Skip to main content

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

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7451)


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.


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

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-32395-9_13
  • Chapter length: 3 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   39.99
Price excludes VAT (USA)
  • ISBN: 978-3-642-32395-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   54.99
Price excludes VAT (USA)


  • 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)

    CrossRef  Google Scholar 

  • 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)

    CrossRef  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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)

    CrossRef  Google Scholar 

  • Comon, P.: Independent Component Analysis, a new concept? Signal Processing 36(3), 287–314 (1994)

    MATH  CrossRef  Google Scholar 

  • 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 

  • 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)

    CrossRef  Google Scholar 

  • 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)

    CrossRef  Google Scholar 

  • 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)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Holzinger, A., Scherer, R., Seeber, M., Wagner, J., Müller-Putz, G. (2012). Computational Sensemaking on Examples of Knowledge Discovery from Neuroscience Data: Towards Enhancing Stroke Rehabilitation. In: Böhm, C., Khuri, S., Lhotská, L., Renda, M.E. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2012. Lecture Notes in Computer Science, vol 7451. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32394-2

  • Online ISBN: 978-3-642-32395-9

  • eBook Packages: Computer ScienceComputer Science (R0)