Contributors:
Thorsten Zander, FIRST FhG (Student)
Guido Dornhege, FIRST FhG
Benjamin Blankertz, FIRST FhG
This approach works on AAR models, whose coefficients where calculated
for 'C3' and 'C4' on each single trial [1]. A weight vector in time,
which reflects the difference in ERD between the given classes, is
determined by an optimization procedure [2]. For this purpose the time
course of the amplitude of the mu-rythm was estimated by calculating
the band power 9-11 Hz in the power spectrum obtained from the AAR
models. This was performed for different choices of the
AAR-model-parameters 'order' and 'update coefficient'. Some of the
resulting features were combined to optimize the cross-validation
performance of a linear classifier [3]. For calculating the confidence
at time 't' of a test trial, the trained classifier is applied to the
(incomplete) trial up to and including 't' and the output is weighted by the
above given ERD measure, normalized by the intra-class variances (of
the estimated classes) and sumed up. So this approach works in a
causal way.
The confidence and classification results are given from 2s to 8.85s.
[1] The AAR models were calculated by a matlab implementation of
A. Schloegel,TU Graz
[2] Based on an algorithm by Michael Zibulevsky, Technion Israel
[3] Feature Combination, Guido Dornhege FIRST FhG
Thanks for helpful talks with
Steven Lemm and Christin Schaefer
both FIRST FhG