School of Electronics and Information, Northwestern Polytechnical University, Xi'an, Shaanxi, People's Republic of China.
School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi, People's Republic of China.
J Neural Eng. 2023 Jun 8;20(3). doi: 10.1088/1741-2552/acd95d.
Rapid serial visual presentation (RSVP) based on electroencephalography (EEG) has been widely used in the target detection field, which distinguishes target and non-target by detecting event-related potential (ERP) components. However, the classification performance of the RSVP task is limited by the variability of ERP components, which is a great challenge in developing RSVP for real-life applications.To tackle this issue, a classification framework based on the ERP feature enhancement to offset the negative impact of the variability of ERP components for RSVP task classification named latency detection and EEG reconstruction was proposed in this paper. First, a spatial-temporal similarity measurement approach was proposed for latency detection. Subsequently, we constructed a single-trial EEG signal model containing ERP latency information. Then, according to the latency information detected in the first step, the model can be solved to obtain the corrected ERP signal and realize the enhancement of ERP features. Finally, the EEG signal after ERP enhancement can be processed by most of the existing feature extraction and classification methods of the RSVP task in this framework.Nine subjects were recruited to participate in the RSVP experiment on vehicle detection. Four popular algorithms (spatially weighted Fisher linear discrimination-principal component analysis (PCA), hierarchical discriminant PCA, hierarchical discriminant component analysis, and spatial-temporal hybrid common spatial pattern-PCA) in RSVP-based brain-computer interface for feature extraction were selected to verify the performance of our proposed framework. Experimental results showed that our proposed framework significantly outperforms the conventional classification framework in terms of area under curve, balanced accuracy, true positive rate, and false positive rate in four feature extraction methods. Additionally, statistical results showed that our proposed framework enables better performance with fewer training samples, channel numbers, and shorter temporal window sizes.As a result, the classification performance of the RSVP task was significantly improved by using our proposed framework. Our proposed classification framework will significantly promote the practical application of the RSVP task.
基于脑电的快速序列视觉呈现 (RSVP) 在目标检测领域得到了广泛应用，它通过检测事件相关电位 (ERP) 成分来区分目标和非目标。然而，RSVP 任务的分类性能受到 ERP 成分可变性的限制，这在开发用于实际应用的 RSVP 时是一个巨大的挑战。为了解决这个问题，本文提出了一种基于 ERP 特征增强的分类框架，以抵消 ERP 成分可变性对 RSVP 任务分类的负面影响，该框架名为潜伏期检测和 EEG 重建。首先，提出了一种用于潜伏期检测的时空相似性测量方法。随后，我们构建了一个包含 ERP 潜伏期信息的单试 EEG 信号模型。然后，根据第一步中检测到的潜伏期信息，可以求解模型，得到校正后的 ERP 信号，并实现 ERP 特征的增强。最后，在这个框架中，可以使用现有的大多数 RSVP 任务的 EEG 信号特征提取和分类方法对增强后的 ERP 信号进行处理。
招募了 9 名受试者参与车辆检测的 RSVP 实验。选择了四种基于 RSVP 的脑机接口中常用的特征提取算法（空间加权 Fisher 线性判别 - PCA、层次判别 PCA、层次判别成分分析和时空混合共空间模式 - PCA）来验证我们提出的框架的性能。实验结果表明，在四种特征提取方法中，我们提出的框架在曲线下面积、平衡准确率、真阳性率和假阳性率方面明显优于传统的分类框架。此外，统计结果表明，我们提出的框架在使用较少的训练样本、通道数量和较短的时间窗口时可以获得更好的性能。
因此，使用我们提出的框架显著提高了 RSVP 任务的分类性能。我们提出的分类框架将极大地促进 RSVP 任务的实际应用。
