loading page

Research on SSVEP-EEG feature enhancement Algorithm based on fractional differentiation
  • +3
  • zenghui Li,
  • Wei Wang,
  • Saijie Yuan,
  • Qianqian yang,
  • Junpeng Pei,
  • Yousong Wang
zenghui Li
University of Shanghai for Science and Technology
Author Profile
Wei Wang
Naval Center for Distinctive Medicine

Corresponding Author:[email protected]

Author Profile
Saijie Yuan
University of Shanghai for Science and Technology
Author Profile
Qianqian yang
University of Shanghai for Science and Technology
Author Profile
Junpeng Pei
University of Shanghai for Science and Technology
Author Profile
Yousong Wang
University of Shanghai for Science and Technology
Author Profile

Abstract

Steady-State Visual Evoked Potentials (SSVEP) have garnered significant attention due to their promising applications in brain-computer interfaces (BCI), medical diagnostics, and several other domains. Enhancing the characteristics of SSVEP signals through intricate signal processing has emerged as a pivotal research focus for more efficient signal extraction. In this work, we introduce a novel layered enhancement algorithm for SSVEP electroencephalogram (SSVEP-EEG) signals based on fractional-order differentiation operators. This innovative approach marries brain signal analysis with image processing methodologies. By utilizing fractional-order differentiation operators in tandem with the Laplace pyramid, the signal undergoes hierarchical enhancement. This amplified signal is then reconstructed, which facilitates an in-depth extraction of image intricacies and attributes, ultimately accentuating the distinctiveness of SSVEP features. To validate the efficacy of the proposed method, we applied it to three recognized target identification algorithms: Canonical Correlation Analysis (CCA), Filter Bank Canonical Correlation Analysis (FBCCA), and Task-Related Component Analysis (TRCA) using a publicly available dataset. Experimental outcomes underscore that, in contrast to contemporary techniques, our proposed algorithm not only effectively attenuates the trend components of SSVEP signals but also substantially elevates the recognition precision of CCA, FBCCA, and TRCA.
31 Aug 2023Submitted to The Journal of Engineering
01 Sep 2023Assigned to Editor
01 Sep 2023Submission Checks Completed
14 Sep 2023Reviewer(s) Assigned
07 Oct 2023Review(s) Completed, Editorial Evaluation Pending
16 Oct 2023Editorial Decision: Revise Major