Towards correlation-based time window selection method for motor imagery BCIs
Towards correlation-based time window selection method for motor imagery BCIs
复制标题
针对运动想象 BCI 的基于相关性的时间窗口选择方法
DOI：
10.1016/j.neunet.2018.02.011
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发表时间：
2018-06-01
期刊：
影响因子：
7.8
通讯作者：
Cichocki, Andrzej
中科院分区：
文献类型：
--
作者：
Feng, Jiankui;Yin, Erwei;Cichocki, Andrzej
提示的开始通常用于启动用于控制基于运动想象（MI）的脑机接口（BCI）系统的特征窗口。然而，在每个参与者的试验中，心肌梗死期间的时间潜伏期是不同的。在基于MI的BCI系统中，固定MI特征的起始时间点会导致系统性能下降。为了解决这个问题，我们提出了一种新的基于关联的时间窗口选择（CTWS）算法。具体而言，基于相关性分析和性能评价，选择了每个类别的优化参考信号。此外，使用相关分析对训练样本和测试样本的时间窗起始点进行调整。最后，利用特征提取和分类算法计算分类精度。在两个数据集上，结果表明，与直接使用特征提取方法相比，CTWS算法显著提高了系统性能。重要的是，与传统的公共空间模式（common spatial pattern， CSP）算法相比，CTWS算法在健康参与者和脑卒中患者数据集上的平均准确率分别提高了16.72%和5.24%。此外，CTWS与Sub-Alpha-Beta Log-Det divergence （Sub-ABLD）算法联合使用时，平均准确率分别提高了7.36%和9.29%。这些发现表明，所提出的CTWS算法有望作为基于mi的bci的通用特征提取方法。(c) 2018 Elsevier Ltd.版权所有。
The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs. (c) 2018 Elsevier Ltd. All rights reserved.