# Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
## 1 Introduction  
Schizophrenia is a severe neuropsychiatric disorder affecting approximately 20 million people worldwide according to the World Health Organization (WHO) report [1, 2]. Schizophrenia is characterized by noticeable psychotic symptoms including hallucinations, delusions, reduction in performance, and thought disorder. Based on neuroimaging evidence on structural, functional, and effective brain connectivity, a core deficit of schizophrenia can be proposed as the failure of effective functional integration within and between brain areas [3]. Several studies proved alterations in functional connectivity (FC) in patients with schizophrenia (SCZs) in comparison to healthy controls (HCs) in response to external cognitive or sensorimotor stimulation [4, 5, 6]. However, resting-state electroencephalography (EEG) FC reflects the intrinsic inter-neuronal connections in specific circuits such as the default mode network (DMN) that are attenuated or interrupted during cognitive or sensorimotor tasks [7]. Therefore, investigating resting-state brain connectivity may reveal an intrinsic functional disintegration of brain regions for SCZ. Machine learning algorithms (MLAs) have been widely used in applications related to neuroscience and psychiatry (e.g., [8, 9, 10, 6]). Recently, there has been an increasing number of studies that use MLAs to diagnose schizophrenia based on resting-state EEG patterns. In Table 1, we highlighted the outcomes of the fourteen most recent studies in this area with the highest classification accuracy. [PERSON] et al. [11] extracted several features including bandpower, autoregressive (AR) model parameters, and a fractal dimension from the recorded resting-state EEG data (286 features). They applied different classifiers to extracted features to classify the two groups of 13 SCZs and 18 HCs. They achieved the highest classification accuracy of 87.51% using a boosted version of direct linear discriminant analysis (BDLDA). [PERSON] et al. [12] used various statistical quantities such as the spectral coherence between all electrode pairs, the absolute and relative power spectral densities (PSDs), the left-to-right hemisphere power ratios, the anterior-to-posterior power ratios, and mutual information between all electrode pairs at the frequency range of 4-36 Hz with 1 Hz resolution to classify 40 SCZs, 64 patients with major depressive disorder (MDD), and 91 HCs. Using 42 most discriminating features and the mixture of factor analysis (MFA) for classification, they achieved the classification accuracy of 87.1%. [PERSON] et al. [13] applied MLA to 20 SCZs and 20 HCs. They first selected the most informative EEG electrodes using mutual information techniques. Several features including autoregressive model parameters, fractal dimension, and band power were used for classification. Using 20 EEG electrodes, the total number of features was 300 for each 1 s window of EEG data, which resulted in 300 \(\times\) 120 = 36,000 features for 2-min recorded data for each participant. Then, they employed genetic programming (GP) to select the best features. GP is a technique of creating algorithms that can program themselves by mimicking natural biological breeding and evolution. GP starts from a population of random programs and lets the machine automatically search among the space of all programs and breed the most successful or suitable ones in new generations [14]. They obtained the highest classification accuracy of 91.94% using the Adaboost classifier with 80 features. [PERSON] et al. [15] considered Shannon entropy and Higuchi's fractal dimension at five lobes of frontal, central, parietal, temporal, and occipital (in total 10 features) as their selected features to compare the complexity of a resting-state EEG signal for SCZ and HC using 55 SCZs and 23 HCs. Using these 10 features, the support vector machine (SVM) classifier obtained the highest accuracy of 80% using 20% of the data in each group for testing. [PERSON] et al.