Advertisement

Towards a General-Purpose Mobile Brain-Body Imaging NeuroIS Testbed

  • Reinhold SchererEmail author
  • Stefan Feitl
  • Matthias Schlesinger
  • Selina C. Wriessnegger
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 16)

Abstract

Navigating (familiar) environments requires spatial memory and spatial orientation. Mobile information systems (IS) have largely taken on this task and have changed human behavior. What impact has the redistribution of problem solving on human skills and knowledge? We are interested in exploring how the use of IS impacts on knowledge/ignorance by means of mobile brain-body imaging. In this paper, we introduce a novel experimental testbed developed to study spatial orientation in the context of geographic maps. Key system features include data synchronization between various devices and data sources, flexibility in designing and modeling research questions and integration of online co-adaptive brain-computer interfacing (BCI) technology. Flexibility, adaptability, scalability and modifiability of the implemented system turn the testbed into a general-purpose tool for studying NeuroIS constructs.

Keywords

Mobile brain and body imaging Spatial orientation General-purpose experiment environment Brain-computer interface 

Notes

Acknowledgements

The authors are thankful to Christian Kothe and David Medine (Schwartz Center for Computational Neuroscience, UC San Diego) for their support with LSL software adaptations. This work was partly funded by the Land Steiermark project MODERNE (NICHT-)WISSENSGESELLSCHAFT (grant no ABT08-21624/2014).

References

  1. 1.
    Ishikawa, T., Takahashi, K.: Relationships between methods for presenting information on navigation tools and users’ wayfinding behavior. Cartogr. Perspect. 75, 17–28 (2013)Google Scholar
  2. 2.
    Woollett, K., Maguire, E.A.: Acquiring ‘the knowledge’ of London’s layout drives structural brain changes. Curr. Biol. 21, 2109–2114 (2011)CrossRefGoogle Scholar
  3. 3.
    Maguire, E.A., Gadian, D.G., Johnsrude, I.S., Good, C.D., Ashburner, J., Frackowiak, R.S., Frith, C.D.: Navigation-related structural change in the hippocampi of taxi drivers. Proc. Natl. Acad. Sci. U. S. A. 97, 4398–4403 (2000)CrossRefGoogle Scholar
  4. 4.
    Henneman, W.J.P., Vrenken, H., Barnes, J., Sluimer, I.C., Verwey, N.A., Blankenstein, M.A., Klein, M., Fox, N.C., Scheltens, P., Barkhof, F., Van Der Flier, W.M.: Baseline CSF p-tau levels independently predict progression of hippocampal atrophy in Alzheimer disease. Neurology 73, 935–940 (2009)CrossRefGoogle Scholar
  5. 5.
    West, G.L., Drisdelle, B.L., Konishi, K., Jackson, J., Jolicoeur, P., Bohbot, V.D.: Habitual action video game playing is associated with caudate nucleus-dependent navigational strategies. Proc. R. Soc. London B Biol. Sci. 282, 20142952 (2015)CrossRefGoogle Scholar
  6. 6.
    Makeig, S., Gramann, K., Jung, T.-P., Sejnowski, T.J., Poizner, H.: Linking brain, mind and behavior. Int. J. Psychophysiol. 73, 95–100 (2009)CrossRefGoogle Scholar
  7. 7.
    Ehinger, B.V., Fischer, P., Gert, A.L., Kaufhold, L., Weber, F., Pipa, G., König, P.: Kinesthetic and vestibular information modulate alpha activity during spatial navigation: A mobile EEG study. Front. Hum. Neurosci. 8, 71 (2014)CrossRefGoogle Scholar
  8. 8.
    Lin, C.-T., Chiu, T.-C., Gramann, K.: EEG correlates of spatial orientation in the human retrosplenial complex. Neuroimage 120, 123–132 (2015)CrossRefGoogle Scholar
  9. 9.
    Sulpizio, V., Boccia, M., Guariglia, C., Galati, G.: Functional connectivity between posterior hippocampus and retrosplenial complex predicts individual differences in navigational ability. Hippocampus (2016). doi: 10.1002/hipo.22592 Google Scholar
  10. 10.
    Baumann, O., Mattingley, J.B.: Medial parietal cortex encodes perceived heading direction in humans. J. Neurosci. 30, 12897–12901 (2010)CrossRefGoogle Scholar
  11. 11.
    Chadwick, M.J., Jolly, A.E.J., Amos, D.P., Hassabis, D., Spiers, H.J.: A goal direction signal in the human entorhinal/subicular region. Curr. Biol. 25, 87–92 (2015)CrossRefGoogle Scholar
  12. 12.
    Brehm, J.W., Self, E.A.: The intensity of motivation. Annu. Rev. Psychol. 40, 109–131 (1989)CrossRefGoogle Scholar
  13. 13.
    Neubauer, A.C., Fink, A.: Intelligence and neural efficiency: Measures of brain activation versus measures of functional connectivity in the brain. Intelligence. 37, 223–229 (2009)CrossRefGoogle Scholar
  14. 14.
    Wolbers, T., Hegarty, M.: What determines our navigational abilities? Trends Cogn. Sci. 14, 138–146 (2010)CrossRefGoogle Scholar
  15. 15.
    Wagner, J., Solis-Escalante, T., Scherer, R., Neuper, C., Müller-Putz, G.R.: It’s how you get there: Walking down a virtual alley activates premotor and parietal areas. Front. Hum. Neurosci. 8, 93 (2014)Google Scholar
  16. 16.
    Klatzky, R.L.: Allocentric and egocentric spatial representations: Definitions, distinctions, and interconnections. 1–18 (1998)Google Scholar
  17. 17.
    Delorme, A., Mullen, T., Kothe, C., Akalin Acar, Z., Bigdely-Shamlo, N., Vankov, A., Makeig, S.: EEGLAB, SIFT, NFT, BCILAB, and ERICA: New tools for advanced EEG processing. Comput. Intell. Neurosci. 2011, 130714 (2011)CrossRefGoogle Scholar
  18. 18.
    Schlesinger, M.: An LSL-based sensor platform for mobile brain imaging, brain-computer interfaces and rehabilitation (Master’s thesis) (2016)Google Scholar
  19. 19.
    Scherer, R., Billinger, M., Wagner, J., Schwarz, A., Hettich, D.T., Bolinger, E., Lloria Garcia, M., Navarro, J., Müller-Putz, G.R.: Thought-based row-column scanning communication board for individuals with cerebral palsy. Ann. Phys. Rehabil. Med. (2015). doi: 10.1016/j.rehab.2014.11.005 Google Scholar
  20. 20.
    Daly, I., Scherer, R., Billinger, M., Müller-Putz, G.R.: FORCe: Fully online and automated artifact removal for brain-computer interfacing. IEEE Trans. Neural Syst. Rehabil. Eng. (2014). doi: 10.1109/TNSRE.2014.2346621 Google Scholar
  21. 21.
    Wagner, J., Solis-Escalante, T., Grieshofer, P., Neuper, C., Müller-Putz, G.R., Scherer, R.: Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects. Neuroimage 63, 1203–1211 (2012)CrossRefGoogle Scholar
  22. 22.
    Scherer, R., Moitzi, G., Daly, I., Müller-Putz, G.R.: On the use of games for noninvasive EEG-based functional brain mapping. IEEE Trans. Comput. Intell. AI Games 5, 155–163 (2013)CrossRefGoogle Scholar
  23. 23.
    Seeber, M., Scherer, R., Wagner, J., Solis-Escalante, T., Müller-Putz, G.R.: EEG beta suppression and low gamma modulation are different elements of human upright walking. Front. Hum. Neurosci. 8, 485 (2014)CrossRefGoogle Scholar
  24. 24.
    Pfurtscheller, G., Müller-Putz, G.R., Schlögl, A., Graimann, B., Scherer, R., Leeb, R., Brunner, C., Keinrath, C., Lee, F.Y., Townsend, G., Vidaurre, C., Neuper, C.: 15 years of BCI research at Graz University of Technology: Current projects. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 205–210 (2006)CrossRefGoogle Scholar
  25. 25.
    Scherer, R., Müller-Putz, G.R., Pfurtscheller, G.: Flexibility and practicality: The Graz brain-computer interface approach. Int. Rev. Neurobiol. 86, 119–131 (2009)CrossRefGoogle Scholar
  26. 26.
    Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., Scherer, R.: Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 313–319 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Reinhold Scherer
    • 1
    Email author
  • Stefan Feitl
    • 1
  • Matthias Schlesinger
    • 1
  • Selina C. Wriessnegger
    • 1
  1. 1.Institute of Neural Engineering, Graz University of TechnologyGrazAustria

Personalised recommendations