EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme
Joint Authors
Ji, Hongfei
Li, Jie
Lu, Rongrong
Gu, Rong
Cao, Lei
Gong, Xiaoliang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-03
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials.
There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI.
However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures.
As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously.
In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification.
Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly.
Therefore, it has great potential use for hybrid BCI.
American Psychological Association (APA)
Ji, Hongfei& Li, Jie& Lu, Rongrong& Gu, Rong& Cao, Lei& Gong, Xiaoliang. 2016. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1099588
Modern Language Association (MLA)
Ji, Hongfei…[et al.]. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-15.
https://search.emarefa.net/detail/BIM-1099588
American Medical Association (AMA)
Ji, Hongfei& Li, Jie& Lu, Rongrong& Gu, Rong& Cao, Lei& Gong, Xiaoliang. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1099588
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099588