Abstract
AbstractSteady‐state visual evoked potentials (SSVEP), significant in brain‐computer interfaces (BCI) and medical diagnostics, benefit from enhanced signal processing for improved analysis and interpretation. This study introduces a novel enhancement algorithm for SSVEP electroencephalogram (EEG) signals, employing fractional‐order differentiation operators combined with image processing techniques. Utilizing fractional‐order differentiation within a Laplace pyramid framework, the algorithm achieves hierarchical signal enhancement, facilitating detailed feature extraction and emphasizing SSVEP signal characteristics. This innovative approach merges the precision of fractional calculus with the structural benefits of the Laplace pyramid, leading to enhanced signal clarity and feature discrimination. The efficacy of this method was validated using canonical correlation analysis (CCA), filter bank CCA (FBCCA), and task‐related component analysis (TRCA) on a public dataset. Compared to conventional methods, our algorithm not only mitigates trend components in SSVEP signals but also significantly boosts the recognition accuracy of CCA, FBCCA, and TRCA algorithms. Experimental results indicate a marked improvement in recognition precision, underscoring the algorithm's potential to advance SSVEP‐based BCI research.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.