Spectral power estimation for unevenly spaced motor imagery data

Junhua Li, Zbigniew Struzik, Liqing Zhang, Andrzej Cichocki

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

2 Citations (Scopus)

Abstract

The human brain can send a command to external devices or communicate with the outside environment by the means of a brain computer interface (BCI) system. The effectiveness depends on how precisely specific brain activities can be identified from EEG. Noise is usually mixed into the EEG signal, and cannot be separated or filtered out in some cases. In a practical BCI system, the whole segment of EEG is discarded when a portion of that segment is contaminated by extreme noise or artifacts. This leads to the weakness that the BCI system cannot output decoding results during the period of that discarded segment. In order to solve this problem, we employed a Lomb-Scargle periodogram to estimate the spectral power based on an unevenly spaced segment, a portion of which has been removed due to noise contamination. According to the classification results of motor imagery data, the accuracy is not dramatically decreased along with increased proportion of data removal.

Original languageEnglish
Title of host publicationNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Pages168-175
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8226 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Neural Information Processing, ICONIP 2013
Country/TerritoryKorea, Republic of
CityDaegu
Period3/11/137/11/13

Keywords

  • Brain computer interface
  • Classification
  • Motor imagery
  • Spectral power estimation
  • Unevenly spaced data

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