Thesis supervision | Madis Masso: Empirical Comparison of Machine Learning Algorithms for EEG Data
The aim of this work is to compare different machine learning algorithms in an attempt to find the best one for classifying EEG data. In order to achieve this, the data from ten subjects were classified by ten machine learning algorithms. The algorithms were compared in three ways: Firstly, they were compared by using three performance metrics, secondly, by using clustergrams and lastly, by using corralation matrices. The results from the comparison show that the without parameter optimization, logistic regression model is the most efficient algorithm for classifying EEG data. However, with parameter optimization, random forest is the most efficient algorithm for classifying EEG data.