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  Oscillatory Source Tensor Discriminant Analysis (OSTDA): A regularized tensor pipeline for SSVEP-based BCI systems

Jorajuría, T., Jamshidi Idaji, M., İşcan, Z., Gómez, M., Nikulin, V. V., & Vidaurre, C. (2021). Oscillatory Source Tensor Discriminant Analysis (OSTDA): A regularized tensor pipeline for SSVEP-based BCI systems. Neurocomputing. doi:10.1016/j.neucom.2021.07.103.

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 Creators:
Jorajuría, Tania1, 2, Author
Jamshidi Idaji, Mina2, 3, 4, Author              
İşcan, Zafer5, Author
Gómez, Marisol1, Author
Nikulin, Vadim V.2, 6, Author              
Vidaurre, Carmen1, Author
Affiliations:
1Statistics, Informatics and Mathematics Department, Public University of Navarre, Spain, ou_persistent22              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
3International Max Planck Research School on Neuroscience of Communication, Leipzig, Germany, ou_persistent22              
4Machine Learning Group, Faculty of Electrical Engineering and Computer Science, TU Berlin, Germany, ou_persistent22              
5Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey, ou_persistent22              
6Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia, ou_persistent22              

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Free keywords: Brain-computer interface; Steady-state visual evoked potential; Spatio-spectral decomposition; Higher order discriminant analysis; Analytical regularization; Tensor-based feature reduction
 Abstract: Periodic signals called Steady-State Visual Evoked Potentials (SSVEP) are elicited in the brain by flickering stimuli. They are usually detected by means of regression techniques that need relatively long trial lengths to provide feedback and/or sufficient number of calibration trials to be reliably estimated in the context of brain-computer interface (BCI). Thus, for BCI systems designed to operate with SSVEP signals, reliability is achieved at the expense of speed or extra recording time. Furthermore, regardless of the trial length, calibration free regression-based methods have been shown to suffer from significant performance drops when cognitive perturbations are present affecting the attention to the flickering stimuli. In this study we present a novel technique called Oscillatory Source Tensor Discriminant Analysis (OSTDA) that extracts oscillatory sources and classifies them using the newly developed tensor-based discriminant analysis with shrinkage. The proposed approach is robust for small sample size settings where only a few calibration trials are available. Besides, it works well with both low- and high-number-of-channel settings, using trials as short as one second. OSTDA performs similarly or significantly better than other three benchmarked state-of-the-art techniques under different experimental settings, including those with cognitive disturbances (i.e. four datasets with control, listening, speaking and thinking conditions). Overall, in this paper we show that OSTDA is the only pipeline among all the studied ones that can achieve optimal results in all analyzed conditions.

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Language(s): eng - English
 Dates: 2021-05-282021-02-262021-07-252021-12-23
 Publication Status: Published online
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 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neucom.2021.07.103
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Title: Neurocomputing
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 0925-2312
CoNE: https://pure.mpg.de/cone/journals/resource/954925566733