Efficiency Comparison of Machine Learning Algorithms for EEG Interpretation

This paper intends to use a small protocol to detect stroke disease on a patient by using signals provided by only three EEG probes. To achieve this objective, we compare the performances in terms of accuracy and time of six machine learning (ML) algorithms (Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Tree and CatBoost) during a process of EEG-based classification pathology. We use a database of EEG recording signals collected by three electrodes, established by Beijing University of Chinese Medicine and carried out on subjects healthy or affected by strokes when they are exposed to the vision of planes of five different colors. The subjects are known to be healthy or affected by strokes. The records are used to train each algorithm for 70% of the population, and the performances are estimated on the remaining 30%. Then the process is repeated one hundred times when changing the set used for training and the set used to test. We then consider a statistic on the results obtained using each method for comparison. Our results show that the SVM algorithm is the most efficient in terms of the accuracy of the results, and can detect stoke disease with a reliability of 70%.