Supervision of time-frequency features selection in EEG signals by a human expert for brain–computer interfacing based on motor imagery - Archive ouverte HAL Access content directly
Conference Papers Year :

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

(1) , (1) , (1)
1

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.
Fichier principal
Vignette du fichier
16smcFinalVersion.pdf (543.16 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01394149 , version 1 (08-11-2016)

Identifiers

  • HAL Id : hal-01394149 , version 1

Cite

Alban Duprès, François Cabestaing, José Rouillard. Supervision of time-frequency features selection in EEG signals by a human expert for brain–computer interfacing based on motor imagery. Systems, Man, and Cybernetics (SMC), Oct 2016, Budapest, Hungary. ⟨hal-01394149⟩
212 View
190 Download

Share

Gmail Facebook Twitter LinkedIn More