Aggregating classification accuracy across time: Application to single trial EEG
Advances in Neural Inf. Proc. Systems (NIPS 06)
We present a method for binary on-line classiﬁcation of triggered but temporally blurred events that are embedded in noisy time series in the context
of on-line discrimination between left and right imaginary hand-movement.
In particular the goal of the binary classiﬁcation problem is to obtain the
decision, as fast and as reliably as possible from the recorded EEG single
trials. To provide a probabilistic decision at every time-point t the presented method gathers information from two distinct sequences of features
across time. In order to incorporate decisions from prior time-points we
suggest an appropriate weighting scheme, that emphasizes time instances,
providing a higher discriminatory power between the instantaneous class
distributions of each feature, where the discriminatory power is quantiﬁed
in terms of the Bayes error of misclassiﬁcation.
The eﬀectiveness of this procedure is veriﬁed by its successful application
in the 3rd BCI competition. Disclosure of the data after the competition
revealed this approach to be superior with single trial error rates as low as
10.7, 11.5 and 16.7% for the three diﬀerent subjects under study.