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- W2293421906 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." @default.
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- W2293421906 date "2015-08-01" @default.
- W2293421906 modified "2023-09-23" @default.
- W2293421906 title "Classifying the auditory P300 using mobile EEG recordings without calibration phase" @default.
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- W2293421906 doi "https://doi.org/10.1109/embc.2015.7318723" @default.
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