Abstract
We developed a framework for systematic evaluation of BCI systems. This framework is intended to compare features extracted from a variety of spectral measures related to functional connectivity, effective connectivity, or instantaneous power. Different measures are treated in a consistent manner, allowing fair comparison within a repeated measures design. We applied the framework to BCI data from 14 subjects recorded on two days each, and demonstrated the framework’s feasibility by confirming results from the literature. Furthermore, we could show that electrode selection becomes more focal in the second BCI session, but classification accuracy stays unchanged.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Billinger, M., Kaiser, V., Neuper, C., Brunner, C.: Automatic frequency band selection for BCIs with ERDS difference maps. In: Proceedings of the 5th International Brain–Computer Interface Conference (2011)
Billinger, M., Neuper, C., Müller-Putz, G.R., Brunner, C.: User-centric performance estimation in a continuous online BCI. In: Proceedings of the 3rd TOBI Workshop (2012)
Brunner, C., Billinger, M., Vidaurre, C., Neuper, C.: A comparison of univariate, vector, bilinear autoregressive, and band power features for brain computer interfaces. Medical and Biological Engineering and Computing 49, 1337–1346 (2011), http://dx.doi.org/10.1007/s11517-011-0828-x , doi:10.1007/s11517-011-0828-x
Brunner, C., Scherer, R., Graimann, B., Supp, G., Pfurtscheller, G.: Online control of a brain-computer interface using phase synchronization. IEEE Transactions on Biomedical Engineering 53, 2501–2506 (2006)
Erla, S., Faes, L., Tranquillini, E., Orrico, D., Nollo, G.: Multivariate autoregressive model with instantaneous effects to improve brain connectivity estimation. International Journal of Bioelectromagnetism 11(2), 74–79 (2009)
Friston, K.J.: Functional and effective connectivity in neuroimaging: A synthesis. Hum. Brain Mapping 2, 56–78 (1994)
Holzinger, A.: On knowledge discovery and interactive intelligent visualization of biomedical data - challenges in human-computer interaction & biomedical informatics. In: Proceedings of the 9th International Joint Conference on e-Business and Telecommunications (ICETE 2012), Rome, Italy, pp. IS9–IS20 (2012)
Holzinger, A., Scherer, R., Seeber, M., Wagner, J., Müller-Putz, G.: 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.) ITBAM 2012. LNCS, vol. 7451, pp. 166–168. Springer, Heidelberg (2012)
Krusienski, D.J., McFarland, D.J., Wolpaw, J.R.: Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain computer interface. Brain Research Bulletin 87(1), 130–134 (2012)
Kübler, A., Furdea, A., Halder, S., Hammer, E.M., Nijboer, F., Kotchoubey, B.: A brain-computer interface controlled auditory event-related potential (P300) spelling system for locked-in patients. Annals of the New York Acadamy of Sciences 1157, 90–100 (2009)
Lemm, S., Blankertz, B., Dickhaus, T., Müller, K.R.: Introduction to machine learning for brain imaging. NeuroImage 56(2), 387–399 (2011), http://www.sciencedirect.com/science/article/pii/S1053811910014163
Lim, J.H., Hwang, H.J., Jung, Y.J., Im, C.H.: Feature extraction for brain–computer interface (BCI) based on the functional causality analysis of brain signals. In: Proceedings of the 5th International Brain–Computer Interface Conference (2011)
Möller, E., Schack, B., Vath, N., Witte, H.: Fitting of one ARMA model to multiple trials increases the time resolution of instantaneous coherence. Biological Cybernetics 89, 303–312 (2003)
Müller-Putz, G.R., Scherer, R., Pfurtscheller, G., Rupp, R.: Brain-computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation. Biomedizinische Technik 51, 57–63 (2006)
Neuper, C., Pfurtscheller, G.: Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates. International Journal of Psychophysiology 43, 41–58 (2001)
Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proceedings of the IEEE 89, 1123–1134 (2001)
Schlögl, A., Keinrath, C., Zimmermann, D., Scherer, R., Leeb, R., Pfurtscheller, G.: A fully automated correction method of EOG artifacts in EEG recordings. Clinical Neurophysiology 118, 98–104 (2007)
Schlögl, A., Supp, G.: Analyzing event-related EEG data with multivariate autoregressive parameters. In: Neuper, C., Klimesch, W. (eds.) Event-related Dynamics of Brain Oscillations, pp. 135–147. Elsevier (2006)
Shoker, L., Sanei, S., Sumich, A.: Distinguishing between left and right finger movement from eeg using svm. In: 27th Annual International Conference on Engineering in Medicine and Biology Society, IEEE-EMBS 2005, pp. 5420–5423 (January 2005)
Storey, J.D.: A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(3), 479–498 (2002), http://dx.doi.org/10.1111/1467-9868.00346
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clinical Neurophysiology 113, 767–791 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Billinger, M., Brunner, C., Scherer, R., Holzinger, A., Müller-Putz, G.R. (2012). Towards a Framework Based on Single Trial Connectivity for Enhancing Knowledge Discovery in BCI. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds) Active Media Technology. AMT 2012. Lecture Notes in Computer Science, vol 7669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35236-2_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-35236-2_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35235-5
Online ISBN: 978-3-642-35236-2
eBook Packages: Computer ScienceComputer Science (R0)