# Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
## 1 Introduction
selected non-linear features including complexity (Cx), Higuchi fractal dimension (HFD), and Lyapunov exponent and the fusion of these features using five different combination rules (R1: summation, R2: product, R3: division, R4: weighted sum using F-values, and R5: weighted sum using information gain ratio (IGR) rules) for 19 EEG electrodes. Using the probabilistic neural network (PNN) classifier and the R3 rule features, they achieved a classification performance of 100%. [PERSON] et al. [24] investigated whether abnormalities in microstates, quasi-stable electrical fields in the EEG data, can be used to classify SCZs and HCs. They used four microstates (microstate A: auditory network; microstate B: visual network; microstate C: salience network; and microstate D: fronto-parietal network) and obtained 24 features including basic microstate features (microstate average duration, occurrence per second, full coverage of the time, 4 \(\times\) 3 = 12 features) and the microstates transition probabilities (12 features). Using 14 out of 24 features that demonstrate significant differences between SCZ and HC and SVM classifier, they yield 82.7% accuracy for classifying 70 SCZs and 75 HCs. [PERSON] et al. [25] recruited 119 SCZs and 119 HCs in their study. They obtained the source-level cortical FC network, where minimum norm estimation (MNE) was used to estimate the time series of source activity and the phase-locking value was used for calculating FC. Values of the clustering coefficient (CC) and path length for the cortical functional network were then used as selected features. Using the linear discriminant analysis (LDA) classifier, the best classification performance was 80.66% by choosing 27 optimal features. \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. (2018) [16] & 1500 & SVM & 91.16\% & 40 SCZs and 40 HCs \\ [PERSON] et al. (2019) [17] & 2730 & MDC-CNN & 93.06\% & 45 SCZs and 39 HCs \\ [PERSON] et al. (2019) [18] & 4 & SVM & 88.10\% & 19 SCZs and 23 HCs \\ [PERSON] et al. (2019) [19] & - & CNN & 81.26\% & 14 SCZs and 14 HCs \\ [PERSON] et al. (2019) [20] & 14 & SVM & 92.91\% & 14 SCZs and 14 HCs \\ [PERSON] et al. (2020) [21] & 200 & RF & 96.77\% & 14 SCZs and 14 HCs \\ [PERSON] et al. (2020) [22] & 21 & RF & 89.29\% & 14 SCZ and 14 HCs \\ [PERSON] et al. & & & & \\ (2020) [23] & 19 & PNN & 100\% & 14 SCZs and 14 HCs \\ [PERSON] et al. (2020) [24] & 14 & SVM & 82.7\% & 70 SCZs and 75 HCs \\ [PERSON] et al. (2020) [25] & 27 & LDA & 80.66\% & 119 SCZs and 119 HCs \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)In eight of these studies, a small dataset of SCZs and HCs was analyzed [11; 13; 18; 19; 20; 21; 22; 23], which limits the power and applicability of the MLAs and deep learning algorithms. Using databases with larger data samples would allow having sufficient training data to adjust the model parameters more accurately and therefore increase the generalizability/reliability of the model, i.e., the performance on previously unseen data. Particularly when the selected features display significant variability, a larger training dataset is required to have a reliable classification performance. Furthermore, the sample size (\(N_{s}\)) to the number of features (\(N_{f}\)) ratio in some studies [11; 12; 13; 16; 20; 21; 22; 23] is much lower than the rule of 10:1, or \(N_{f}\) is larger than the square root of \(N_{s}\), which are referred to as the rules of preventing over-fitting (good quality) [24]. Finally, two of these studies [17; 19] used deep learning algorithms, which are more complex compared to traditional MLAs and therefore require more training data to be reliable. Furthermore, complex feature engineering (the process of using domain knowledge to extract features from raw data) increases the difficulty to interpret the model [26].