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Combining canonical correlation analysis and infinite reference for frequency recognition of steady-state visual evoked potential recordings: A comparison with periodogram method

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 3rd International Conference on Biomedical Engineering and Biotechnology, 25–28 September 2014, Beijing, China

Article type: Research Article

Authors: Tian, Yin; ; ; | Li, Fali; | Xu, Peng | Yuan, Zhen; | Zhao, Dechun | Zhang, Haiyong

Affiliations: Bio-Information College, Chongqing University of Posts and Telecommunications, Chongqing 400065, China | Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau SAR, China | Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China

Note: [] Corresponding authors: Yin Tian, Bio-information College, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. Tel./Fax: 86-23-62460536; E-mail: YinTian2010@gmail.com.

Note: [] Zhen Yuan, Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau SAR, China. Tel./Fax: 853-88224989/88222314; E-mail: Zhenyuan@umac.mo.

Keywords: Steady-state visual evoked potentials, canonical correlation analysis with infinite reference, frequency recognition, periodogram, brain computer interface

DOI: 10.3233/BME-141109

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 2901-2908, 2014

Published: 2014
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Abstract

Steady-state visual evoked potentials (SSVEP) are the visual system responses to a repetitive visual stimulus flickering with the constant frequency and of great importance in the study of brain activity using scalp electroencephalography (EEG) recordings. However, the reference influence for the investigation of SSVEP is generally not considered in previous work. In this study a new approach that combined the canonical correlation analysis with infinite reference (ICCA) was proposed to enhance the accuracy of frequency recognition of SSVEP recordings. Compared with the widely used periodogram method (PM), ICCA is able to achieve higher recognition accuracy when extracts frequency within a short span. Further, the recognition results suggested that ICCA is a very robust tool to study the brain computer interface (BCI) based on SSVEP.

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