## 1 Introduction  
Parkinson's disease (PD) is the second largest progressive neurodegenerative disorder of the central nervous system [1]. It is characterized by several motor symptoms such as tremor at rest, rigidity, bradykinesia, and postural instability. Generally, the clinical diagnosis is based on a combination of clinical symptoms, a thorough history of patients, and a response to levodopa [1; 2; 3]. It is reported that motor symptoms appear only when approximately 40-60% of the dopaminergic neurons in the substantia nigra are lost [4; 5; 6; 7]. In most cases, by the time PD is diagnosed, the disease may have already started 4 to 6 years beforehand [7]. There is therefore is a need to develop a test for detecting PD before the onset of the motor symptoms (called Presymptomatic PD (PPD) [8; 9]). Such a test would help a clinician not only in detecting PPD, but also in monitoring the progression of PD or in monitoring efficacy of early therapeutic interventions [10; 11]. Detecting PPD helps patients to start their treatment in the presymptomatic phase. The utilization of machine learning techniques for the early diagnosis of [PERSON]'s disease is an active area of research. These efforts aim to leverage the power of these techniques to enhance the accuracy and timeliness of diagnosis [12; 13; 14; 15; 16]. It has been established that there are advantages of early pharmacological and therapeutic intervention in PD such as monoamine oxidase B inhibitors [17], catechol-o-methyl-transferase inhibitors [18], amantadine [19], amplitude training [20], reciprocal pattern training [21], and gait-balance training [22], including a reduction in symptoms, particularly dyskinesia, and the delay of levodopa initiation [23]. Both the reduction in symptoms and the potential for slowing disease progression can have a significant impact on improving patients' quality of life [23]. Although there have been recent advances in identifying potential biomarkers [24], including genetic [25; 26] and neuroimaging techniques [11] that help in detecting PD, these are still in the early stages, and further work is needed in this direction. Further, it has also been recognized that a variety of nonmotor symptoms associated with PD may be observed years before the onset of motor symptoms and hence may be used as markers for detecting PPD [27]. Examples of these nonmotor symptoms are olfactory loss [28; 29], rapid eye movement sleep behavior disorders [30; 31; 32], bowel dysfunction, and so on [27; 33; 34]. Recently, reference [7] proposed that a combination of tests (including smell test, transcranial sonography, and SPECT) could be helpful in detecting PPD. Further, [PERSON] [35] proposed a general methodology and an automatic system that can be used for the detection of presymptomatic phase or diagnosis, and/or to monitor the treatment effectiveness for a variety of neurological disorders based on eye movement data for various stimuli. An important ingredient that is lacking in these methodologies is that they are empirical and are not based primarily on an understanding of the mechanism causing these symptoms. As widely noted in the literature [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49], the sensorimotor system of a healthy individual may be seen as a stable control system, while the sensorimotor system of an individual with PD showing motor symptoms may be viewed as a control system with instabilities. The source and mechanism of these instabilities remain topics of debate and investigation with delay-induced instability (with higher loop-delay in PD causing the instability, further explained in Section 2.1) being one of the prominent theories [42; 46; 47; 49]. In general, the latencies in the sensorimotor loop are also known to be amplified and cause instabilities in other conditions as well, such as Multiple Sclerosis (MS) and stroke survivors [50].