a. Dataset and data acquisition (20p): Explain the dataset and data acquisition system. Originality provides extra point for evaluation. You can plot acquisition hardware if exists. Explain labeling system if exists. b. Analytical Explanation (30p): For each class of your biomedical signal extract, plot and explain analytical points. i. Time domain features. ii. Frequency domain features. iii. Time-frequency (STFT, Wavelet, MFCC, or/and....) features. c. Dataset Extraction and Output Correlation (10p): Create a timeseries dataset with sliding window. Plot cross-correlation map between inputs(features) and outputs (targets). Explain properties of newly created/custom dataset. d. ML Train and Compare (30p): Split the dataset as training and test and train machine learning algorithms with K-Fold cross validation. Create a table to compare the used ML algorithms according to accuracy, precision, F1 and recall metrics. e. Deployment (10p): Deploy trained model of best algorithm and test the system