School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, People's Republic of China.
Author to whom any correspondence should be addressed.
J Neural Eng. 2021 Feb 5;18(1). doi: 10.1088/1741-2552/abc8d5.
Brain-computer interface (BCI) systemsdirectly translate human intentions to instructions for machines by decoding the neural signals. The rapid serial visual presentation (RSVP) task is a typical paradigm of BCIs, in which subjects can detect the targets in the high-speed serial images. There are still two main challenges in electroencephalography (EEG) classification for RSVP tasks: inter-trial variability of event-related potentials (ERPs) and limited trial number of EEG training data.This study proposed an algorithm of discriminant analysis and classification for interval ERPs (DACIE) in RSVP tasks. Firstly, an interval model of ERPs was exploited to solve the inter-trial variability problem. Secondly, a spatial structured sparsity regularization was utilized to reinforce the important channels, which provided a spatial region of interest (sROI). Meanwhile, a temporal auto-weighting technique was conducted to emphasize the important discriminant components, which obtained a temporal regions of interest (tROIs). Thirdly, classification features were obtained by the discriminant eigenvalue analysis to avoid the ill-conditioned estimation of covariance matrix caused by fewer training trials.EEG datasets of 12 subjects in RSVP tasks were analyzed to evaluate the classification performance of proposed algorithm. The average accuracy rate, true positive rate, false positive rate and AUC value are 96.9%, 81.6%, 2.8% and 0.938, respectively. Compared with several state-of-the-art algorithms, the proposed algorithm can provide significantly better classification performance.The interval model of ERPs was exploited in a spatial linear discriminant framework to overcome the inter-trial variability. The sROIs and tROIs were explored to reinforce the pivotal channels and temporal components. And the proposed algorithm can provide good performance with fewer training trials.
脑机接口 (BCI) 系统通过解码神经信号，直接将人类意图转化为机器指令。快速序列视觉呈现 (RSVP) 任务是 BCI 的典型范例，在该任务中，被试可以在高速串行图像中检测到目标。在 RSVP 任务的脑电图 (EEG) 分类中仍然存在两个主要挑战：事件相关电位 (ERP) 的试验间可变性和 EEG 训练数据的有限试验次数。本研究提出了一种用于 RSVP 任务中间隔 ERP 的判别分析和分类算法 (DACIE)。首先，利用 ERP 的间隔模型解决试验间可变性问题。其次，利用空间结构稀疏正则化来增强重要通道，提供感兴趣的空间区域 (sROI)。同时，采用时间自动加权技术来强调重要的判别分量，获得感兴趣的时间区域 (tROIs)。然后，通过判别特征值分析获得分类特征，以避免由于训练试验次数较少而导致协方差矩阵的不适定估计。分析了 12 名被试在 RSVP 任务中的 EEG 数据集，以评估所提出算法的分类性能。平均准确率、真阳性率、假阳性率和 AUC 值分别为 96.9%、81.6%、2.8%和 0.938。与几种最先进的算法相比，所提出的算法可以提供更好的分类性能。ERP 的间隔模型被用于空间线性判别框架中，以克服试验间可变性。探索了 sROIs 和 tROIs，以增强关键通道和时间分量。并且，该算法可以在较少的训练试验次数下提供良好的性能。
