Advertisement

Computational Sensemaking on Examples of Knowledge Discovery from Neuroscience Data: Towards Enhancing Stroke Rehabilitation

  • Andreas Holzinger
  • Reinhold Scherer
  • Martin Seeber
  • Johanna Wagner
  • Gernot Müller-Putz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7451)

Abstract

Strokes are often associated with persistent impairment of a lower limb. Functional brain mapping is a set of techniques from neuroscience for mapping biological quantities (computational maps) into spatial representations of the human brain as functional cortical tomography, generating massive data. Our goal is to understand cortical reorganization after a stroke and to develop models for optimizing rehabilitation with non-invasive electroencephalography. The challenge is to obtain insight into brain functioning, in order to develop predictive computational models to increase patient outcome. There are many EEG features that still need to be explored with respect to cortical reorganization. In the present work we use independent component analysis, and data visualization mapping as tools for sensemaking. Our results show activity patterns over the sensorimotor cortex, involved in the execution and association of movements; our results further supports the usefulness of inverse mapping methods and generative models for functional brain mapping in the context of non-invasive monitoring of brain activity.

Keywords

Knowledge discovery data mining human-computer interaction gait analysis biomedical informatics infomax independent component analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Harwin, W., Murgia, A., Stokes, E.: Assessing the effectiveness of robot facilitated neurorehabilitation for relearning motor skills following a stroke. Medical and Biological Engineering and Computing 49(10), 1093–1102 (2011)CrossRefGoogle Scholar
  2. Lo, A.C., Guarino, P.D., Richards, L.G., Haselkorn, J.K., Wittenberg, G.F., Federman, D.G., Ringer, R.J., Wagner, T.H., Krebs, H.I., Volpe, B.T., Bever, C.T., Bravata, D.M., Duncan, P.W., Corn, B.H., Maffucci, A.D., Nadeau, S.E., Conroy, S.S., Powell, J.M., Huang, G.D., Peduzzi, P.: Robot-Assisted Therapy for Long-Term Upper-Limb Impairment after Stroke. New England Journal of Medicine 362(19), 1772–1783 (2010)CrossRefGoogle Scholar
  3. Scherer, R., Pradhan, S., Dellon, B., Kim, D., Klatzky, R., Matsuoka, Y.: Characterization of multi-finger twist motion toward robotic rehabilitation. In: ICORR 2009, Kyoto (Japan), pp. 812–817. IEEE (2009)Google Scholar
  4. Simonic, K.M., Holzinger, A., Bloice, M., Hermann, J.: Optimizing Long-Term Treatment of Rheumatoid Arthritis with Systematic Documentation. In: Proceedings of Pervasive Health - 5th International Conference on Pervasive Computing Technologies for Healthcare, Dublin, pp. 550–554. IEEE (2011)Google Scholar
  5. Holzinger, A.: Weakly Structured Data in Health-Informatics: The Challenge for Human-Computer Interaction. In: Baghaei, N., Baxter, G., Dow, L., Kimani, S. (eds.) Proceedings of INTERACT 2011 Workshop: Promoting and Supporting Healthy Living by Design. IFIP, Lisbon (Portugal), pp. 5–7 (2011)Google Scholar
  6. Wong, B.L.W., Xu, K., Holzinger, A.: Interactive Visualization for Information Analysis in Medical Diagnosis. In: Holzinger, A., Simonic, K.-M. (eds.) USAB 2011. LNCS, vol. 7058, pp. 109–120. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. Comon, P.: Independent Component Analysis, a new concept? Signal Processing 36(3), 287–314 (1994)zbMATHCrossRefGoogle Scholar
  8. Boehm, C., Faloutsos, C., Plant, C.: Outlier-robust clustering using independent components. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, pp. 185–198. ACM (2008)Google Scholar
  9. Riener, R., Lünenburger, L., Maier, I.C., Colombo, G., Dietz, V.: Locomotor Training in Subjects with Sensori-Motor Deficits: An Overview of the Robotic Gait Orthosis Lokomat. Journal of Healthcare Engineering 1(2), 197–216 (2010)CrossRefGoogle Scholar
  10. Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 134, 9–21 (2004)CrossRefGoogle Scholar
  11. Oostenveld, R., Oostendorp, T.F.: Validating the boundary element method for forward and inverse EEG computations in the presence of a hole in the skull. Human Brain Mapping 17, 179–192 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andreas Holzinger
    • 1
  • Reinhold Scherer
    • 2
  • Martin Seeber
    • 2
  • Johanna Wagner
    • 2
  • Gernot Müller-Putz
    • 2
  1. 1.Institute for Medical Informatics, Statistics & Documentation, Research Unit HCI4MEDMedical University GrazGrazAustria
  2. 2.Institute for Knowledge Discovery, Laboratory of Brain-Computer InterfacesGraz University of TechnologyGrazAustria

Personalised recommendations