# Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
## 1 Introduction
Hence, only three studies [15; 24; 25] meet the optimal properties of a high to very high sample size (\(N_{s}>100\)) and \(N_{f}\) to \(N_{s}\) ratio rules; however, the classification accuracy of these studies is less than 85%. The objective of this study is to develop a new MLA based on effective connectivity (EC) measurements to study distinguishing characteristics between schizophrenics and healthy brains using a small set of selected features. FC reflects the statistical dependencies of signals from different brain regions as typically revealed by cross-correlation, coherency, or phase lag index measures. In contrast, EC measures the causal influences that a neural unit applies over another, which defines the mechanisms of neuronal coupling much more precisely than FC [27]. The method to measure EC must fulfill four criteria to be useful for measuring connectivity between brain areas [28], which are (1) independence from any a priori definitions and models; (2) ability to detect strong non-linear interactions across all levels of the brain function from the mechanism of action potential generation in neurons to psychometric functions; (3) the ability to detect EC even with a wide interaction delay between the two signals, reflecting signal transmission through multiple pathways or over complex axonal networks; and (4) robustness against linear cross-talk between signals. Transfer entropy (TE), a model-free statistic that can measure the directed flow of information between two incidents, accomplishes all four of these criteria and can therefore be considered as a suitable method to measure EC [28; 29]. For these reasons, TE has gained growing application in neurological science for measuring the information exchanges or understanding the EC across data modalities like EEG [28]. Moreover, it has been recently demonstrated that, for Gaussian variables, it can be estimated with linear vector autoregressive models, since it is operationally equivalent to Granger causality [30]. In this form, it has been used for the estimation of EC on real EEG data [31]. Various methods are available to estimate TE from experimental data (e.g., [32; 33; 34]). However, most of the methods are very sensitive to noise and need large amounts of data and parameters tuning which limit their utility. Symbolic TE (STE) [35], which estimates TE through symbolization, is a convenient, robust, and computationally efficient method to measure the flow of information in dynamic and multidimensional systems. This makes STE a promising measure of the preferred direction of information flow between brain regions. STE has been widely applied in EEG studies, including the effects of anesthesia on information processing in the brain [36], the study of epileptic networks [37], investigating the impacts of sleep apnea-hypopnea on the EEG signal [38] and predicting response to clozapine therapy for SCZ patients using resting-state [10] and P300 activities [39]. This confirms that the STE method is a promising tool to study brain network connectivity and its alteration due to mental and neurological disorders and the use of medications. However, to the best of our knowledge, this is the first study applying STE to diagnose schizophrenia from resting-state EEG data. In this study, we investigated the impacts of schizophrenia on STE at various frequency bands. The contribution of this paper is 2-fold. First, the fast and robust STE approach is used to measure the EC between brain regions for schizophrenic patients. Second, an MLA is developed based on the features extracted from these EC measures to diagnose schizophrenia. The novelty of this study is therefore in combining STE and MLAs to diagnose schizophrenia, which helps to discriminate SCZ patients from HCs with high accuracy by using a small number of features and less complex traditional MLAs, relative to previous studies.