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Hybrid approach: combining eCCA and SSCOR for enhancing SSVEP decoding

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Abstract

Currently, steady-state visual evoked potentials (SSVEPs) are applied in a variety of fields. In these applications, spatial filtering is the most commonly used method for decoding SSVEPs. However, existing methods for the SSVEP decoding require improvements in terms of the interaction speed, accuracy, and ease of use. Recent advances in performance have been attributed to the existing hybridisation-based approaches. This article presents and evaluates a novel approach for target identification of SSVEP. The suggested target recognition method significantly enhances the performance of SSVEP decoding by combining two existing methods: extended canonical correlation analysis (eCCA) and the sum of squared correlation (SSCOR). This combined approach is referred to as the extended CCA and sum of squared correlation approach (eCCA-SSCOR). The results of the offline experimental comparison are based on two publicly available datasets, including the San Diego dataset, which comprises 12 frequency targets recorded from 10 individuals, and the Benchmark dataset, comprising 40 frequency targets from 35 subjects. The statistical significance of the improvement was tested using paired samples t-tests and Wilcoxon rank-sum tests. The results indicate that the suggested eCCA-SSCOR approach significantly improves detection accuracy and ITR compared to four existing state-of-the-art methods: canonical correlation analysis (CCA), filter bank CCA (FBCCA), eCCA, and SSCOR. Our method achieved a high average accuracy for all subjects, with 99.5% for the San Diego dataset, and 98.60% for the Benchmark dataset corresponding to data length of 3.5 s and 5 s, respectively. Furthermore, a high ITR of 212.44 bits/min was achieved with a data duration (\(T_w\)) of 0.75 s using the San Diego dataset and was 238.66 bits/min with a \(T_w\) of 1 s for the Benchmark dataset. These offline results demonstrate that the proposed approach successfully combines eCCA and SSCOR to improve SSVEP decoding. Additionally, it is advantageous for applications based on offline processing and can be adapted for online analysis. Online applications can benefit from these results by providing fast interfaces with a large number of commands.

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Notes

  1. https://www.kaggle.com/datasets/lzyuuu/ssvep-sandiego

  2. http://bci.med.tsinghua.edu.cn/download.html

  3. https://www.geeksforgeeks.org/paired-sample-t-test-in-excel/.

  4. https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/paired-sample-t-test/.

  5. https://users.stat.ufl.edu/~winner/sta3024/chapter14.pdf.

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Acknowledgements

this work was supported by the Ministry of High Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco under the number 14/FSA/2021. The APC is sponsored by IUB Sponsored Research Grant #2021-SETS-07)"

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  1. Mustapha Moufassih, Ousama Tarahi, Said Agounad and Hafida Idrissi Azami have contributed equally to this work.

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  1. Laboratory of Metrology and Information Processing, Physics Department, Faculty of Science, Ibn Zohr University, B.P. 8106, Agadir, Morocco

    Soukaina Hamou, Mustapha Moufassih, Ousama Tarahi, Said Agounad & Hafida Idrissi Azami

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  1. Soukaina Hamou
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Hamou, S., Moufassih, M., Tarahi, O. et al. Hybrid approach: combining eCCA and SSCOR for enhancing SSVEP decoding. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06027-7

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