Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase

One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration ph...

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Bibliographic Details
Published in:Journal of neural engineering Vol. 13; no. 2; p. 026005
Main Authors: Zink, Rob, Hunyadi, Borbála, Huffel, Sabine Van, Vos, Maarten De
Format: Journal Article
Language:English
Published: England 01.04.2016
Subjects:
Acoustic Stimulation - classification
Acoustic Stimulation - methods
Acoustic Stimulation - standards
Adult
Auditory Cortex - physiology
Brain-Computer Interfaces - classification
Brain-Computer Interfaces - standards
Calibration
Female
Humans
Male
Young Adult
ISSN:1741-2552
Online Access:Get more information
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Summary:One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.
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ISSN:1741-2552
DOI:10.1088/1741-2560/13/2/026005