A Different Statistical Approach Aiming at EEG Parameter Investigation for Brain Machine Interface Use

Maria Claudia F. de Castro, Fabio Gerab

2014

Abstract

A lot of effort has been made to investigate EEG features that could better represent signal characteristics. The results are usually based on the best mean recognition rates and statistical analysis is done only when different methods are compared. In this work, we propose a new approach that applies multiple rate intercomparisons based on large samples aiming at detecting differences among treatments in order to recognize their importance for the classification rates. Ten frequency band compositions expressed by power spectral density averages were extracted from 8 EEG channels during 4 motor imageries, and spatial feature selections were also considered during the recognition process. Classification rate in large samples can be represented by a normal distribution and, for multiple rate inter-comparisons, the level of significance was corrected based on the Bonferroni Method. The variables were considered to be independents and the test was performed as non paired samples in a very conservative approach. The results showed that there are significant differences among cases of spatial feature selection and thus the considered electrodes are important parameters. On the other hand, considering or not the Delta and Theta bands along with different arrangements for Gamma band resulted in no significant difference.

References

  1. Al-Ani, T. and Trad, D. (2010). Signal Processing and Classification Approaches for Brain-computer Interface. In Somerset, V. S., editor, Intelligent and Biosensors, chapter 2. InTech.
  2. Bland, J. M. and Altman, D. G. (1995). significance tests: the bonferroni method. 310(6973):170.
  3. Castro, M. C. F., Galhianne, J. P. O. P., and Colombini, E. L. (2013). EEG Motor Imagery Classification of Upper Limb Movements. In International Conference on Bioinspired Systems and Signal Processing., pages 314-317.
  4. Fitzgibbon, S., Pope, K., Mackenzie, L., Clark, C., and Wiloughby, J. (2004). Cognitive tasks augment gamma EEG power. Clinical Neurophysiology, 115(8):1802-1809.
  5. Hema, C. R., Paulraj, M. P., Yaacob, S., Adom, A. H., and Nagarajan, R. (2010). An Analysis of the Effect of EEG Frequency Bands on the Classification of Motor Imagery Signals. Biomedical Soft Computing and Human Sciences, 16(1):121-126.
  6. Herman, P., Prasad, G., McGinnity, T. M., and Coyle, D. (2008). Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(4):317-26.
  7. Higashi, H., Tanaka, T., and Funase, A. (2009). Classification of single trial EEG during imagined hand movement by rhythmic component extraction. In 31st Annual International Conference of the IEEE EMBS, pages 2482-5.
  8. Liu, H., Wang, J., Zheng, C., and He, P. (2005). Study on the effect of different frequency bands of EEG signals on mental tasks classification. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society., volume 5, pages 5369-5372.
  9. Millan, J. D. R., Rupp, R., Muller-Putz, G. R., MurraySmith, R., Giugliemma, C., Tangermann, M., Vidaurre, C., Cincotti, F., Kubler, A., Leeb, R., Neuper, C., Muller, K. R., and Mattia, D. (2010). Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. Frontiers in Neuroscience, 4:161-193.
  10. Thomaz, C. and Gillies, D. (2005). Approach for limited sample size problems - with application to face recognition. In XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI05), volume 12, pages 89-96.
Download


Paper Citation


in Harvard Style

Claudia F. de Castro M. and Gerab F. (2014). A Different Statistical Approach Aiming at EEG Parameter Investigation for Brain Machine Interface Use . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014) ISBN 978-989-758-011-6, pages 244-250. DOI: 10.5220/0004804602440250


in Bibtex Style

@conference{biosignals14,
author={Maria Claudia F. de Castro and Fabio Gerab},
title={A Different Statistical Approach Aiming at EEG Parameter Investigation for Brain Machine Interface Use},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)},
year={2014},
pages={244-250},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004804602440250},
isbn={978-989-758-011-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)
TI - A Different Statistical Approach Aiming at EEG Parameter Investigation for Brain Machine Interface Use
SN - 978-989-758-011-6
AU - Claudia F. de Castro M.
AU - Gerab F.
PY - 2014
SP - 244
EP - 250
DO - 10.5220/0004804602440250