Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen 361005, China.
Comput Math Methods Med. 2022 Feb 18;2022:6331956. doi: 10.1155/2022/6331956. eCollection 2022.
Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with more information on cognitive activities. In recent years, more and more researchers try to find an effective method to extract single-trial ERPs, because most of the existing methods have poor generalization ability or suffer from strong assumptions about the characteristics of ERPs, resulting in unsatisfactory results under the condition of a very low signal-to-noise ratio. In this paper, an EEG classification-based method for single-trial ERP detection and estimation was proposed. This study used a linear generated EEG model containing templates of ERP local descriptors which include amplitude and latency, and this model can avoid the invalid assumption about ERPs taken by other methods. The purpose of this method is not to recover the whole ERP waveform but to model the amplitude and latency of ERP components. This method afterwards examined the three machine learning models including logistic regression, neural network, and support vector machine in the EEG signal classification for ERP detection and selected the best performed MLPNN model for detection. To get the utmost out of information produced in the classification process, this study also used extra information to propose a new optimization model, with which outperformed detection results were obtained. Performance of the proposed method is evaluated on simulated N170 and real P50 data sets, and the results show that the model is more effective than the Woody filter and the SingleTrialEM algorithm. These results are also consistent with the conclusion of sensory gating, which demonstrated good generalization ability.
事件相关电位 (ERPs) 可以反映大脑的高级思维活动。在 ERP 分析中，通常使用叠加和平均方法来估计 ERPs。然而，单次试验 ERP 估计可以为研究人员提供更多关于认知活动的信息。近年来，越来越多的研究人员试图寻找一种有效的方法来提取单次试验 ERPs，因为大多数现有的方法都存在泛化能力差或对 ERPs 特征的假设很强的问题，导致在信噪比非常低的情况下效果不佳。在本文中，提出了一种基于脑电分类的单次试验 ERP 检测和估计方法。该研究使用了一种包含 ERP 局部描述符模板（包括振幅和潜伏期）的线性生成 EEG 模型，该模型可以避免其他方法对 ERPs 的无效假设。该方法的目的不是恢复整个 ERP 波形，而是对 ERP 成分的振幅和潜伏期进行建模。该方法随后在 EEG 信号分类中检查了包括逻辑回归、神经网络和支持向量机在内的三种机器学习模型，以用于 ERP 检测，并选择性能最佳的 MLPNN 模型进行检测。为了充分利用分类过程中产生的信息，本研究还使用了额外的信息来提出一个新的优化模型，从而获得了更好的检测结果。在所提出的方法的性能评估中，使用了模拟 N170 和真实 P50 数据集，结果表明该模型比 Woody 滤波器和 SingleTrialEM 算法更有效。这些结果也与感觉门控的结论一致，这表明了良好的泛化能力。
