# Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
## 1 Introduction
[16] applied MLA to the resting-state EEG data of 40 clinically high-risk individuals (CHRs), 40 SCZs, and 40 HCs to investigate whether the EEG characteristics of these three groups can differentiate CHRs and SCZs from each other and from HCs. Using von Neumann Entropy as the linear eigenvalue statistics (LES) feature for each window of 200 EEG data samples (1500 features in total for 300,000 EEG data samples), they showed that the SVM classifier achieved the highest classification performance of 91.16% for classifying SCZs from HCs and 73.31% for classifying the three classes of CHRs, SCZs, and HCs. [PERSON] et al. [17] proposed a deep multi-domain connectome convolutional neural network (MDC-CNN) framework for classifying the resting-state EEG-derived brain connectome in SCZs and HCs. By combining three connectivity features of (1) time-domain vector autoregressive (VAR) model coefficients; (2) the frequency-domain partial directed coherence (PDC); and (3) the network topology-based complex network (CN) measures (2730 features), they achieved the classification performance of 93.06% for classifying 45 SCZs and 39 HCs. [PERSON] et al. [18] used the inherent spatial pattern of the network (SPN) features extracted from resting-state EEG data to classify 19 SCZs and 23 HCs. Using four SPN filters, they achieved the highest accuracy of 88.10% with the SVM classifier. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Article**} & **\# of** & \multirow{2}{*}{**Classifier**} & **Best** & \multirow{2}{*}{**EEG Dataset**} \\  & **Features** & & **Accuracy** & \\ \hline [PERSON] et al. (2009) [11] & 286 & BDLDA & 87.51\% & 13 SCZs and 18 HCs \\ [PERSON] et al. (2010) [12] & 42 & MFA & 87.1\% & 40 SCZs, 64 MDDs, & \\  & & & & and 91 HCs \\ [PERSON] et al. (2011) [13] & 80 & Adaboost & 91.94\% & 20 SCZs and 20 HCs \\ [PERSON] et al. (2017) [15] & 10 & SVM & 80\% & 55 SCZs and 23 HCs \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of most recent and accurate automated frameworks to diagnose schizophrenia using resting-state EEG data. [PERSON] et al. [19] applied an 11-layer CNN model to differentiate resting-state EEGs of 14 SCZs and 14 HCs with deep learning. A total of 1142 EEG segments were used for each subject, where each segment consisted of 6250 time samples and 19 electrodes. Therefore, the total number of sampling points was 1142 \(\times\) 6250 \(\times\) 19 = 135,612,500. The most significant features were then automatically extracted by the CNN. Their proposed model achieved classification accuracies of 81.26%. This dataset (14 SCZs and 14 HCs) has been used in four more studies to diagnose schizophrenia [20; 21; 22; 23]. In [20], [PERSON] et al. first segmented the EEG data for each subject into segments of 25 sec. Therefore, they obtained 516 segments for HC and 626 segments for SCZ. They extracted 157 non-linear features such as the largest Lyapunov exponent, Kolmogorov-Sinai entropy, Hjorth complexity and mobility, Kolmogorov complexity, bispectrum, and permutation entropy. The optimal 14 features were then selected and applied to various classifiers, where the best performance belonged to the SVM classifier with an accuracy of 92.91%. In [21], [PERSON] et al. used 200 power bands within a range of 0.5 Hz each as features that applied to a random forest (RF) classifier. Using 499 1-min samples for all 28 subjects (375 for training and 124 for evaluation), they yield an accuracy of 96.77%. In [22], [PERSON] et al. used 21 dynamic features of dynamic FC (DFC) at delta frequency bands (0.5-4 Hz) such as its entropy and multifractal properties to classify the two groups. They achieved a classification performance of 89.29% using an RF classifier. In [23], [PERSON] et al.