Raz J, Dickerson L, Turetsky B
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, USA.
Brain Lang. 1999 Jan;66(1):61-88. doi: 10.1006/brln.1998.2025.
The standard methods for decomposition and analysis of evoked potentials are bandpass filtering, identification of peak amplitudes and latencies, and principal component analysis (PCA). We discuss the limitations of these and other approaches and introduce wavelet packet analysis. Then we propose the "single-channel wavelet packet model," a new approach in which a unique decomposition is achieved using prior time-frequency information and differences in the responses of the components to changes in experimental conditions. Orthogonal sets of wavelet packets allow a parsimonious time-frequency representation of the components. The method allows energy in some wavelet packets to be shared among two or more components, so the components are not necessarily orthogonal. The single-channel wavelet packet model and PCA both require constraints to achieve a unique decomposition. In PCA, however, the constraints are defined by mathematical convenience and may be unrealistic. In the single-channel wavelet packet model, the constraints are based on prior scientific knowledge. We give an application of the method to auditory evoked potentials recorded from cats. The good frequency resolution of wavelet packets allows us to separate superimposed components in these data. Our present approach yields estimates of component waveforms and the effects of experiment conditions on the amplitude of the components. We discuss future extensions that will provide confidence intervals and p values, allow for latency changes, and represent multichannel data.
诱发电位分解与分析的标准方法包括带通滤波、峰值幅度和潜伏期识别以及主成分分析（PCA）。我们讨论了这些方法及其他方法的局限性，并介绍了小波包分析。然后我们提出了“单通道小波包模型”，这是一种新方法，通过使用先验时频信息以及各成分对实验条件变化的响应差异来实现独特的分解。小波包的正交集允许对各成分进行简洁的时频表示。该方法允许某些小波包中的能量在两个或更多成分之间共享，因此这些成分不一定是正交的。单通道小波包模型和PCA都需要约束条件来实现独特的分解。然而，在PCA中，约束条件是出于数学便利性而定义的，可能不切实际。在单通道小波包模型中，约束条件基于先验科学知识。我们给出了该方法在猫记录的听觉诱发电位中的应用。小波包良好的频率分辨率使我们能够分离这些数据中的叠加成分。我们目前的方法可以得到成分波形的估计以及实验条件对成分幅度的影响。我们讨论了未来的扩展，这些扩展将提供置信区间和p值，考虑潜伏期变化，并表示多通道数据。
