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

Min Hye Chang, Kwangsuk Park

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

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages2220-2223
Number of pages4
DOIs
StatePublished - 31 Oct 2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: 3 Jul 20137 Jul 2013

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period3/07/137/07/13

Fingerprint

Brain-Computer Interfaces
Brain computer interface
Visual Evoked Potentials
Bioelectric potentials
Flickering
Power spectral density

Cite this

Chang, M. H., & Park, K. (2013). Frequency recognition methods for dual-frequency SSVEP based brain-computer interface. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 (pp. 2220-2223). [6609977] https://doi.org/10.1109/EMBC.2013.6609977
Chang, Min Hye ; Park, Kwangsuk. / Frequency recognition methods for dual-frequency SSVEP based brain-computer interface. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. 2013. pp. 2220-2223
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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.",
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Chang, MH & Park, K 2013, Frequency recognition methods for dual-frequency SSVEP based brain-computer interface. in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013., 6609977, pp. 2220-2223, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, Osaka, Japan, 3/07/13. https://doi.org/10.1109/EMBC.2013.6609977

Frequency recognition methods for dual-frequency SSVEP based brain-computer interface. / Chang, Min Hye; Park, Kwangsuk.

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. 2013. p. 2220-2223 6609977.

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

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AB - 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.

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Chang MH, Park K. Frequency recognition methods for dual-frequency SSVEP based brain-computer interface. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. 2013. p. 2220-2223. 6609977 https://doi.org/10.1109/EMBC.2013.6609977