Faculty of Physics, University of Warsaw, L. Pasteura 5 Street, Warsaw, 02-093, Poland.
Faculty of Psychology, University of Warsaw, Stawki 5/7 Street, Warsaw, 10-587, Poland.
Neuroinformatics. 2022 Oct;20(4):827-862. doi: 10.1007/s12021-022-09575-6. Epub 2022 Mar 14.
In this study, we propose a new algorithm for analysing event-related components observed in EEG signals in psychological experiments. We investigate its capabilities and limitations. The algorithm is based on multivariate matching pursuit and clustering. It is aimed to find patterns in EEG signals which are similar across different experimental conditions, but it allows for variations in amplitude and slight variability in topography. The method proved to yield expected results in numerical simulations. For the real data coming from an emotional categorisation task experiment, we obtained two indications. First, the method can be used as a specific filter that reduces the variability of components, as defined classically, within each experimental condition. Second, equivalent dipoles fitted to items of the activity clusters identified by the algorithm localise in compact brain areas related to the task performed by the subjects across experimental conditions. Thus this activity may be studied as candidates for hypothetical latent components. The proposed algorithm is a promising new tool in ERP studies, which deserves further experimental evaluations.
在这项研究中，我们提出了一种新的算法，用于分析心理实验中 EEG 信号中观察到的事件相关成分。我们研究了它的功能和局限性。该算法基于多元匹配追踪和聚类。其目的是在不同实验条件下找到相似的 EEG 信号模式，但允许幅度变化和轻微的地形变化。该方法在数值模拟中得到了预期的结果。对于来自情感分类任务实验的真实数据，我们得到了两个指示。首先，该方法可以用作特定的滤波器，减少每个实验条件下经典定义的成分的可变性。其次，拟合到算法识别的活动聚类项的等效偶极子定位于与被试在实验条件下执行的任务相关的紧凑脑区。因此，这种活动可以作为假设的潜在成分进行研究。所提出的算法是 ERP 研究中一种有前途的新工具，值得进一步的实验评估。
