A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials
Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibratio...
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| Published in | PloS one Vol. 10; no. 10; p. e0140703 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
19.10.2015
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0140703 |
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| Abstract | Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibration. However, the detection performance can be deteriorated by the interference from the spontaneous EEG activities. Recently, various extended methods have been developed to incorporate individual EEG calibration data in CCA to improve the detection performance. Although advantages of the extended CCA methods have been demonstrated in separate studies, a comprehensive comparison between these methods is still missing. This study performed a comparison of the existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment. Classification accuracy and information transfer rate (ITR) were used for performance evaluation. The results suggest that individual calibration data can significantly improve the detection performance. Furthermore, the results showed that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance. |
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| AbstractList | Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibration. However, the detection performance can be deteriorated by the interference from the spontaneous EEG activities. Recently, various extended methods have been developed to incorporate individual EEG calibration data in CCA to improve the detection performance. Although advantages of the extended CCA methods have been demonstrated in separate studies, a comprehensive comparison between these methods is still missing. This study performed a comparison of the existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment. Classification accuracy and information transfer rate (ITR) were used for performance evaluation. The results suggest that individual calibration data can significantly improve the detection performance. Furthermore, the results showed that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance. |
| Audience | Academic |
| Author | Nakanishi, Masaki Jung, Tzyy-Ping Wang, Yu-Te Wang, Yijun |
| AuthorAffiliation | University of Electronic Science and Technology of China, CHINA 3 State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China 2 Center for Advanced Neurological Engineering, Institute of Engineering in Medicine, University of California San Diego, La Jolla, California, United States of America 1 Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America |
| AuthorAffiliation_xml | – name: 3 State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China – name: 1 Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America – name: 2 Center for Advanced Neurological Engineering, Institute of Engineering in Medicine, University of California San Diego, La Jolla, California, United States of America – name: University of Electronic Science and Technology of China, CHINA |
| Author_xml | – sequence: 1 givenname: Masaki surname: Nakanishi fullname: Nakanishi, Masaki – sequence: 2 givenname: Yijun surname: Wang fullname: Wang, Yijun – sequence: 3 givenname: Yu-Te surname: Wang fullname: Wang, Yu-Te – sequence: 4 givenname: Tzyy-Ping surname: Jung fullname: Jung, Tzyy-Ping |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26479067$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2015 Public Library of Science 2015 Nakanishi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2015 Nakanishi et al 2015 Nakanishi et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Conceived and designed the experiments: MN YW Y-TW. Performed the experiments: MN Y-TW YW. Analyzed the data: MN YW. Wrote the paper: MN YW T-PJ. |
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| SubjectTerms | Adult Algorithms Analysis Brain Brain-Computer Interfaces Calibration Canonical correlation (Statistics) Communication Computer simulation Correlation Correlation analysis Data processing EEG Electroencephalography Engineering Evoked potentials Evoked Potentials, Visual Female Fourier transforms Human-computer interface Humans Identification Information transfer Interfaces Life assessment Male Medicine Methods Neurosciences Performance evaluation Reference signals Signal processing Statistics as Topic - methods Steady state Steady state theory Studies User training Visual evoked potentials |
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| Title | A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials |
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