Maximizing information transfer rates in an SSVEP-based BCI using individualized Bayesian probability measures. Academic Article uri icon

abstract

  • Successful brain-computer interfaces (BCIs) swiftly and accurately communicate the user's intention to a computer. Typically, information transfer rate (ITR) is used to measure the performance of a BCI. We propose a multi-step process to speed up detection and classification of the user's intent and maximize ITR. Users randomly looked at 4 frequency options on the interface in two sessions, one without and one with performance feedback. Analysis was performed off-line. A ratio of the canonical correlation analysis (CCA) coefficients was used to construct a Bayesian probability model and a thresholding method for the ratio of the posterior probability of the target frequency over maximal posterior probability of non-target frequencies was used as classification criteria. Moreover, the probability thresholds were optimized for each frequency, subject to maximizing the ITR. We achieved a maximum ITR of 39.82 bit/min. Although the performance feedback did not improve the overall ITR, it did improve the accuracy measure. Possible applications in the medical industry are discussed.

published proceedings

  • Annu Int Conf IEEE Eng Med Biol Soc

author list (cited authors)

  • Reagor, M. K., Zong, C., & Jafari, R.

citation count

  • 5

complete list of authors

  • Reagor, Mary K||Zong, Chengzhi||Jafari, Roozbeh

publication date

  • January 2014