A partial least squares-based stimulus frequency recognition model for steady-state visual evoked potentials detection

Ruimin Wang, Yue Leng, Yuankui Yang, Wen Wu, Keiji Iramina, Sheng Ge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

With shorter calibration times and higher information transfer rates, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been studied most activity in recent years. Target identification is the ongoing core task in BCI researches, and plays a significant role in practical applications. In order to improve the performance of SSVEP-based BCI system, we proposed a partial least squares (PLS)-based stimulus frequency recognition model for SSVEP detection. Moreover, we compared the proposed method with canonical correlation analysis (CCA) and least absolute shrinkage and selection operator (LASSO) method, respectively. The experiment results showed that PLS can not only extract the SSVEP features effectively, but also can increase the classification accuracies of SSVEP-based BCI systems.

Original languageEnglish
Title of host publicationProceedings - 2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages699-703
Number of pages5
ISBN (Electronic)9781479958382
DOIs
Publication statusPublished - 2014
Event2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014 - Dalian, China
Duration: Oct 14 2014Oct 16 2014

Other

Other2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014
CountryChina
CityDalian
Period10/14/1410/16/14

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All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Health Information Management
  • Information Systems
  • Biomedical Engineering
  • Health Informatics

Cite this

Wang, R., Leng, Y., Yang, Y., Wu, W., Iramina, K., & Ge, S. (2014). A partial least squares-based stimulus frequency recognition model for steady-state visual evoked potentials detection. In Proceedings - 2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014 (pp. 699-703). [7002863] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BMEI.2014.7002863