Supervision of time-frequency features selection in EEG signals by a human expert for brain-computer interfacing based on motor imagery

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 fr...

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Bibliographic Details
Published in2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) pp. 000861 - 000866
Main Authors Dupres, Alban, Cabestaing, Francois, Rouillard, Jose
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
brain-computer interface
Conferences
EEG signal processing
Electroencephalography
Feature extraction
feature selection
human expertise
Laplace equations
motor imagery
Pipelines
sparse feature set
Time-frequency analysis
Training
Online AccessGet full text
DOI10.1109/SMC.2016.7844348

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Summary: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.
DOI:10.1109/SMC.2016.7844348