Higher-order PLS for classification of ERPs with application to BCIs

Qibin Zhao, Liqing Zhang, Jianting Cao, Andrzej Cichocki

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

3 Citations (Scopus)

Abstract

The EEG signals recorded during Brain Computer Interfaces (BCIs) are naturally represented by multi-way arrays in spatial, temporal, and frequency domains. In order to effectively extract the underlying components from brain activities which correspond to the specific mental state, we propose the higher-order PLS approach to find the latent variables related to the target labels and then make classification based on latent variables. To this end, the low-dimensional latent space can be optimized by using the higher-order SVD on a cross-product tensor, and the latent variables are considered as shared components between observed data and target output. The EEG signals recorded under the P300-type affective BCI paradigm were used to demonstrate the effectiveness of our new approach.

Original languageEnglish
Title of host publication2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
Publication statusPublished - 2012
Externally publishedYes
Event2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012 - Hollywood, CA, United States
Duration: 3 Dec 20126 Dec 2012

Publication series

Name2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012

Conference

Conference2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
Country/TerritoryUnited States
CityHollywood, CA
Period3/12/126/12/12

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