Publikationen
Bewegungsvorstellung Virtual Reality Neurorehabilitation mit BCI Funktionelle Elektrostimulation Robotische Rehabilitation
Irimia, D.C., Ortner, R., Poboroniuc, M.S., Ignat, B.E. and Guger, C., 2018. High classification accuracy of a motor imagery based brain-computer interface for stroke rehabilitation training. Frontiers in Robotics and AI, 5, p.130.
Guger, C., Millán, J.D.R., Mattia, D., Ushiba, J., Soekadar, S.R., Prabhakaran, V., Mrachacz-Kersting, N., Kamada, K. and Allison, B.Z., 2018. Brain-computer interfaces for stroke rehabilitation: summary of the 2016 BCI Meeting in Asilomar. Brain-Computer Interfaces, 5(2-3), pp.41-57.
Irimia, D. C., Cho, W., Ortner, R., Allison, B. Z., Ignat, B. E., Edlinger, G., & Guger, C. (2017). Brain‐computer interfaces with multi‐sensory feedback for stroke rehabilitation: a case study. Artificial organs, 41(11), E178-E184.
Irimia D.C., Poboroniuc M.S., Ortner R., Allison B.Z., Guger C. (2017). Preliminary Results of Testing a BCI-Controlled FES System for Post-Stroke Rehabilitation. Proceedings of the 7th Graz Brain-Computer Interface Conference 2017, 204-209.
Cho W, Heilinger A, Xu R, Zehetner M, Schobesberger S, et al. (2017) Hemiparetic Stroke Rehabilitation Using Avatar and Electrical Stimulation Based on Non-invasive Brain Computer Interface. International Journal of Physical Medicine and Rehabilitation 5:411.
Huggins, J. E., Guger, C., Ziat, M., Zander, T. O., Taylor, D., Tangermann, M., ... & Ruffini, G. (2017). Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future. Brain-Computer Interfaces, 1-34.
Xu R., Allison B. Z., Ortner R., Irimia D. C., Espinosa A., Lechner A., & Guger C. (2017). How Many EEG Channels Are Optimal for a Motor Imagery Based BCI for Stroke Rehabilitation?. In Converging Clinical and Engineering Research on Neurorehabilitation II (pp. 1109-1113). Springer International Publishing.
Cho W., Sabathiel N., Ortner R., Lechner A., Irimia D.C., Allison B.Z., Edlinger G. and Guger C., 2016. Paired Associative Stimulation using Brain-Computer Interfaces for Stroke Rehabilitation: A Pilot study. European Journal of Translational Myology, 26(3).
C. Guger, C. Kapeller, R. Ortner, K. Kamada, Motor Imagery with Brain-Computer Interface Neurotechnology (pp. 61-79), in: Motor Imagery: Emerging Practices, Role in Physical Therapy and Clinical Implications, edited by B.M Garcia, 2015.
R. Ortner, J. Scharinger, A. Lechner, C. Guger (2015). How many people can control a motor imagery based BCI using common spatial patterns?, in: 7th International IEEE/EMBS Conference on Neural Engineering (NER) 2015, pp. 202-205.
Rupert Ortner, Alexander Lechner, Christoph Guger (2015): Stroke Rehabilitation assisted by a Brain-Computer Interface (BCI) and multimodal feedback: First results. In proccedings of the European Stroke Conference, 15.05.2015, Vienna, AT. Poster.
D. C. Irimia, M. S. Poboroniuc and R. Ortner, “Improved Method to Perform FES & BCI Based Rehabilitation,” in The 4th IEEE International Conference on E-Health and Bioengineering, 2013.
C. Guger, H. Ramoser and G. Pfurtscheller, “Real-Time EEG Analysis with Subject-Specific Spatial Patterns for a Brain–Computer Interface (BCI),” IEEE Trans. Rehab. Eng, vol. 8, pp. 447-456, 2000.
K. Shindo, K. Kawashima and e. a. Ushiba, “Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study,” J Rehabil Med, pp. 951-957, 43(10) 2011.
J.C. Moreno, J. L. Pons, E. Gruenbacher, C. Guger (2010). BCI-driven stroke rehabilitation; the concept of the BETTER project..
C. Guger, W. Harkam, C. Hertnaes, G. Pfurtscheller (1999). Prosthetic control by an EEG-based brain-computer interface (BCI). 5th European Conference for the Advancement of Assitive Technolgoy Düsseldorf, Germany, AAATE.
G. Pfurtscheller, C. Guger (1999). "Brain-computer communication system: EEG-based control of hand orthosis in a tetraplegic patient." Acta Chir. Austriaca 31(159): pp. 23 - 25. Brain-computer communication system.
R. Ortner, D. Ram, A. Kollreider, H. Pitsch, J. Wojtowicz, and G. Edlinger, “Human-computer confluence for rehabilitation purposes after stroke,” in Virtual, Augmented and Mixed Reality. Systems and Applications, Springer, 2013, pp. 74–82.
R. Ortner, D.-C. Irimia, C. Guger, and G. Edlinger, “Human Computer Confluence in BCI for Stroke Rehabilitation,” in Foundations of Augmented Cognition, Springer, 2015, pp. 304–312.
A. Ramos-Murguialday, D. Broetz, M. Rea, L. Läer, O. Yilmaz, F. L. Brasil, G. Liberati, M. R. Curado, E. Garcia-Cossio, A. Vyziotis, W. Cho, M. Agostini, E. Soares, S. Soekadar, A. Caria, L. G. Cohen, and N. Birbaumer, “Brain-machine-interface in chronic stroke rehabilitation: A controlled study.,” Ann Neurol. 2013, p. doi: 10.1002/ana.23879, 2013.
Cho, W., Vidaurre, C., Hoffmann, U., Birbaumer, N., & Ramos-Murguialday, A. (2011, August). Afferent and efferent activity control in the design of brain computer interfaces for motor rehabilitation. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE (pp. 7310-7315). IEEE.
Messergebnisse
Patient/in Nr. 1
| 9-hole PEG test | ||
| Session | Left hand(s) | Right hand(s) |
| 0 | 31 | 65 |
| 3 | 32 | 54 |
| 6 | 32 | 45 |
| 9 | 31 | 42 |
| 12 | 31 | 42 |
| 15 | 29 | 38 |
| 18 | 29 | 34 |
| 21 | 29 | 30 |
Tabelle 1: Um den Fortschritt des Rehabilitationsprozesses der Patientin beurteilen zu können, wurden mehrere 9-hole PEG Tests durchgeführt. Dabei wird die Zeit gemessen. Wir sammelten Daten vor der ersten Trainingseinheit mit recoveriX und wiederholten den Test dann immer nach drei Einheiten. Die folgende Tabelle zeigt die Ergebnisse der Tests für beide Hände. Die benötigte Zeit, um den Test mit der betroffenen Hand zu beenden, reduzierte sich von 65 Sekunden vor der ersten Einheit auf 30 Sekunden nach der letzten Einheit. Die benötigte Zeit mit der gesunden Hand blieb während des gesamten Trainings mehr oder weniger konstant.
Patient/in Nr. 2
Abbildung 5: Die Patientin war nach 10 Einheiten mit recoveriX in der Lage eine Dorsalextension des betroffenen Handgelenks ohne funktioneller, elektrischer Muskelstimulation durchzuführen.
Fachbeirat
Die gemeinsame Mission ist es, recoveriX mit kognitiven Prozessen und motorischen Bewegungen zu kombinieren und Schlaganfall Rehabilitation effektiver zu gestalten.
Dr. Brendan Allison
Universität von Kalifornien, San Diego
Allison Consulting
Prof. Dr. Marian Poboroniuc
Technische Universität Iasi, Rumänien
Dr. Milena Korostenskaja
Florida Krankenhaus für Kinder, USA
Dr. Vivek Prabhakaran
Universität von Wisconsin, USA
Dr. Tetsuo Ota
Medizinische Universität Asahikawa, Japan
Dr. Rosella Spataro
Universität von Palermo, Italien
Dr. Kyosuke Kamada
Medizinische Universtität Asahikawa, Japan
Dr. Adam Hebb
Swedish Medical Center, USA
