Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/6023

Pseudo-Online BMI Based on EEG to Detect the Appearance of Sudden Obstacles during Walking


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Title:
Pseudo-Online BMI Based on EEG to Detect the Appearance of Sudden Obstacles during Walking
Authors:
Elvira, María
Láñez, E.
Quiles Zamora, Vicente
Ortiz, Mario
Azorín Poveda, José María
Department:
Departamentos de la UMH::Ingeniería de Sistemas y Automática
Issue Date:
2019-12-10
Abstract:
The aim of this paper is to describe new methods for detecting the appearance of unexpected obstacles during normal gait from EEG signals, improving the accuracy and reducing the false positive rate obtained in previous studies. This way, an exoskeleton for rehabilitation or assistance of people with motor limitations commanded by a Brain-Machine Interface (BMI) could be stopped in case that an obstacle suddenly appears during walking. The EEG data of nine healthy subjects were collected during their normal gait while an obstacle appearance was simulated by the projection of a laser line in a random pattern. Different approaches were considered for selecting the parameters of the BMI: subsets of electrodes, time windows and classifier probabilities, which were based on a linear discriminant analysis (LDA). The pseudo-online results of the BMI for detecting the appearance of obstacles, with an average percentage of 63.9% of accuracy and 2.6 false positives per minute, showed a significant improvement over previous studies
Keywords/Subjects:
Brain-Machine Interface (BMI)
EEG
obstacle
gait
Type of document:
application/pdf
Access rights:
info:eu-repo/semantics/openAccess
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