Detection of Target Frequency from Multichannel SSVEP-Based BCI System using a Combined Approach of BSS-CCA
| International Journal of Electrical and Electronics Engineering |
| © 2025 by SSRG - IJEEE Journal |
| Volume 12 Issue 6 |
| Year of Publication : 2025 |
| Authors : Mukesh Kumar Ojha, Manoj Kumar Mukul |
10.14445/23488379/IJEEE-V12I6P103
How to Cite?
Mukesh Kumar Ojha, Manoj Kumar Mukul, "Detection of Target Frequency from Multichannel SSVEP-Based BCI System using a Combined Approach of BSS-CCA," SSRG International Journal of Electrical and Electronics Engineering, vol. 12, no. 6, pp. 25-34, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I6P103
Abstract:
This paper demonstrates the combined approach of Blind Source Separation (BSS) and Canonical Correlation Analysis (CCA) to detect the frequency component of a Steady-State Visual Evoked Potential (SSVEP). Accurate detection of the SSVEP frequency component is the most challenging task for developing the SSVEP-based brain-computer interface (BCI) system. Canonical Correlation Analysis (CCA) is the most widely and rigorously employed method to detect the SSVEP frequency component from multichannel recorded Electroencephalogram (EEG) signals. However, spontaneous EEG signals and artifacts often occurring while recording scalp-based EEG signals may deteriorate the detection accuracy of the SSVEP frequency component from the recorded EEG signal. This work investigates the BSS as a pre-processing technique to decorrelate the source signal (SSVEP) from the recorded mixed-signal (EEG) to improve the detection accuracy of the SSVEP-based BCI Inference system. This paper proposes second-order statistics-based BSS AMUSE algorithms as pre-processing methods for multichannel EEG signals. The CCA technique employs the pre-processed signal to detect the SSVEP frequency components from the recorded EEG signal. The obtained finding indicates that the proposed BSS-CCA method significantly improved the SSVEP detection accuracy compared to the standard CCA method. The authors have also observed that the selection of stimulus frequency also plays a vital role in improving the detection accuracy of the SSVEP BCI system. The analysis indicates that average detection accuracy is much higher when stimulus frequency is in the range of the alpha band (8Hz – 16Hz) compared to stimulus frequency beyond the alpha band (above 16Hz) using both CCA and BSS-CCA approaches.
Keywords:
Brain-Computer Inference (BCI), Blind Source Separation (BSS), Canonical Correlation Analysis (CCA), Electroencephalography (EEG), Steady-State Visual Evoked Potential (SSVEP).
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