A MULTIMODAL BRAIN-ARM INTERFACE FOR OPERATION OF COMPLEX ROBOTIC SYSTEMS AND UPPER LIMB MOTOR RECOVERY

Michele Folgheraiter, Elsa Andrea Kirchner, Anett Seeland, Su Kyoung Kim, Mathias Jordan, Hendrik Wöhrle, Bertold Bongardt, Steffen Schmidt, Jan Christian Albiez, Frank Kirchner

Abstract

This work introduces the architecture of a novel brain-arm haptic interface usable to improve the operation of complex robotic systems, or to deliver a fine rehabilitation therapy to the human upper limb. The proposed control scheme combines different approaches from the areas of robotics, neuroscience and human-machine interaction in order to overcome the limitations of each single field. Via the adaptive Brain Reading Interface (aBRI) user movements are anticipated by classification of surface electroencephalographic data in a millisecond range. This information is afterwards integrated into the control strategy of a wearable exoskeleton in order to finely modulate its impedance and therefore to comply with the motion preparation of the user. Results showing the efficacy of the proposed control approach are presented for the single joint case.

References

  1. Balconi, E. (2009). The multicomponential nature of movement-related cortical potentials: functional generators and psychological factors. Neuropsychological Trends, 5:59-84.
  2. Blankertz, B., Curio, G., and Müller, K. (2002). Classifying single trial EEG: Towards brain computer interfacing. In Advances in neural information processing systems 14: proceedings of the 2001 conference, pages 157- 164. MIT Press.
  3. Blankertz, B., Dornhege, G., Lemm, S., Krauledat, M., Curio, G., and Müller, K. (2006). The Berlin BrainComputer Interface: Machine learning based detection of user specific brain states. Journal of Universal Computer Science, 12(6):581-607.
  4. Blankertz, B., Dornhege, G., Schäfer, C., Krepki, R., Kohlmorgen, J., Müller, K., Kunzmann, V., Losch, F., and Curio, G. (2003). Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2):127.
  5. Farwell, L. and Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing eventrelated brain potentials. Electroencephalography and clinical Neurophysiology, 70(6):510-23.
  6. Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27(8):861-874.
  7. Folgheraiter, M., Bongardt, B., Albiez, J., and Kirchner, F. (2009a). Design of a bio-inspired wearable exoskeleton for applications in robotics. In International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC-2009), Portugal, Porto.
  8. Folgheraiter, M., Bongardt, B., de Gea Fernandéz, S. S. J., Albiez, J., and Kirchner, F. (2009b). Design of an arm exoskeleton using an hybrid motion-capture and model-based technique. In IEEE International Conference on Robotics and Automation(ICRA-2009), May 12-17, Kobe, Japan.
  9. Folgheraiter, M., de Gea, J., Bongardt, B., Albiez, J., and Kirchner, F. (2009c). Bio-inspired control of an arm exoskeleton joint with active-compliant actuation system. Applied Bionics and Biomechanics, 6(2):193- 204.
  10. Harwin, W., Patton, J., and Edgerton, V. (2006). Challenges and opportunities for robot-mediated neurorehabilitation. Proceedings of the IEEE, 94(9):1717-1726.
  11. Hogan, N. (1985). Impedance control - an approach to manipulation. i - theory. ii - implementation. iii - applications. ASME Transactions Journal of Dynamic Systems and Measurement Control B, 107:1-24.
  12. Jones, E., Oliphant, T., Peterson, P., et al. (2001). SciPy: Open source scientific tools for Python.
  13. Kirchner, E. A., Metzen, J. H., Duchrow, T., Kim, S., and Kirchner, F. (2009). Assisting telemanipulation operators via real-time Brain Reading. In Lemgoer Schriftenreihe zur industriellen Informationstechnik, Paderborn.
  14. Kirchner, E. A., Wöhrle, H., Bergatt, C., S. K. Kim, J. H. M., and Kirchner, F. (2010). Towards operator monitoring via brain reading - an eeg-based approach for space applications. In Procedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space.
  15. Kornhuber, H. and Deecke, L. (1965). Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Plügers Archiv für die gesamte Physiologie des Menschen und der Tiere, 284:1-17.
  16. Krauledat, M., Dornhege, G., Blankertz, B., Curio, G., and Müller, K. (2004). The Berlin brain-computer interface for rapid response. Biomedizinische Technik, 49(1):61-62.
  17. Krebs, H. I., Ferraro, M., Buerger, S. P., Newbery, M. J., Makiyama, A., Sandmann, M., Lynch, D., Volpe, B. T., and Hogan, N. (2004). Rehabilitation robotics: pilot trial of a spatial extension for mit-manus. Journal of Neuroengineering and Rehabiltation, 1(1):5.
  18. Leeb, R., Keinrath, C., Friedman, D., Guger, C., Scherer, R., Neuper, C., Garau, M., Antley, A., Steed, A., and Slater, M. (2006). Walking by thinking: the brainwaves are crucial, not the muscles! Presence: Teleoperators and Virtual Environments, 15(5):500-514.
  19. Li, Y., Gao, X., Liu, H., and Gao, S. (2004). Classification of single-trial electroencephalogram during finger movement. IEEE Transactions on biomedical engineering, 51(6):1019-1025.
  20. Masaki, H., Wild-Wall, N., Sangals, J., and Sommer, W. (2004). The functional locus of the lateralized readiness potential. Psychophysiology, 41(2):220-230.
  21. Mistry, Mohajerian, and Schaal (2005). Arm movement experiments with joint space force fields unsing an exoskeleton robot. In 9th International Conference on Rehabilitation Robotics.
  22. Pires, G., Nunes, U., and Castelo-Branco, M. (2007). Single-trial EEG classification of movement related potential. In IEEE 10th International Conference on Rehabilitation Robotics, 2007. ICORR 2007, pages 569-574.
  23. Reinkensmeyer, D., Lum, P., and Winters, J. (2001). Emerging technologies for improving access to movement therapy following neurologic injury. Emerging and Accessible Telecommunications.
  24. Reinkensmeyer, D. J., Emken, J. L., and Cramer, S. C. (2004). Robotics, motor learning, and neurologic recovery. Annual review of biomedical engineering, 6:497-525.
  25. Rosen, J., Perry, J., Manning, N., Burns, S., and Hannaford, B. (2005). The human arm kinematics and dynamics during daily activities - toward a 7 dof upper limb powered exoskeleton. In Advanced Robotics, 2005. ICAR 7805. Proceedings., 12th International Conference on, pages 532-539.
  26. Sajda, P., Gerson, A., Muller, K., Blankertz, B., and Parra, L. (2003). A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces. IEEE Transactions on neural systems and rehabilitation engineering, 11(2):184-185.
  27. Schiele, A. and van der Helm, F. (2006). Kinematic design to improve ergonomics in human machine interaction. IEEE Transactions on neural systems and rehabilitation engineering, 14(4):456-469.
  28. Squires, N., Squires, K., and Hillyard, S. (1975). Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli. Electroencephalography and clinical Neurophysiology, 38(4):387-401.
  29. Wolpaw, J., Birbaumer, N., McFarland, D., Pfurtscheller, G., and Vaughan, T. (2002). Brain-computer interfaces for communication and control. Clinical neurophysiology, 113(6):767-791.
  30. Zito, T., Wilbert, N., Wiskott, L., and Berkes, P. (2008). Modular toolkit for data processing (mdp): a python data processing frame work. Front. Neuroinform., 8(2).
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Paper Citation


in Harvard Style

Folgheraiter M., Andrea Kirchner E., Seeland A., Kyoung Kim S., Jordan M., Wöhrle H., Bongardt B., Schmidt S., Christian Albiez J. and Kirchner F. (2011). A MULTIMODAL BRAIN-ARM INTERFACE FOR OPERATION OF COMPLEX ROBOTIC SYSTEMS AND UPPER LIMB MOTOR RECOVERY . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2011) ISBN 978-989-8425-37-9, pages 150-162. DOI: 10.5220/0003135501500162


in Bibtex Style

@conference{biodevices11,
author={Michele Folgheraiter and Elsa Andrea Kirchner and Anett Seeland and Su Kyoung Kim and Mathias Jordan and Hendrik Wöhrle and Bertold Bongardt and Steffen Schmidt and Jan Christian Albiez and Frank Kirchner},
title={A MULTIMODAL BRAIN-ARM INTERFACE FOR OPERATION OF COMPLEX ROBOTIC SYSTEMS AND UPPER LIMB MOTOR RECOVERY},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2011)},
year={2011},
pages={150-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003135501500162},
isbn={978-989-8425-37-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2011)
TI - A MULTIMODAL BRAIN-ARM INTERFACE FOR OPERATION OF COMPLEX ROBOTIC SYSTEMS AND UPPER LIMB MOTOR RECOVERY
SN - 978-989-8425-37-9
AU - Folgheraiter M.
AU - Andrea Kirchner E.
AU - Seeland A.
AU - Kyoung Kim S.
AU - Jordan M.
AU - Wöhrle H.
AU - Bongardt B.
AU - Schmidt S.
AU - Christian Albiez J.
AU - Kirchner F.
PY - 2011
SP - 150
EP - 162
DO - 10.5220/0003135501500162