Binary On-line Classification Based on Temporally Integrated Information

We present a method for on-line classification of triggered but temporally blurred events that are embedded in noisy time series. This means that the time point at which an event is initiated or a dynamical system is perturbed is known, e.g., the moment an injection of a therapeutic agent is given to a patient. From the ongoing monitoring of the system one has to derive a classification of the event or the induced change of the state of the system, e.g., whether the state of health improves or degrades. For simplification we assume that the reactions form two classes of interest. In particular the goal of the binary classification problem is to obtain the decision on-line, as fast and as reliable as possible.

To provide a probabilistic decision at every time-point

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the presented method gathers information across time by incorporating decisions from prior time-points using an appropriate weighting scheme. For this specific weighting we utilize the Bayes error to gain insight into the discriminative power between the instantaneous class distributions.

The effectiveness of this procedure is verified by its successful application in the context of a Brain Computer Interface, especially to the binary discrimination task of left against right imaginary hand-movements from ongoing raw EEG data.