希尔伯特-黄变换
模式(计算机接口)
频道(广播)
视力
计算机科学
空间滤波器
对比度(视觉)
人工智能
光学
计算机视觉
滤波器(信号处理)
物理
电信
操作系统
作者
Xiaowei Zheng,Xun Zhang,Guanghua Xu,Rui Zhang
出处
期刊:IEEE Transactions on Neural Systems and Rehabilitation Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:31: 4203-4210
被引量:1
标识
DOI:10.1109/tnsre.2023.3323000
摘要
This study aimed to improve the performance of single-channel steady-state visual evoked potential (SSVEP)-based visual acuity assessment by mode decomposition methods.Using the SSVEP dataset induced by the vertical sinusoidal gratings at six spatial frequency steps from 11 subjects, 3-40 Hz band-pass filtering and other four mode decomposition methods, i.e., empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and variational mode decomposition (VMD), were used to preprocess the singlechannel SSVEP signals from Oz electrode.After comparing the SSVEP signal characteristics corresponding to each mode decomposition method, the visual acuity threshold estimation criterion was used to obtain the final visual acuity results.The agreement between subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for band-pass filtering (-0.095 logMAR), EMD (-0.112 logMAR), EEMD (-0.098 logMAR), ICEEMDAN (-0.093 logMAR), and VMD (-0.090 logMAR) was all pretty good, with an acceptable difference between FrACT and SSVEP acuity for band-pass filtering (0.129 logMAR), EMD (0.083 logMAR), EEMD (0.120 logMAR), ICEEMDAN (0.103 log-MAR), and VMD (0.108 logMAR), finding that the visual acuity obtained by these four mode decompositions had a lower limit of agreement and a lower or close difference compared to the traditional band-pass filtering method.This study proved that the mode decomposition methods can enhance the performance of singlechannel SSVEP-based visual acuity assessment, and also recommended ICEEEMDAN as the mode decomposition method for single-channel electroencephalography (EEG) signal denoising in the SSVEP visual acuity assessment.
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