  Template-based signal detection most often relies on computing a correlation,
or a dot product, between an incoming data stream and a signal template. Such a
correlation results in an ongoing estimate of the magnitude of the signal in
the data stream. However, it does not directly indicate the presence or absence
of the signal. The problem is really one of model-testing, and the relevant
quantity is the Bayesian evidence (marginal likelihood) of the signal model.
Given a signal template and an ongoing data stream, we have developed an
evidence-based filter that computes the Bayesian evidence that a signal is
present in the data. We demonstrate this algorithm by applying it to
brain-machine interface (BMI) data obtained by recording human brain electrical
activity, or electroencephalography (EEG). A very popular and effective
paradigm in EEG-based BMI is based on the detection of the P300 evoked brain
response which is generated in response to particular sensory stimuli. The goal
is to detect the presence of a P300 signal in ongoing EEG activity as
accurately and as fast as possible. Our algorithm uses a subject-specific P300
template to compute the Bayesian evidence that a applying window of EEG data
contains the signal. The efficacy of this algorithm is demonstrated by
comparing receiver operating characteristic (ROC) curves of the evidence-based
filter to the usual correlation method. Our results show a significant
improvement in single-trial P300 detection. The evidence-based filter promises
to improve the accuracy and speed of the detection of evoked brain responses in
BMI applications as well the detection of template signals in more general
signal processing applications
