title: Maximizing information transfer rates in an SSVEP-based BCI using individualized Bayesian probability measures.
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.
authors.forename: Mary K
authors.initials: MK
authors.lastname: Reagor
authors.forename: Chengzhi
authors.initials: C
authors.lastname: Zong
authors.forename: Roozbeh
authors.initials: R
authors.lastname: Jafari
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D062207:Brain-Computer Interfaces; D004566:Electrodes; D004569:Electroencephalography / Q000295:instrumentation; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D014793:Visual Cortex / Q000502:physiology