Abstract
There has been much interest recently in the concept of using information from the motor cortex region of the brain, recorded using non-invasive scalp electrodes, to construct a crude interface with a computer. It is known that movements of the limbs, for example, are accompanied by desynchronisations and synchronisations within the scalp-recorded electroencephalogram (EEG). These event-related desynchronisations and synchronisations (ERD and ERS), however, appear to be present when volition to move a limb occurs, even when actual movement of the limb does not in fact take place. The determination and classification of the ERD/S offers many exciting possibilities for the control of peripheral devices via computer analysis. To date most effort has concentrated on the analysis of the changes in absolute frequency content of signals recorded from the motor cortex. The authors present results which tackle the issues of both the interpretation of changes in signals with time and across channels with simple methods which monitor the temporal and spatial ‘complexity’ of the data. Results are shown on synthetic and real data sets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Broomhead, D. andKing, G. (1986): ‘Extracting qualitative dynamics from experimental data,’Physica D,20, pp. 217–236
Grassberger, P. andProcaccia, I. (1983): ‘Measuring the strangeness of strange attractors,’Physica D,9D, pp. 189–208
Kember, G. andFowler, A. C. (1993): ‘A correlation function for choosing time delays in phase portrait reconstructions,’Phys. Lett. A,179, (2), pp. 72–80
Kirkup, L., Searle, A., Craig, A., McIsaac, P. andMoses, P. (1997): ‘EEG-based system for rapid on-off switching without prior learning,’Med. Biol. Eng. Comput.,35, (5), pp. 504–509
Pfurtscheller, G., Flotzinger, D. andKalcher, J. (1993): ‘Brain-computer interface—a new communication device for handicapped people,’J. Microcomput. Appl.,16, pp. 293–299
Pfurtscheller, G., Flotzinger, D. andNeuper, C. (1994): ‘Differentiation between finger, toe and tongue movement in man based on 40Hz EEG,’Electroenceph. Clin. Neurol.,90, pp. 456–460
Pincus, S. M. (1991): ‘Approximate entropy as a measure of system complexity,’Proc. Nat. Acad. Sci., USA,88, pp. 2297–2301
Porta, A., Baselli, G., Liberati, D., Montano, N., Cogliati, C., Gnecchi-Ruscone, T., Malliani, A. andCeruti, S. (1998): ‘Measuring regularity by means of a corrected conditional entropy in sympathetic outflow,’Biol. Cybern.,78, pp. 71–78
Rezek, I. A. andRoberts, S. J. (1998): ‘Stochastic complexity measures for physiological signal analysis,’IEEE Trans. Biomed. Eng.,44, (9)
Takens, F. (1981): ‘Detecting strange attractors in turbulence’, inRand, D. A. andYoung, L. S. (Eds.): ‘Dynamical systems and turbulence’, lecture notes in mathematics, Vol. 898 (Springer) pp. 366–381
Wolf, A., Swift, J.B., Swiney, H. L. andVastano, J. A. (1985): ‘Determining Lyapunov exponents from a time series,’Physica D,16D, (3), pp. 285–317
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Roberts, S.J., Penny, W. & Rezek, I. Temporal and spatial complexity measures for electroencephalogram based brain-computer interfacing. Med. Biol. Eng. Comput. 37, 93–98 (1999). https://doi.org/10.1007/BF02513272
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02513272