Feature extraction by nonnegative tucker decomposition from eeg data including testing and training observations

Fengyu Cong, Anh Huy Phan, Qibin Zhao, Qiang Wu, Tapani Ristaniemi, Andrzej Cichocki

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

5 Citations (Scopus)

Abstract

The under-sample classification problem is discussed for 21 normal childrenand 21 children with reading disability. We first rejected data of one subject in each group and produced 441 sub-datasets including 40 subjects in each. Regarding each sub-dataset, we extracted features through nonnegative Tucker decomposition (NTD) from event-related potentials, and used the leave-one-out paradigm for classification. Averaged accuracies over 441 sub-datasets were 77.98% (linear discriminate analysis), 73.55% (support vector machine), and 76.97% (adaptive boosting). In summary, assuming K observations with known labels, for the new observation without the group information, the feature of the new observation can be extracted through performing NTD to extract features from data of all observations (K+1). Since the group information of the first K observations is known, their features can train the classifier, and then, the trained classifier recognizes new features to determine the group information of new observation.

Original languageEnglish
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages166-173
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7665 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Neural Information Processing, ICONIP 2012
Country/TerritoryQatar
CityDoha
Period12/11/1215/11/12

Keywords

  • Classification
  • Event-related potential
  • Mismatch negativity
  • Multidomain feature extraction
  • Nonnegative Tucker decomposition
  • Undersample

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