Design a detector$/$classifier using classical techniques to distinguish between healthy and epileptic subjects based on EEG recordings provided in $.$fif file format. The EEG data is recorded using the $10-20$ electrode placement system, with each recording lasting $3-5$ minutes and sampled at $125$ Hz$.$

Your task includes the following steps:

Preprocess the EEG data: Apply necessary preprocessing steps such as filtering and artifact removal.

Plot the EEG data: Visualize the preprocessed EEG signals.

Feature Extraction: Extract features from the EEG data, including Haar wavelet transform, power spectral density, and Shannon entropy.

Plot PDF: Plot the Probability Density Function $($PDF$)$ of the extracted features.

Likelihood Ratio Test: Set up a likelihood ratio test using the extracted features.

Calculate PFA and PD: Determine the Probability of False Alarm $($PFA$)$ and Probability of Detection $($PD$).$Plot ROC Curve: Plot the ROC curve showing the relationship between PD and PFA $,$ write a proper matlab code by using diffrent feature.