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Erschienen in:
Electroencephalogram (EEG) and Its Background Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

8. Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications

verfasst von : Siuly Siuly, Yan Li, Yanchun Zhang

Erschienen in: EEG Signal Analysis and Classification

Verlag: Springer International Publishing

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Abstract

One crucial and challenging issue in BCI systems is the identification of motor imagery (MI) task based EEG signals in the biomedical engineering research area. Although BCI techniques have been developing quickly in recent decades, there remains a number of unsolved problems such as the improvement of MI signal classification.This chapter proposes a new approach, the ‘Cross-correlation aided logistic regression model’ called “CC-LR” for efficient identification of MI tasks.

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Vorheriges Kapitel Injecting Principal Component Analysis with the OA Scheme in the Epileptic EEG Signal Classification
Nächstes Kapitel Modified CC-LR Algorithm for Identification of MI-Based EEG Signals
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Metadaten
Titel
Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications
verfasst von
Siuly Siuly
Yan Li
Yanchun Zhang
Copyright-Jahr
2016
Buch
EEG Signal Analysis and Classification

Print ISBN: 978-3-319-47652-0

Electronic ISBN: 978-3-319-47653-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47653-7_8

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