Authors:
Maria Claudia F. de Castro
and
Fabio Gerab
Affiliation:
Centro Universitário da FEI, Brazil
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 ver
y 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.
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