  Template-based signal detection most often relies on computing a correlation,
or a dot product, between an incoming data stream and a signal template. While
such a correlation results in an ongoing estimate of the magnitude of the
signal in the data stream, it does not directly indicate the presence or
absence of a signal. Instead, the problem of signal detection is one of
model-selection. Here we explore the use of the Bayesian odds-ratio (OR), which
is the ratio of posterior probabilities of a signal-plus-noise model over a
noise-only model. We demonstrate this method by applying it to simulated
electroencephalographic (EEG) signals based on the P300 response, which is
widely used in both Brain Computer Interface (BCI) and Brain Machine Interface
(BMI) systems. The efficacy of this algorithm is demonstrated by comparing the
receiver operating characteristic (ROC) curves of the OR-based (logOR) filter
to the usual correlation method where we find a significant improvement in P300
detection. The logOR filter promises to improve the accuracy and speed of the
detection of evoked brain responses in BCI/BMI applications as well the
detection of template signals in general.
