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Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase.
Rob Zink1, Borbála Hunyadi, Sabine Van Huffel
1KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium. iMinds Medical IT, Leuven, Belgium.
Journal of Neural Engineering
|January 30, 2016
Summary
This study introduces a new method for brain-computer interfaces (BCI) that eliminates the need for subject-specific training. This approach enables faster BCI exploration for new users by allowing direct classification of EEG data.
Area of Science:
- Neuroscience
- Computer Science
- Signal Processing
Background:
- Brain-computer interfaces (BCI) typically require extensive subject-specific training, hindering rapid user adoption.
- The need for supervised calibration is a significant limitation in current EEG-based BCI systems.
Purpose of the Study:
- To develop a subject-independent BCI classification method, removing the need for a calibration phase.
- To enable faster and more intuitive BCI exploration for new users.
Main Methods:
- Exploration of canonical polyadic and block term tensor decompositions applied to electroencephalography (EEG) data.
- Construction of BCI tensors by concatenating event-related potential (ERP) templates from multiple subjects.
- Utilizing inherent tensor structure for accurate classification in a three-class auditory oddball paradigm.
Main Results:
- The novel tensor decomposition approach achieves fast and intuitive classification.
- Classification accuracies are competitive with traditional supervised linear discriminant analysis (LDA) methods.
Conclusions:
- The proposed tensor decomposition methods offer a promising alternative for BCI data classification.
- This approach provides a direct link to the P300 event-related potential (ERP) signal, outperforming conventional supervised methods.
