CenEEGs: valid EEG selection for classification

Chenglong Dai, Dechang Pi, Stefanie I. Becker, Jia Wu, Lin Cui, Blake Johnson

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


This article explores valid brain electroencephalography (EEG) selection for EEG classification with different classifiers, which has been rarely addressed in previous studies and is mostly ignored by existing EEG processing methods and applications. Importantly, traditional selection methods are not able to select valid EEG signals for different classifiers. This article focuses on a source control-based valid EEG selection to reduce the impact of invalid EEG signals and aims to improve EEG-based classification performance for different classifiers. We propose a novel centroid-based EEG selection approach named CenEEGs, which uses a scale-and-shift-invariance similarity metric to measure similarities of EEG signals and then applies a globally optimal centroid strategy to select valid EEG signals with respect to a similarity threshold. A detailed comparison with several state-of-the-art time series selection methods by using standard criteria on 8 EEG datasets demonstrates the efficacy and superiority of CenEEGs for different classifiers.

Original languageEnglish
Article number18
Pages (from-to)1-25
Number of pages25
JournalACM Transactions on Knowledge Discovery from Data
Issue number2
Publication statusPublished - Mar 2020


  • centroid searching
  • classification
  • EEG similarity
  • Electroencephalography (EEG) selection


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