Optimal Feature Subset Selection For Pattern Classification and Recognition Using Spectral Entropy as a Complexity Measure From Alcohol and Sleep EEG Data

The quantity of unprocessed data in multi-channel EEG analysis is enormous. Most datasets have some level of noise or redundancy, which could lower the accuracy of any relevant features that are uncovered. This result in a rise in computational complexity and a corresponding rise in the importance of concerns related to memory consumption. Consequently, feature selection has become the matter of significant research in the arenas of machine learning and statistics since it is crucial for efficient analysis of data while needing less memory and time. Based on the findings of this study, selecting features with high class separability from multichannel EEG recordings is an efficient method for performing supervised learning tasks. With this study, we intend to test the following theory: The best feature subset will have features that are substantially linked with the class labels but not connected with each other. Using the same alcoholic EEG database as these other investigations, we created and compared three feature selection algorithms. The purpose here is to use class knowledge in a feature selection technique to enhance the accuracy with which classes may be detected.