Striving for Better and Earlier Movement Prediction by Postprocessing of Classification Scores

Brain-computer interfaces that enable movement prediction are useful for many application fields from telemanipulation to rehabilitation. Current systems still struggle with a level of unreliability that requires improvement. Here, we investigate several postprocessing methods that operate on the classification outcomes. In particular, the data was classified after preprocessing using a support vector machine (SVM). The output of the SVM, i.e. the raw score values, were postprocessed using previously obtained scores to account for trends in the classification result. The respective methods differ in the way the transformation is performed. The idea is to use trends, like the rise of the score values approaching an upcoming movement, to yield a better prediction in terms of detection accuracy and/or an earlier time point. We present results from different subjects where upcoming voluntary movements of the right arm were predicted using the lateralized readiness potential from the EEG. The results illustrate that better and earlier predictions are indeed possible with the suggested methods. However, the best postprocessing method was rather subject-specific. Depending on the requirements of the application at hand, postprocessing the classification scores as suggested here can be used to find the best compromise between prediction accuracy and time point.