Authors:
D. A. Blanco-Mora
1
;
A. Aldridge
1
;
C. Jorge
1
;
A. Vourvopoulos
2
;
P. Figueiredo
2
and
S. Bermúdez i Badia
3
;
1
Affiliations:
1
Madeira Interactive Techonologies Institute, Universidade da Madeira, Funchal, Portugal
;
2
Institute for Systems and Robotics - Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
;
3
Faculdade de Ciências Exatas e da Engenharia, Universidade da Madeira, Funchal, Portugal
Keyword(s):
Brain-computer Interface, BCI, Motor Imagery, MI, Classification Accuracy, Common Spatial Pattern, CSP, Electroencephalography, EEG, Neurorehabilitation, Stroke.
Abstract:
Motor imagery classification using electroencephalography is based on feature extraction over a length of
time, and different configurations of settings can alter the performance of a classifier. Nevertheless, there
is a lack of standardized settings for motor imagery classification. This work analyzes the effect of age on
motor imagery training performance for two common spatial pattern-based classifier pipelines and various
configurations of timing parameters, such as epochs, windows, and offsets. Results showed significant (p
≤ 0.01) inverse correlations between performance and feature quantity, as well as between performance and
epoch/window ratio.