title: Classifying the auditory P300 using mobile EEG recordings without calibration phase.
abstract: One of the major drawbacks in mobile EEG Brain Computer Interfaces (BCI) is the need for subject specific training data to train a classifier. By removing the need for supervised classification and calibration phase, new users could start immediate interaction with a BCI. We propose a solution to exploit the structural difference by means of canonical polyadic decomposition (CPD) for three-class auditory oddball data without the need for subject-specific information. We achieve this by adding average event-related-potential (ERP) templates to the CPD model. This constitutes a novel similarity measure between single-trial pairs and known-templates, which results in a fast and interpretable classifier. These results have similar accuracy to those of the supervised and cross-validated stepwise LDA approach but without the need for having subject-dependent data. Therefore the described CPD method has a significant practical advantage over the traditional and widely used approach.
authors.forename: R
authors.initials: R
authors.lastname: Zink
authors.forename: B
authors.initials: B
authors.lastname: Hunyádi
authors.forename: S
authors.initials: S
authors.lastname: Van Huffel
authors.forename: M
authors.initials: M
authors.lastname: De Vos
mesh_terms: D062207:Brain-Computer Interfaces; D002138:Calibration; D004569:Electroencephalography / Q000379:methods*; D018913:Event-Related Potentials, P300; D005072:Evoked Potentials, Auditory; D006801:Humans; D008962:Models, Theoretical; D015203:Reproducibility of Results; D055815:Young Adult