Paper Info
Title
Striving for Better and Earlier Movement Prediction by Postprocessing of Classification Scores.
Abstract
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.
Year
DOI
Venue
2013
10.5220/0004632600130020
NEUROTECHNIX: PROCEEDINGS OF THE INTERNATIONAL CONGRESS ON NEUROTECHNOLOGY, ELECTRONICS AND INFORMATICS
Keywords
Field
DocType
EEG,PRP,Brain-computer Interface,Classification Score,Movement Prediction,Online Prediction
Computer science,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Sirko Straube1948.62
Anett Seeland2163.59
David Feess300.34