## 3 Proposed Methodology
### Data Preprocessing  
Two different streams of preprocessing have been applied due to the different nature of the data. In the very first approach, the preprocessing of EEG has been performed, in which the EEG data are prone to various types of noise and artifacts, including eye blinks, muscle movements, and external electromagnetic interferences, as shown in Figure 5.  
Figure 4: MRI snippet of PD and non-PD patients. Figure 3: EEG snippet of PD and non-PD patients. However, EEG preprocessing does not have a universally agreed-upon pipeline, as it remains an area of ongoing research [35]. The optimal preprocessing steps can vary significantly depending on the specific characteristics of each EEG signal. It is important to carefully consider the properties of the dataset in question to determine which preprocessing methods will most effectively enhance the validity and interpretability of the data. Each dataset may require a unique combination of steps tailored to its specific needs and the objectives of the study. The working of EEG Preprocessing is shown in Algorithm 1. ```
Input:X--EEG data matrix Output:Xpro. --EEG data
1. Artifact Removal using ICA \(\text{A},\text{S}\leftarrow\text{ICA}(\text{X})\)// Decompose X \(\text{S}\_\text{clean}\leftarrow\text{S}-\text{Artifacts}\)// Remove artifact components from S \(\text{X}\_\text{clean}\leftarrow\text{A}^{\text{-1}}\ast\text{S}\_\text{clean}\)// Reconstruct clean EEG data
2. Remove Bad Channels \(\text{C}\_\text{bad}\leftarrow\text{IdentifyBadChannels}(\text{X}\_\text{clean})\)// Identify bad channels \(\text{X}\_\text{clean}\_\text{good}\leftarrow\text{X}\_\text{clean}-\text{C}\_ \text{bad}\)// Exclude bad channels
3. Apply Filters \(\text{X}\_\text{filtered}\leftarrow\text{Filter}(\text{X}\_\text{clean}\_\text{ good})\)// Apply necessary filters (e.g., low-pass, high-pass) 4. Output Processed Data \(\text{Xpro. }\leftarrow\text{X}\_\text{filtered}\)// Final processed EEG data

Figure 5: EEG anomalies. In this research, ICA (Independent Component Analysis) [36] has been utilized to identify and extract eye blinks and movements, subsequently removing these elements from the EEG signals. Another critical aspect of preprocessing involves identifying \"bad\" channels in the EEG data, which do not provide reliable information. It is crucial to exclude these channels early in the analysis process, as retaining them can adversely affect further analyses. For this purpose, visualization methods have been employed. Additionally, various types of filters are applied to the signals to refine the data further. These include a low-pass filter [37] that retains frequencies below a certain threshold (low frequencies \"pass through\"), while higher frequencies above this threshold are filtered out. A high-pass filter [37] that operates inversely to the low-pass filter allows only high frequencies to remain, eliminating those below a specified value. Whereas the notch filter [38] has been designed to remove a specific single frequency (noise), multiple notch filters can be used together to eliminate a particular set of single frequencies. The working of MRI Preprocessing is shown in Algorithm 2. ```
Input: M \(\leftarrow\)MRI data matrix Output: Mpro. 1. Image Quality Enhancement M_enhanced \(\leftarrow\) EnhanceQuality(M) // Correct spatial distortions and enhance contrast
2. Skull Stripping M_brain \(\leftarrow\) StripSkull(M_enhanced) // Remove non-brain tissues
3. Normalization M_normalized \(\leftarrow\) Normalize(M_brain) // Standardize intensity across images
4. Spatial Normalization M_standardized \(\leftarrow\) AlignToTemplate(M_normalized) // Align images to a standard brain template
5. Smoothing M_smoothed \(\leftarrow\) Smooth(M_standardized) // Apply Gaussian smoothing filter
6. Output Processed Data M_pro.