Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution

Antonio Quintero-Rincón, Arlos D'Giano, Hadj Batatia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper deals with the detection of mu- suppression from electroencephalographic (EEG) signals in brain-computer interface (BCI). For this purpose, an efficient algorithm is proposed based on a statistical model and a linear classifier. Precisely, the generalized extreme value distribution (GEV) is proposed to represent the power spectrum density of the EEG signal in the central motor cortex. The associated three parameters are estimated using the maximum likelihood method. Based on these parameters, a simple and efficient linear classifier was designed to classify three types of events: imagery, movement, and resting. Preliminary results show that the proposed statistical model can be used in order to detect precisely the mu-suppression and distinguish different EEG events, with very good classification accuracy.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
ISBN (Electronic)9781728169262
DOIs
Publication statusPublished - 28 Sep 2020
Event2020 International Joint Conference on Neural Networks - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameInternational Joint Conference on Neural Networks
ISSN (Electronic)2161-4407

Conference

Conference2020 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2020
CountryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

Keywords

  • Brain-computer interface
  • Electroencephalography
  • Generalized extreme value
  • Motor imagery
  • Mu-suppression

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution'. Together they form a unique fingerprint.

Cite this