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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| Vydané v: | Journal of neural engineering Ročník 13; číslo 2; s. 026005 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
01.04.2016
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| ISSN: | 1741-2552 |
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| Abstract | 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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| AbstractList | 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. OBJECTIVEOne 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.APPROACHWe 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.MAIN RESULTSThe presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach.SIGNIFICANCEThe 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. |
| Author | Huffel, Sabine Van Vos, Maarten De Hunyadi, Borbála Zink, Rob |
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| Snippet | 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... OBJECTIVEOne of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need... |
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| SubjectTerms | 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 |
| Title | Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase |
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