Paper Info
Title
Supervision Of Time-Frequency Features Selection In Eeg Signals By A Human Expert For Brain-Computer Interfacing Based On Motor Imagery
Abstract
In the context of brain-computer interfacing based on motor imagery, we propose a method which allows an expert to select manually time-frequency features. This selection is performed specifically for each subject, by analysing a set of curves that emphasize differences of brain activity recorded from electroencephalographic signals during the execution of various motor imagery tasks. We will show that expert knowledge is very valuable to supervise the selection of a sparse set of significant time-frequency features. Features selection is performed through a graphical user interface to allow an easy access to experts with no specific programming skills. In this paper, we compare our method with three fully-automatic features selection methods, using dataset 2A of BCI competition IV. Results are better for five of the nine subjects compared to the best competing method.
Year
Venue
Keywords
2016
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
brain-computer interface, motor imagery, EEG signal processing, sparse feature set, feature selection, human expertise
Field
DocType
ISSN
Feature selection,Pattern recognition,Computer science,Brain–computer interface,Interfacing,Feature extraction,Brain activity and meditation,Graphical user interface,Artificial intelligence,Machine learning,Electroencephalography,Motor imagery
Conference
1062-922X
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Alban Duprès112.03
François Cabestaing26010.67
Jose Rouillard3409.58