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<p>This study sought to enhance visual acuity assessment using steady-state visual evoked potentials (SSVEPs) through subject-specific training methods. SSVEPs were elicited from eleven subjects using the vertical sinusoidal gratings at six various spatial frequency steps, and then the classical approach of Oz single-channel, the spatial filtering method of canonical correlation analysis (CCA), and five subject-specific training methods, i.e., individual template-based canonical correlation analysis (IT-CCA), multi-way canonical correlation analysis (MwayCCA), multi-set canonical correlation analysis (MsetCCA), task-related component analysis (TRCA), and correlated component analysis (CORCA), were used as preprocessed methods for six-channel SSVEP signals. Subsequently, by comparing the SSVEP response characteristics, MwayCCA and TRCA were selected for further processing with Oz-channel and CCA as the controls. After carrying out the SSVEP visual acuity estimation criterion, Bland-Altman analysis showed an agreement of 0.201, 0.195, 0.188, and 0.196 logMAR between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and the objective SSVEP visual acuity for Oz-channel, CCA, MwayCCA, and TRCA, respectively, demonstrating that the subject-specific training method of MwayCCA showed the optimal performance in SSVEP-based visual acuity assessment. This study demonstrated that subject-specific training methods enhance SSVEP-based visual acuity assessment and recommended MwayCCA as the preferred approach for signal preprocessing in such assessments.</p>

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Enhancing performance of SSVEP-based visual acuity via exploiting subject-specific information

  • 期刊论文

  • 发表日期：

    2025年9月6日

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  • Xiaowei Zheng,
  • Boyu Wen,
  • Xin Yan,
  • Guanghua Xu,
  • Rui Zhang

摘要

This study sought to enhance visual acuity assessment using steady-state visual evoked potentials (SSVEPs) through subject-specific training methods. SSVEPs were elicited from eleven subjects using the vertical sinusoidal gratings at six various spatial frequency steps, and then the classical approach of Oz single-channel, the spatial filtering method of canonical correlation analysis (CCA), and five subject-specific training methods, i.e., individual template-based canonical correlation analysis (IT-CCA), multi-way canonical correlation analysis (MwayCCA), multi-set canonical correlation analysis (MsetCCA), task-related component analysis (TRCA), and correlated component analysis (CORCA), were used as preprocessed methods for six-channel SSVEP signals. Subsequently, by comparing the SSVEP response characteristics, MwayCCA and TRCA were selected for further processing with Oz-channel and CCA as the controls. After carrying out the SSVEP visual acuity estimation criterion, Bland-Altman analysis showed an agreement of 0.201, 0.195, 0.188, and 0.196 logMAR between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and the objective SSVEP visual acuity for Oz-channel, CCA, MwayCCA, and TRCA, respectively, demonstrating that the subject-specific training method of MwayCCA showed the optimal performance in SSVEP-based visual acuity assessment. This study demonstrated that subject-specific training methods enhance SSVEP-based visual acuity assessment and recommended MwayCCA as the preferred approach for signal preprocessing in such assessments.