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A Comparison of the Analysis of Methods for Feature Extraction and Classification in SSVEP BCIs

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Abstract

Most brain–computer interface (BCI) systems operate on the basis of electroencephalography (EEG) due to their straightforwardness of applications and high temporal resolution of brain signals. One of the most helpful tools in BCI systems is the steady-state visual evoked potential, which is derived from the EEG data. This work evaluates a number of feature extraction-based methods for evaluating Shannon entropy, skewness, kurtosis, mean, and variance. A number of feature selection techniques, including decision trees, principal component analysis (PCA), t tests, and Wilcoxon, are also assessed. Several classification techniques, including the k nearest neighbor, support vector machine, Bayesian classifier, and multilayer perceptron neural network, were contrasted in the decision step. The decision tree and PCA are used to define a relatively new feature selection method that is the base of the present study. Finally, based on the four acquired frequencies as well as which represent the four directions of right, left, up, and down, the greatest percentage of accuracy was 91.39%.

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Availability of Data and Materials

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

    Hoda Heidari

  2. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA

    Zahra Einalou & Mehrdad Dadgostar

  3. Department of Electrical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

    Hamidreza Hosseinzadeh

  4. Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Leila Kalhor

Authors
  1. Hoda Heidari
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  2. Zahra Einalou
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  3. Mehrdad Dadgostar
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  4. Hamidreza Hosseinzadeh
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  5. Leila Kalhor
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All authors contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.

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Correspondence to Zahra Einalou.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the Brain Science Institute, Laboratory for Advanced Brain Signal Processing and its later amendments or comparable ethical standards.

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Heidari, H., Einalou, Z., Dadgostar, M. et al. A Comparison of the Analysis of Methods for Feature Extraction and Classification in SSVEP BCIs. SN COMPUT. SCI. 5, 356 (2024). https://doi.org/10.1007/s42979-024-02638-2

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  • DOI: https://doi.org/10.1007/s42979-024-02638-2

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