%0 Journal Article
%T Frequency recognition in SSVEP-based BCIs using combination of PARAFAC decomposition and Canonical Component Analysis
%J Iranian Journal of Biomedical Engineering
%I Iranian Society for Biomedical Engineering
%Z 5869-2008
%A farhadnia, maryam
%A Hajipour, Sepideh
%A mikaili, mohammad
%D 2023
%\ 05/22/2023
%V 17
%N 1
%P 1-10
%! Frequency recognition in SSVEP-based BCIs using combination of PARAFAC decomposition and Canonical Component Analysis
%K Electroencephalogram (EEG)
%K Brain-Computer Interface (BCI). Steady State Visual Evoked Potential (SSVEP)
%K Multivariate Canonical Correlation Analysis (MCCA)
%K PARAFAC decomposition
%R 10.22041/ijbme.2023.1974395.1814
%X Today, usage of brain-computer interface systems based on steady-state visual evoked potentials (SSVEPs) has been increased due to some advantages such as acceptable accuracy and minimal need for user training. Steady-state visual potentials are one of the most important patterns used in BCI systems, which are generated in the occipital region of the brain by visual stimulation between 6 and 60 Hz. One of the effective methods for extracting the SSVEP frequency in BCI systems is called the Multiway Correlation Coefficient Analysis (MCCA) method, which is a tensorized version of the classical Correlation Coefficient Analysis (CCA) method and is based on multidimensional data.In this paper, inspired by the MCCA method, two new algorithms (PARAFAC-CCA and C-PARAFAC-CCA) have been proposed using the combination of CCA and PARAFAC decomposition. The purpose of the proposed algorithms is to improve the initial reference signal and achieve higher accuracy in SSVEP frequency detection in BCI systems. In the PARAFAC-CCA algorithm, after performing the PARAFAC decomposition on the multidimensional training data and obtaining the time component, the CCA method is implemented between the obtained time component and the sine-cosine reference signal, and the optimal reference signal is made from its output. Finally, the MLR algorithm is used between the EEG test data and the optimal reference signal in order to achieve the target frequency. The general steps of the C-PARAFAC-CCA algorithm are also similar to PARAFAC-CCA, with the difference that in the calculation of the time component, constrained PARAFAC is used in such a way that in each step of the ALS algorithm, CCA is applied once and the time component is improved. The efficiency of the proposed algorithms was investigated on the real data set and it was shown that compared to the MCCA method, the proposed algorithms have reached a higher average accuracy.
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