2009. 12(2): p. 175-81.

van Til, J.A., et al., The potential for shared decision-making and decision aids in rehabilitation medicine. J Rehabil Med, 2010. 42(6): p. 598-604.

Vernon, D. J. (2005). Can neurofeedback training enhance performance? An evaluation of the evidence with implications for future research. Applied psychophysiology and biofeedback, 30(4), 347-64. doi:10.1007/s10484-005-8421-4

Vernon, D., Egner, T., Cooper, N., Compton, T., Neilands, C., Sheri, A., & Gruzelier, J. (2003). The effect of training distinct neurofeedback protocols on aspects of cognitive performance. International journal of psychophysiology, 47(1), 75-85.

Vogele, C., et al., Cardiac autonomic regulation and anger coping in adolescents. Biol Psychol, 2010. 85(3): p. 465-71.

Vogele, C., Klinische Psychologie: Körperliche Erkrankungen. 2009, Weinheim: Verlagsgruppe Beltz - Psychologie Verlags Union.

Vogele, C., et al., Effects of cardiac autonomic balance on performance in P300 brain-computer-interface (BCI). Clinical Neurophysiology, submitted.

Vogele, C., Psychische Störungen bei HIV und AIDS: Klinisch-psychologische Diagnostik und Intervention. Psychotherapeut, 2010. 55: p. 194-202.

Vogele, C. and A. von Leupoldt, Mental disorders in chronic obstructive pulmonary disease (COPD). Respir Med, 2008. 102(5): p. 764-73.

Vogele, C., et al., Cognitive mediation of clinical improvement after intensive exposure therapy of agoraphobia and social phobia. Depress Anxiety, 2010. 27(3): p. 294-301.

Walter, W.G., et al., Contingent Negative Variation: An Electric Sign of Sensorimotor Association and Expectancy in the Human Brain. Nature, 1964. 203: p. 380-4.

Wang, Y. and J.M. Winters. An event-driven dynamic recurrent neuro-fuzzy system for adaptive prognosis in rehabilitation. in 25th Annual International Conference of the IEEE. 2003.

Wangler, S., et al., Neurofeedback in children with ADHD: Specific event-related potential findings of a randomized controlled trial. Clin Neurophysiol, 2010.

Warren, J.E., et al., Anterior temporal lobe connectivity correlates with functional outcome after aphasic stroke. Brain, 2009. 132(Pt 12): p. 3428-42.

Weiss, S., & Rappelsberger, P. (2000). Long-range EEG synchronization during word encoding correlates with successful memory performance. Brain research. Cognitive brain research, 9(3), 299-312.

Werkle-Bergner, M., Müller, V., Li, S.-C., & Lindenberger, U. (2006). Cortical EEG correlates of successful memory encoding: implications for lifespan comparisons. Neuroscience and biobehavioral reviews, 30(6), 839-54. doi:10.1016/j.neubiorev.2006.06.009

Wheat, A.L. and K.T. Larkin, Biofeedback of heart rate variability and related physiology: a critical review. Appl Psychophysiol Biofeedback, 2010. 35(3): p. 229-42.

Wiesner, G., J. Grimm, and E. Bittner, Schlaganfall: Prävalenz, Inzidenz, Trend, Ost-West-Vergleich. Erste Ergebnisse aus dem Bundes-Gesundheitssurvey 1998. Gesundheitswesen, 1999. 61: p. 79-84.

Winhuisen, L., et al., The right inferior frontal gyrus and poststroke aphasia: a follow-up investigation. Stroke, 2007. 38(4): p. 1286-92.

Wriessnegger, S.C., J. Kurzmann, and C. Neuper, Spatio-temporal differences in brain oxygenation between movement execution and imagery: a multichannel near-infrared spectroscopy study. Int J Psychophysiol, 2008. 67(1): p. 54-63.

Zickler, C., et al. Brain Computer Interaction Applications for People with Disabilities: Defining User Needs and User Requirements. in Assistive Technology from Adapted Equipment to Inclusive Environments. 2009. Amsterdam: IOS Press.

Zoefel, B., Huster, R. J., & Herrmann, C. S. (2011). Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. NeuroImage, 54(2), 1427-31. Elsevier Inc. doi:10.1016/j.neuroimage.2010.08.078

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