###### Abstract
Non-motor symptoms typically appear long before motor disorders, and they include autonomic symptoms, anosmia, pain, depression, anxiety, sleep disturbances and cognitive impairment [7; 16]. The symptoms related to the earlier stage of PD (non-motor symptoms) are difficult to differentiate from other psychiatric syndromes by clinical means [7]. Several imaging techniques, such as single photon emission computed tomography (SPECT), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and transcranial echography, are helpful for confirming the clinical diagnosis of PD and for differentiating from Parkinson [17; 18; 19]. However, these techniques are not useful in detecting the neurological non-motor symptoms preceding the late-stage movement disorders that characterize the clinical onset of PD. All PD symptoms correspond to abnormal neuronal firing rates or the segregation/synchronization of neurons from specific circuits and loops between basal ganglia, thalamus, limbic areas, and specific cortical lobes [20]. Therefore, techniques such as quantitative electroencephalography (qEEG) with the use of event-related potentials and somatosensoryial-evoked potential tests have been proposed to detect the aforementioned abnormalities in brain activity and identify cognitive impairment in the early stages of PD [21; 22]. These abnormalities describe variations in brainwave power: \(\delta\) (0-4 Hz), \(\theta\) (4-8 Hz), \(\alpha\) (8-13 Hz), \(\beta\) (13-30 Hz), and \(\gamma\) (\(>\)30 Hz). The spectral covariance wavelet analysis (CWA) of electroencephalographic records (EEG) was first used in data from women with PD to determine electroencephalographic differences when these were compared to control groups [23]. The CWA showed information undiscernible in a standard analysis of EEG, revealing differences in brain activity (in an eyes-closed resting state) between healthy women and women with PD when compared to age-matched groups (\(<\)60 and \(>\)60 years old) [23]. In this research, we employed a new advanced signal analysis technique, the Wavelet Transform (WT), which is an intelligent algorithm, to extract the spectral information of an EEG. It is important to highlight that the choice of WT over other methodologies, such as the probabilistic analysis provided by machine learning/deep learning (ML/DL), bears significant implications. Unlike ML/DL, WT operates as a spectral analysis method, rendering it highly robust and stable. Consequently, upon repeating the WT algorithms, the results remain consistent and robust, a feature not shared by ML/DL approaches. The inherent stability of WT is of paramount importance, as it enables us to identify and characterize EEG biomarkers that exhibit invariance across multiple clinical analyses. Such biomarkers hold immense potential for clinical applications, facilitating reliable and reproducible assessments of neurological conditions. Moreover, the invariant nature of these biomarkers lends them additional credibility, enhancing their suitability for longitudinal studies, disease progression monitoring, and treatment response evaluation. The Wavelet spectral analysis approach confers distinct advantages, as it allows us to discern subtle variations in the EEG across specific frequency bands. The frequency-specific patterns are of particular relevance in understanding various neurological phenomena, such as brain rhythms, oscillatory dynamics, and event-related or triggered responses. By harnessing the power of the WT, we have unlocked valuable insights into EEG characteristics, laying the foundation for improved diagnostic accuracy, personalized therapeutic interventions, and hence a deeper understanding of neurological disorders. Consequently, the WT not only aids in the identification of stable and reliable biomarkers, but also contributes to the understanding of the underlying neural processes. The specific goal of this study was to apply the CWA to an EEG obtained originally for clinical purposes from men diagnosed with PD.