Chen Xiaoqian, Gupta Resh S, Gupta Lalit
School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University, Carbondale, IL 62901, USA.
Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA 92161, USA.
Brain Sci. 2022 Dec 22;13(1):21. doi: 10.3390/brainsci13010021.
Features extracted from the wavelet transform coefficient matrix are widely used in the design of machine learning models to classify event-related potential (ERP) and electroencephalography (EEG) signals in a wide range of brain activity research and clinical studies. This novel study is aimed at dramatically improving the performance of such wavelet-based classifiers by exploiting information offered by the cone of influence (COI) of the continuous wavelet transform (CWT). The COI is a boundary that is superimposed on the wavelet scalogram to delineate the coefficients that are accurate from those that are inaccurate due to edge effects. The features derived from the inaccurate coefficients are, therefore, unreliable. In this study, it is hypothesized that the classifier performance would improve if unreliable features, which are outside the COI, are zeroed out, and the performance would improve even further if those features are cropped out completely. The entire, zeroed out, and cropped scalograms are referred to as the "same" (S)-scalogram, "zeroed out" (Z)-scalogram, and the "valid" (V)-scalogram, respectively. The strategy to validate the hypotheses is to formulate three classification approaches in which the feature vectors are extracted from the (a) S-scalogram in the standard manner, (b) Z-scalogram, and (c) V-scalogram. A subsampling strategy is developed to generate small-sample ERP ensembles to enable customized classifier design for single subjects, and a strategy is developed to select a subset of channels from multiple ERP channels. The three scalogram approaches are implemented using support vector machines, random forests, k-nearest neighbor, multilayer perceptron neural networks, and deep learning convolution neural networks. In order to validate the performance hypotheses, experiments are designed to classify the multi-channel ERPs of five subjects engaged in distinguishing between synonymous and non-synonymous word pairs. The results confirm that the classifiers using the Z-scalogram features outperform those using the S-scalogram features, and the classifiers using the V-scalogram features outperform those using the Z-scalogram features. Most importantly, the relative improvement of the V-scalogram classifiers over the standard S-scalogram classifiers is dramatic. Additionally, enabling the design of customized classifiers for individual subjects is an important contribution to ERP/EEG-based studies and diagnoses of patient-specific disorders.
从小波变换系数矩阵中提取的特征，在机器学习模型设计中被广泛应用，用于在广泛的脑活动研究和临床研究中对事件相关电位（ERP）和脑电图（EEG）信号进行分类。这项新研究旨在通过利用连续小波变换（CWT）的影响锥（COI）所提供的信息，显著提高此类基于小波的分类器的性能。COI是一个叠加在小波尺度图上的边界，用于区分由于边缘效应而准确的系数和不准确的系数。因此，从不准确系数中导出的特征是不可靠的。在本研究中，假设如果将COI之外的不可靠特征清零，分类器性能将会提高，如果将这些特征完全裁剪掉，性能将进一步提高。完整的、清零的和裁剪后的尺度图分别称为“相同”（S）尺度图、“清零”（Z）尺度图和“有效”（V）尺度图。验证假设的策略是制定三种分类方法，其中特征向量分别从（a）标准方式的S尺度图、（b）Z尺度图和（c）V尺度图中提取。开发了一种子采样策略来生成小样本ERP集合，以实现针对单个受试者的定制分类器设计，并开发了一种策略从多个ERP通道中选择一个通道子集。使用支持向量机、随机森林、k近邻、多层感知器神经网络和深度学习卷积神经网络来实现三种尺度图方法。为了验证性能假设，设计了实验来对五名区分同义词和非同义词对的受试者的多通道ERP进行分类。结果证实，使用Z尺度图特征的分类器优于使用S尺度图特征的分类器，使用V尺度图特征的分类器优于使用Z尺度图特征的分类器。最重要的是，V尺度图分类器相对于标准S尺度图分类器的相对改进非常显著。此外，能够为个体受试者设计定制分类器，对基于ERP/EEG的患者特定疾病研究和诊断做出了重要贡献。
