Enhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods Pub Date : 2024-11-20 DOI:10.1016/j.jneumeth.2024.110325

Guoxian Xia, Li Wang, Shiming Xiong, Jiaxian Deng

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引用次数: 0

Abstract

Background

In recent years, spatial filter-based frequency recognition methods have become popular in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. However, these methods are ineffective in suppressing local noise, and they rely on the length of the data. In practical applications, enhancing recognition performance with short data windows is a significant challenge for the BCI systems.

New method

With extracting temporal information and eliminating local noise, a temporally local canonical correlation analysis based on training data-driven (TI-tdCCA) method is proposed to enhance the recognition performance of SSVEPs. Based on a novel framework, the filters are derived by incorporating the Laplacian matrix through the use of TI-CCA between the concatenated training data and individual templates. The target frequency is subsequently determined by applying the appropriate spatial filters and Laplacian matrix.

Results

The experimental results on two datasets, consisting of 40 classes and recording from 35 and 70 subjects respectively, demonstrate that the proposed method consistently outperforms the eight competing methods in the majority of cases. The proposed method is simultaneously evaluated by an extended version that incorporates artificial reference signals. The extended method demonstrates a significant improvement over the proposed method. Specifically, with a time window of 0.7 s, the average recognition accuracy of the subjects increases by 10.71 % on the Benchmark dataset and by 6.98 % on the BETA dataset, respectively.

Comparison with existing methods

Our extended method outperforms the state-of-the-art methods by at least 3 %, and it effectively suppresses local noise and maintains excellent scalability.

Conclusions for research articles

The proposed method can effectively combine spatial and temporal filters to improve the recognition performance of SSVEPs.

查看原文本刊更多论文

通过新颖的时间局部典型相关性分析，增强基于 SSVEP 的 BCI 检测。

背景：近年来，基于空间滤波器的频率识别方法在基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）系统中大受欢迎。然而，这些方法在抑制局部噪声方面效果不佳，而且依赖于数据的长度。在实际应用中，利用短数据窗口提高识别性能是 BCI 系统面临的一个重大挑战：新方法：通过提取时间信息和消除局部噪声，提出了一种基于训练数据驱动的时间局部典型相关分析（TI-tdCCA）方法，以提高 SSVEPs 的识别性能。基于新颖的框架，滤波器是通过将拉普拉斯矩阵纳入串联训练数据和单个模板之间的 TI-CCA 得出的。随后，通过应用适当的空间滤波器和拉普拉斯矩阵确定目标频率：两个数据集分别包含 40 个类别和来自 35 和 70 个受试者的记录，实验结果表明，在大多数情况下，所提出的方法始终优于八种竞争方法。同时，还对包含人工参考信号的扩展版方法进行了评估。扩展版方法比建议的方法有显著改进。具体来说，在时间窗口为 0.7s 的情况下，受试者在基准数据集上的平均识别准确率提高了 10.71%，在 BETA 数据集上的平均识别准确率提高了 6.98%：与现有方法的比较：我们的扩展方法比最先进的方法优越至少 3%，它有效地抑制了局部噪声，并保持了出色的可扩展性：提出的方法可以有效地结合空间和时间滤波器，提高 SSVEPs 的识别性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Neuroscience Methods 医学-神经科学

CiteScore

7.10

自引率

3.30%

发文量

226

审稿时长

52 days

期刊介绍： The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.