Joint time-frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or frequency domain analysis. This representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-frequency data to experimentally relevant components are essential. In this paper, we present a method that reduces the large volume of ERP time-frequency data into a few significant time-frequency parameters. The proposed method is based on applying the widely-used matching pursuit (MP) approach, with a Gabor dictionary, to principal components extracted from the time-frequency domain. The proposed PCA-Gabor decomposition is compared with other time-frequency data reduction methods such as the time-frequency PCA approach alone and standard matching pursuit methods using a Gabor dictionary for both simulated and biological data. The results show that the proposed PCA-Gabor approach performs better than either the PCA alone or the standard MP data reduction methods, by using the smallest amount of ERP data variance to produce the strongest statistical separation between experimental conditions.

中文翻译：

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事件相关电位的时频数据减少：结合主成分分析和匹配追踪。

联合时频表示提供了无法通过单个时域或频域分析获得的事件相关电位 (ERP) 的丰富表示。然而，这种表示是以增加数据量和解释结果表示的难度为代价的。因此，能够将大量时频数据减少到实验相关分量的方法是必不可少的。在本文中，我们提出了一种将大量 ERP 时频数据简化为几个重要时频参数的方法。所提出的方法基于将广泛使用的匹配追踪 (MP) 方法与 Gabor 字典应用于从时频域中提取的主成分。将提出的 PCA-Gabor 分解与其他时频数据简化方法进行比较，例如单独的时频 PCA 方法和使用 Gabor 字典对模拟和生物数据的标准匹配追踪方法。结果表明，所提出的 PCA-Gabor 方法的性能优于单独的 PCA 或标准 M​​P 数据缩减方法，通过使用最小量的 ERP 数据方差来产生实验条件之间最强的统计分离。