Article

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

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BIOSTEC 2014: Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4

Pages 244 - 250

https://doi.org/10.5220/0004804602440250

Published: 03 March 2014 Publication History

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.

Index Terms

A Different Statistical Approach Aiming at EEG Parameter Investigation for Brain Machine Interface Use
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

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Published In

BIOSTEC 2014: Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4

March 2014

338 pages

ISBN:9789897580116

Editors:
Harald Loose
Brandenburg University of Applied Sciences
,
Guy Plantier
ESEO, GSII
,
Tanja Schultz
Karlsruhe Institute of Technology
,
Ana Fred
Instituto de Telecomunicações / IST
,
Hugo Gamboa
LIBPHYS-UNL / FCT - New University of Lisbon

Publisher

SCITEPRESS - Science and Technology Publications, Lda

Setubal, Portugal

Publication History

Published: 03 March 2014

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Recommendations

EEG based automated detection of seizure using machine learning approach and traditional features

Development of Brainwave Balancing Index Using EEG

Utilization of Genetic Algorithm for Optimal EEG Channel Selection in Brain-Computer Interface Application

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