## 1 Introduction  
Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting seniors [1]. The motor features of PD include bradykinesia, posture, and rest tremors, while non-motor complications can include constipation, postural hypotension, depression, and dementia. Pathologically, PD is characterized primarily by the degeneration of dopaminergic cells in the substantia nigra in the midbrain [2], leading to dopamine depletion in the striatum [3]. This neurochemical alteration impairs neuronal processing in the basal ganglia [4], resulting in prominent 8-30 Hz oscillations in local field potential recordings. Non-invasive neuroimaging modalities (e.g., fMRI, MEG, EEG) have assisted in detecting altered brain networks in PD. As fMRI has limited temporal resolution (~sec) [5], it cannot directly detect features such as pathological oscillations seen at higher frequencies (e.g., 8-30 Hz). On the other hand, fMRI is capable of assessing changes in functional connectivity between macroscopic brain regions. Prior studies have demonstrated that changes in striatal dopamine profoundly affect cortico-striatal connectivity, with decreases in connectivity between the posterior putamen and cortical somatosensory and motor regions [5]. Resting-state fMRI studies have also shown the disruption of connectivity in cortico-striatal networks [6]. The improved temporal resolution of EEG and MEG allows for the assessment of oscillations and has also demonstrated alterations in connectivity in PD at much more rapid time scales [7]. Thus, researchers are starting to explore ways to utilize rsEEG in diagnosing neurodegenerative diseases such as PD [8]. Several methods already exist to analyze rsEEG signals. These include spectral and complexity analysis [9], energy and entropy features followed by a decision tree classifier [10], and time-frequency properties of components from blind source separation [11]. In many of the aforementioned approaches (e.g., [12]), the EEG signals are divided into short (e.g., 2-s) epochs, which may fail to capture low-frequency dynamics of potential diagnostic utility. Here, we used a full 60 s of data to avoid this complication. Moreover, authors in this paper used time-frequency representations in which the channels' location are not considered as we consider here. In [13], the authors have developed a classification approach using extracted features with the aim of emotion classification in HC and advanced (Hoehn-Yahr scale 4-5 stage) PD subjects, in contrast to the mildly affected people assessed here (Hoehn-Yahr 1-2). Many previous approaches do not fully consider the spatial, temporal, and spectral properties of EEG signals while looking at the interaction between EEG channels may also be fruitful [14]. For example, in [15], a 1D-CNN was developed to classify features (as opposed to utilizing the raw data). Based on the assumption that the spatial location of the channels may benefit classification, EEG signals can be converted into multispectral images for subsequent classification [16]. In a related study, a feature-fusion multispectral image method (FMIM) was proposed to analyze multi-channel EEG signals and proved superior to conventional multispectral image methods [17]. Here, our aim is to consider channels connectivity as a key factor for [PERSON]'s classification, which would provide us with the exact interaction of channels instead of considering only their relative locations. We propose using images based on the directional connectivity (DC) between EEG channels for the classification of the EEG from PD subjects. DC approaches model how neural activity in one region modulates neural activity in another location (but not necessarily vice versa). One effective way to compute DC is to employ multivariate autoregressive (MVAR) modeling [18]. This utilizes a linear combination of previous values of one channel to find its causal influences on another one, akin to Granger causality [19; 20].