基于新型时间滤波器的ssvep检测鲁棒相似度量

IF 10.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE transactions on neural networks and learning systems Pub Date : 2021-10-14 DOI:10.1109/TNNLS.2021.3118468
Jing Jin;Zhiqiang Wang;Ren Xu;Chang Liu;Xingyu Wang;Andrzej Cichocki
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引用次数: 51

摘要

基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)以其训练时间短、识别性能好、信息转译率高等特点受到广泛关注。目前,大多数强大的ssvep检测方法都是基于空间滤波器和Pearson相关系数的相似性度量。其中,基于任务相关成分分析(task-related component analysis, TRCA)的方法及其变体——基于集成的任务相关成分分析(ensemble TRCA, eTRCA)方法是两种具有较高性能和巨大潜力的方法。然而,它们有一个缺陷,那就是它们只能抑制某些类型的噪声,而不能抑制更一般的噪声。为了解决这一问题,在基于trca方法的目标函数中引入时间局部加权,并利用奇异值分解设计了一种新的时间滤波器。在此基础上,提出了基于时间滤波器和(e) trca的相似性度量方法,可以进行鲁棒的相似性度量,增强ssvep的检测能力。使用来自35名受试者的基准数据集来评估所提出的方法,并将其与(e)基于trca的方法进行比较。结果表明,该方法的性能明显优于(e)基于trca的方法。因此,我们认为所提出的时间滤波器和相似性度量方法在ssvep检测中具有很大的潜力。
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Robust Similarity Measurement Based on a Novel Time Filter for SSVEPs Detection
The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has received extensive attention in research for the less training time, excellent recognition performance, and high information translate rate. At present, most of the powerful SSVEPs detection methods are similarity measurements based on spatial filters and Pearson’s correlation coefficient. Among them, the task-related component analysis (TRCA)-based method and its variant, the ensemble TRCA (eTRCA)-based method, are two methods with high performance and great potential. However, they have a defect, that is, they can only suppress certain kinds of noise, but not more general noises. To solve this problem, a novel time filter was designed by introducing the temporally local weighting into the objective function of the TRCA-based method and using the singular value decomposition. Based on this, the time filter and (e)TRCA-based similarity measurement methods were proposed, which can perform a robust similarity measure to enhance the detection ability of SSVEPs. A benchmark dataset recorded from 35 subjects was used to evaluate the proposed methods and compare them with the (e)TRCA-based methods. The results indicated that the proposed methods performed significantly better than the (e)TRCA-based methods. Therefore, it is believed that the proposed time filter and the similarity measurement methods have promising potential for SSVEPs detection.
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来源期刊
IEEE transactions on neural networks and learning systems
IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
CiteScore
23.80
自引率
9.60%
发文量
2102
审稿时长
3-8 weeks
期刊介绍: The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.
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