INSERM, U751, Marseille, France.
J Neurosci Methods. 2011 Aug 15;199(2):273-89. doi: 10.1016/j.jneumeth.2011.04.028. Epub 2011 May 10.
Brain oscillations constitute a prominent feature of electroencephalography (EEG), in both physiological and pathological states. An efficient separation of oscillation from transient signals in EEG is important not only for detection of oscillations, but also for advanced signal processing such as source localization. A major difficulty lies in the fact that filtering transient phenomena can lead to spurious oscillatory activity. Therefore, in the presence of a mixture of transient and oscillatory events, it is not clear to which extent filtering methods are able to separate them efficiently. The objective of this study was to evaluate methods for separating oscillations from transients. We compared three methods: finite impulse response (FIR) filtering, wavelet analysis with stationary wavelet transform (SWT), time-frequency sparse decomposition with Matching Pursuit (MP). We evaluated the quality of reconstruction and the results of automatic detection of oscillations intermingled with transients. The emphasis of our study was on epileptic signals and single channel processing. In both simulations and on real data, FIR performed generally worse than the time-frequency methods. Both SWT and MP showed good results in separation and detection, each method having its advantages and its limitations. The SWT had good results in separation and detection of transients due to the time invariance property, but still did not completely resolve the frequency overlap for the oscillation during the time-frequency thresholding. The MP provides a sparse representation, and gave good results for simulated data. However, in the real data, we observed distortions introduced by the subtractive approach, and departure from dictionary waveforms. Future directions are proposed for overcoming these limitations.
脑电波是脑电图（EEG）中一个显著的特征，无论是在生理状态还是病理状态下都是如此。在 EEG 中，有效地将震荡与瞬态信号分离，不仅对于检测震荡至关重要，而且对于源定位等高级信号处理也很重要。一个主要的难点在于，过滤瞬态现象可能会导致虚假的震荡活动。因此，在瞬态和震荡事件混合存在的情况下，尚不清楚滤波方法在多大程度上能够有效地将它们分离。本研究的目的是评估从瞬态中分离震荡的方法。我们比较了三种方法：有限脉冲响应（FIR）滤波、具有平稳小波变换（SWT）的小波分析和基于匹配追踪（MP）的时频稀疏分解。我们评估了重建质量和自动检测混合瞬态的震荡的结果。我们的研究重点是癫痫信号和单通道处理。在模拟和真实数据中，FIR 通常比时频方法表现更差。SWT 和 MP 在分离和检测方面都取得了良好的效果，每种方法都有其优点和局限性。SWT 由于具有时间不变性，因此在分离和检测瞬态方面取得了良好的效果，但在时频阈值处理过程中，仍未完全解决震荡的频率重叠问题。MP 提供了稀疏表示，并且在模拟数据中取得了良好的效果。然而，在真实数据中，我们观察到了减法方法引入的失真以及与字典波形的偏离。提出了未来克服这些限制的方向。
