Non-supervised Feature Selection: Evaluation in a BCI for Single-Trial Recognition of Gait Preparation/Stop

Denis Delisle-Rodriguez, Ana Cecilia Villa-Parra, Alberto López-Delis, Anselmo Frizera-Neto, Eduardo Rocon, Teodiano Freire-Bastos

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Is presented a non-supervised method for feature selection based on similarity index, which is applied in a brain-computer interface (BCI) to recognize gait preparation/stops. Maximal information compression index is here used to obtain redundancies, while representation entropy value is employed to find the feature vectors with high entropy. EEG signals of six subjects were acquired on the primary cortex during walking, in order to evaluate this approach in a BCI. The maximum accuracy was 55% and 85% to recognize gait preparation/stops, respectively. Thus, this method can be used in a BCI to improve the time delay during dimensionality reduction.

Original languageEnglish
Title of host publicationBiosystems and Biorobotics
PublisherSpringer International Publishing
Number of pages6
StatePublished - 2017

Publication series

NameBiosystems and Biorobotics
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570

Bibliographical note

Funding Information:
Acknowledgments Authors would like to thank CNPq, CAPES, FAPES and SENESCYT for supporting this research.

Publisher Copyright:
© 2017, Springer International Publishing AG.


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