Machine Learning Algorithms Based on the Classification of Motor Imagination Signals Acquired with an Electroencephalogram

Recent studies of brain-computer interface (BCI) have focused on the use of machine learning algorithms for the classification of brain signals. These algorithms find patterns in brain waves to distinguish between one class and another to turn them into control commands. To discuss the efficiency of classification algorithms in BCI for the classification of electroencephalogram (EEG) signals with motor images (MI), in this study twelve machine learning (ML) classifiers are applied and analyzed. The algorithms used are: 1) Convolutional network-Long short term memory (CNN-LSTM), 2) Convolutional network-gate recurrent unit (CNN-GRU), 3) Convolutional-bidirectional long short term memory (CNN-BiLSTM), 4) convolutional-bidirectional gated recurrent unit (CNN-BiGRU), 5) Random Forest, 6) Decision tree (DT), 7) Multilayer Perceptron (MLP), 8) Gaussian Naive Bayes, 9) Support Vector Machine (SVM), 10) Logistic Regression, 11) AdaBoost, 12) K-nearest neighbor (KNN). As classification tests, four mental tasks were registered, which are, the imagination of the movement of the left foot, the imagination of the movement of the left hand, state of relaxation, and mathematical activity, these mental tasks were obtained by a portable electroencephalogram (EEG) device. In the tests carried out it was found that the highest rate was 97% and the low rate was 22%.