(a) (b) (c)

FIGURE 6 Recognition accuracy of CCA(a), FBCCA(b), and TRCA(c).

Figure 6b unfurls the recognition accuracy comparison between the SF_FBCCA methodology, as introduced by Yan et al. [5], and the ERLLP_FBCCA approach of the present study. Drawing from the findings of [15], the demarcated corner frequencies for the passband and stopband for the initial group are registered at [6, 90] Hz, for the second assembly at [14, 90] Hz, and for the third cluster at [22, 90] Hz. As is manifest in Figure 6b, when measuring over stimulus durations spanning 1, 1.2, 1.4, 1.6, 1.8, and 2 seconds, the ERLLP_FBCCA methodology manifests recognition accuracy enhancements of 2.07%, 1.17%, 2.23%, 1.65%, 2.03%, and 1.88%, contrasted against the SF_FBCCA paradigm.

Figure 6c portrays the recognition accuracy contrasts between the SF_TRCA approach, as articulated by Yan et al. [5], and the ERLLP_TRCA methodology elucidated in this manuscript. The data scrutiny ensues over intervals from 0.5 to 0.8 seconds, incremented by 0.1 seconds. In gauging the recognition accuracy for individual subjects, an exhaustive leave-one-out cross-validation methodology was harnessed, leveraging five from the sextet of blocks for indoctrination and sequentially probing the recognition efficacy on the solitary sixth block.

As illuminated in Figure 6c, over stimulus durations of 0.5, 0.6, 0.7, and 0.8 seconds, the recognition accuracy of ERLLP_TRCA method witnesses surges of 2.40%, 2.82%, 1.02%, and 0.98%, respectively, in juxtaposition with the foundational SF-TRCA protocol.

In summation, through the judicious integration of the ERLLP technique, which exhibits a proficient capability for SSVEP signal enhancement, with established methodologies such as CCA, FBCCA, and TRCA, we have witnessed a notable elevation in the recognition accuracy metrics. This convergence elucidates the synergistic potential of combining advanced signal enhancement with classical recognition techniques in the realm of neuroscientific research.

| DISCUSSION AND CONCLUSION

In this study, we have presented a novel approach for enhancing the analysis of steady-state visual evoked potentials (SSVEP) signals. Our method, termed Enhanced-RL based on Laplacian pyramid (ERLLP), combines the benefits of fractional-order differentiation and Laplace pyramid to enhance the features of SSVEP signals. We have demonstrated the effectiveness of ERLLP in improving the recognition accuracy of SSVEP target identification, using three well-known algorithms: Canonical Correlation Analysis (CCA), Filter Bank Canonical Correlation Analysis (FBCCA), and Task-Related Component Analysis (TRCA).

Our results indicate that the application of ERLLP leads to a significant enhancement of SSVEP signal analysis. By leveraging fractional-order differentiation, we successfully enhance the fine details of SSVEP signals, effectively mitigating the influence of trends and noise. Importantly, ERLLP consistently improves recognition accuracy across various stimulus durations for CCA, FBCCA, and TRCA algorithms. The significance of the ERLLP approach extends beyond improved recognition accuracy. The integration of image processing techniques and hierarchical enhancement introduces a novel perspective in brain signal analysis. Through the combination of fractional-order differentiation and pyramid structures, we achieve effective noise reduction and trend elimination in SSVEP signals. This novel approach not only advances SSVEP target identification but also offers promising applications in the broader field of brain-computer interface research. Despite the promising outcomes, it is important to acknowledge challenges and potential refinements associated with the ERLLP approach. Parameters such as the fractional-order differentiation order and the number of pyramid levels warrant further optimization and investigation for optimal performance. Additionally, the algorithm’s robustness across individual variations and real-world scenarios requires further exploration.

In conclusion, the ERLLP algorithm presented in this study introduces innovative strategies for SSVEP signal analysis. By employing image processing and hierarchical enhancement techniques, we enhance signal quality and recognition accuracy. Future research can explore the adaptability and applicability of this approach in diverse brain signal analysis tasks. Moreover, the optimization of algorithm parameters and its extension to various real-world scenarios hold potential for further advancements in brain-computer interface research.