Li Yilin, Sommer Werner, Tian Liang, Zhou Changsong
Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China.
Institute of Interdisciplinary Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China.
Cogn Neurodyn. 2024 Dec;18(6):4055-4069. doi: 10.1007/s11571-024-10181-2. Epub 2024 Oct 21.
Data-driven strategies have been widely used to distinguish experimental effects on single-trial EEG signals. However, how latency variability, such as within-condition jitter or latency shifts between conditions, affects the performance of EEG classifiers has not been well investigated. Without explicitly considering and disentangling such attributes of single trials, neural network-based classifiers have limitations in measuring their contributions. Inspired by domain knowledge of subcomponent latency and amplitude from traditional cognitive neuroscience, this study applies a stepwise latency correction method on single trials to control for their contributions to classifier behavior. As a case study demonstrating the value of this method, we measure repetition priming effects of faces, which induce large reaction time differences, latency shifts, and amplitude effects in averaged event-related potentials. The results show that within-condition jitter negatively impacts classifier performance, but between-condition latency shifts improve accuracy, whereas genuine amplitude differences have no significant influence. While demonstrated in the case of priming effects, this methodology can be generalized to experiments involving many kinds of time-varying signals to account for the contributions of latency variability to classifier performance.
The online version contains supplementary material available at 10.1007/s11571-024-10181-2.
数据驱动策略已被广泛用于区分对单次试验脑电图（EEG）信号的实验效应。然而，诸如条件内抖动或条件间潜伏期偏移等潜伏期变异性如何影响EEG分类器的性能尚未得到充分研究。在没有明确考虑和区分单次试验的这些属性的情况下，基于神经网络的分类器在衡量它们的贡献方面存在局限性。受传统认知神经科学中关于子成分潜伏期和振幅的领域知识启发，本研究对单次试验应用逐步潜伏期校正方法，以控制它们对分类器行为的贡献。作为证明该方法价值的一个案例研究，我们测量了面孔的重复启动效应，其在平均事件相关电位中会引起较大的反应时间差异、潜伏期偏移和振幅效应。结果表明，条件内抖动对分类器性能有负面影响，但条件间潜伏期偏移提高了准确率，而真正的振幅差异没有显著影响。虽然在启动效应的案例中得到了证明，但这种方法可以推广到涉及多种时变信号的实验中，以考虑潜伏期变异性对分类器性能的贡献。
在线版本包含可在10.1007/s11571-024-10181-2获取的补充材料。
