Department of Mathematics, San Francisco State University, 1600 Holloway Ave., San Francisco, CA 94070, United States.
Institute for Systems Analysis, FRC CSC RAS, Higher School of Economics, Moscow, Russia.
Comput Methods Programs Biomed. 2017 Dec;152:131-139. doi: 10.1016/j.cmpb.2017.09.001. Epub 2017 Sep 9.
A crucial step in a classification of electroencephalogram (EEG) records is the feature selection. The feature selection problem is difficult because of the complex structure of EEG signals. To classify the EEG signals with good accuracy, most of the recently published studies have used high-dimensional feature spaces. Our objective is to create a low-dimensional feature space that enables binary classification of EEG records.
The proposed approach is based on our theory of the ϵ-complexity of continuous functions, which is extended here (see Appendix) to the case of vector functions. This extension permits us to handle multichannel-EEG records. The method consists of two steps. Firstly, we estimate the ϵ-complexity coefficients of the original signal and its finite differences. Secondly, we utilize the random forest (RF) or support vector machine (SVM) classifier.
We demonstrated the performance of our method on simulated data. We also applied it to the problem of classification of multichannel-EEG records related to a group of healthy adolescents (39 subjects) and a group of adolescents with schizophrenia (45 subjects). We found that the random forest classifier provides a superior result. In particular, out-of-bag accuracy in the case of RF was 85.3%. Using 10-fold cross-validation (CV), RF gave an average accuracy of 84.5% on a test set, whereas SVM gave an accuracy of 81.07%. We note that the highest accuracy on CV was 89.3%. To compare our method with the classical approach, we performed classification using the spectral features. In this case, the best performance was achieved using seven-dimensional feature space, with an average accuracy of 83.6%.
We developed a model-free method for binary classification of EEG records. The feature space was reduced to four dimensions. The results obtained indicate the effectiveness of the proposed method.
脑电（EEG）记录分类的关键步骤是特征选择。由于 EEG 信号的复杂结构，特征选择问题具有一定难度。为了实现 EEG 信号的高精度分类，最近发表的大多数研究都使用了高维特征空间。我们的目标是创建一个低维特征空间，以实现 EEG 记录的二进制分类。
所提出的方法基于我们连续函数的ε复杂度理论，该理论在此处（见附录）扩展到了向量函数的情况。这种扩展使我们能够处理多通道 EEG 记录。该方法由两个步骤组成。首先，我们估计原始信号及其有限差分的ε复杂度系数。其次，我们利用随机森林（RF）或支持向量机（SVM）分类器。
我们在模拟数据上演示了我们方法的性能。我们还将其应用于与一组健康青少年（39 名受试者）和一组青少年精神分裂症患者（45 名受试者）相关的多通道 EEG 记录分类问题。我们发现 RF 分类器提供了卓越的结果。特别是，RF 的袋外准确率为 85.3%。使用 10 倍交叉验证（CV），RF 在测试集中的平均准确率为 84.5%，而 SVM 的准确率为 81.07%。我们注意到 CV 的最高准确率为 89.3%。为了将我们的方法与经典方法进行比较，我们使用频谱特征进行分类。在这种情况下，使用七维特征空间可实现最佳性能，平均准确率为 83.6%。
我们开发了一种用于 EEG 记录二进制分类的无模型方法。特征空间减少到四个维度。所得到的结果表明了所提出方法的有效性。
