NDLI: Frequency recognition methods for dual-frequency SSVEP based brain-computer interface

Content Provider	IEEE Xplore Digital Library
Author	Min Hye Chang Kwang Suk Park
Copyright Year	2013
Description	Author affiliation: Interdiscipl. Program for Bioeng., Seoul Nat. Univ., Seoul, South Korea (Min Hye Chang) \|\| Dept. of Biomed. Eng., Seoul Nat. Univ., Seoul, South Korea (Kwang Suk Park)
Abstract	Dual-frequency steady-state visual evoked potential (SSVEP) was suggested to generate more stimuli using a few flickering frequencies for brain-computer interface. Dual-frequency SSVEP peaks at more than two frequencies-both main and harmonic frequencies. However multi-frequency recognition strategy has not been investigated for dual-frequency SSVEP. In this paper, three modified power spectral density analysis (PSDA) methods and two modified canonical correlation analysis (CCA) methods were tested for dual-frequency SSVEP classification. Three methods among the five methods used conventional features or classification techniques, and the other two methods used modified features for harmonic frequencies. As a result, CCA with novel features showed the best BCI performance. Also the use of harmonic frequencies improved BCI performance of dual-frequency SSVEP.
Starting Page	2220
Ending Page	2223
File Size	243024
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609977
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Harmonic analysis Correlation Signal to noise ratio Electroencephalography Accuracy Brain-computer interfaces Visualization
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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