脑电图两种常用参考点的分析
An Analysis of Two Common Reference Points for EEGs
摘要：
临床脑电图（EEG）数据根据许多操作条件（例如，电极的类型和放置、所使用的电气接地的类型）。本研究探讨了两种不同的参考蒙太奇：连接耳（LE）和平均参考（AR）的统计差异。其中每一个都占TUH EEG语料库中数据的大约45%。在这项研究中，我们探讨了这种可变性对机器学习性能的影响。我们比较使用这两个蒙太奇产生的功能的统计特性，并探讨我们的标准隐马尔可夫模型（HMM）的分类系统的性能的影响。我们发现，在LE数据上训练的系统明显优于仅在AR数据上训练的系统（77.2% vs. 61.4%）。我们还证明了在两个数据集上训练的系统的性能有所降低（71.4%对77.2%）。对数据的统计分析表明，应考虑均值、方差和通道归一化。然而，倒谱平均减法未能产生性能的改善，这表明这些统计差异的影响是微妙的。
英文摘要：
Clinical electroencephalographic (EEG) data varies significantly depending on a number of operational conditions (e.g., the type and placement of electrodes, the type of electrical grounding used). This investigation explores the statistical differences present in two different referential montages: Linked Ear (LE) and Averaged Reference (AR). Each of these accounts for approximately 45% of the data in the TUH EEG Corpus. In this study, we explore the impact this variability has on machine learning performance. We compare the statistical properties of features generated using these two montages, and explore the impact of performance on our standard Hidden Markov Model (HMM) based classification system. We show that a system trained on LE data significantly outperforms one trained only on AR data (77.2% vs. 61.4%). We also demonstrate that performance of a system trained on both data sets is somewhat compromised (71.4% vs. 77.2%). A statistical analysis of the data suggests that mean, variance and channel normalization should be considered. However, cepstral mean subtraction failed to produce an improvement in performance, suggesting that the impact of these statistical differences is subtler.
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