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Authors: Maria Claudia F. de Castro and Fabio Gerab

Affiliation: Centro Universitário da FEI, Brazil

ISBN: 978-989-758-011-6

Keyword(s): EEG, Power Spectral Density, Frequency Bands, Spatial Feature Selection, Pattern Recognition, Statistical Analysis.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

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. (More)

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Paper citation in several formats:
Castro, Maria Claudia F. 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

@conference{biosignals14,
author={Castro, Maria Claudia F. 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},
}

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 - Castro, Maria Claudia F.
AU - Gerab, F.
PY - 2014
SP - 244
EP - 250
DO - 10.5220/0004804602440250

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