Sequential selection of window length for improved SSVEP-based BCI classification

Erik C. Johnson, James J.S. Norton, David Jun, Timothy Bretl, Douglas L. Jones

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

Abstract

Brain-computer interfaces (BCI) utilizing steady-state visually evoked potentials (SSVEP) recorded by electroencephalography (EEG) have exciting potential to enable new systems for disabled individuals and novel controls for robotic and computer systems. To interact with SSVEP-based BCIs, users attend to visual stimuli modulated at predetermined frequencies. A key problem for SSVEP-based BCIs is to classify which modulation frequency the user is attending, for which there is an inherent trade-off between speed and accuracy. As SSVEP signals vary with time and stimulation frequency, a fixed-length data window does not necessarily optimize this trade-off. We propose a strategy, developed from sequential analysis, to vary the window-length used for classification. Our proposed technique adapts to the data, continuing to collect data until it is confident enough to make a classification decision. Our strategy was compared to a fixed window-length method using a simple experiment involving five frequencies presented individually to three participants. Using a canonical correlation analysis classifier to compare the proposed variable-length scheme to a standard fixed-length scheme, the variable-length approach improved the classifier information transfer rate by an average of 43%.

Original languageEnglish (US)
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages7060-7063
Number of pages4
DOIs
StatePublished - Oct 31 2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: Jul 3 2013Jul 7 2013

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period7/3/137/7/13

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Johnson, E. C., Norton, J. J. S., Jun, D., Bretl, T., & Jones, D. L. (2013). Sequential selection of window length for improved SSVEP-based BCI classification. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 (pp. 7060-7063). [6611184] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2013.6611184