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Lopes-Dias, C; Sburlea, AI; Muller-Putz, GR.
A Generic Error-related Potential Classifier Offers a Comparable Performance to a Personalized Classifier.
Annu Int Conf IEEE Eng Med Biol Soc. 2020; 2020: 2995-2998. Doi: 10.1109/EMBC44109.2020.9176640
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Leading authors Med Uni Graz: Lopes Dias Maria Catarina
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Abstract:: Brain-computer interfaces (BCIs) provide more independence to people with severe motor disabilities but current BCIs' performance is still not optimal and often the user's intentions are misinterpreted. Error-related potentials (ErrPs) are the neurophysiological signature of error processing and their detection can help improving a BCI's performance.A major inconvenience of BCIs is that they commonly require a long calibration period, before the user can receive feedback of their own brain signals. Here, we use the data of 15 participants and compare the performance of a personalized ErrP classifier with a generic ErrP classifier. We concluded that there was no significant difference in classification performance between the generic and the personalized classifiers (Wilcoxon signed rank tests, two-sided and one-sided left and right). This results indicate that the use of a generic ErrP classifier is a good strategy to remove the calibration period of a ErrP classifier, allowing participants to receive immediate feedback of the ErrP detections.
Find related publications in this database (using NLM MeSH Indexing): Brain - administration & dosage; Brain-Computer Interfaces - administration & dosage; Calibration - administration & dosage; Electroencephalography - administration & dosage; Feedback - administration & dosage; Humans - administration & dosage