EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition

Esteve Gallego-Jutglà, Tomasz M. Rutkowski, Andrzej Cichocki, Jordi Solé-Casals

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%.

Original languageEnglish
Title of host publicationIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence
Pages670-676
Number of pages7
Publication statusPublished - 2012
Externally publishedYes
Event4th International Joint Conference on Computational Intelligence, IJCCI 2012 - Barcelona, Spain
Duration: 5 Oct 20127 Oct 2012

Publication series

NameIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence

Conference

Conference4th International Joint Conference on Computational Intelligence, IJCCI 2012
Country/TerritorySpain
CityBarcelona
Period5/10/127/10/12

Keywords

  • Alzheimer Disease
  • Artifacts
  • EEG
  • Linear Discriminant Analysis
  • Multivariate Empirical Mode Decomposition
  • Neural Networks

Fingerprint

Dive into the research topics of 'EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition'. Together they form a unique fingerprint.

Cite this