Investigating the presence of mu signal during motor movements using SVM, LDA, and CNN

BCI is a technology that enables individuals to interact with computers or other devices using only their brain signals. The mu rhythm is a type of EEG signal that is observed over the sensorimotor cortex during rest or motor tasks [1]. This paper investigates the presence of mu wave in Motor Imagery (MI) based Brain-Computer Interface (BCI) experiments using the Berlin BCI competition IV dataset 1. In this study, an epoch of 4 seconds each was extracted using Event codes and labels. Butterworth Bandpass of 8-12Hz, 8-14Hz, and 8-16Hz were used for preprocessing the data with three different frequency ranges, known to encompass the frequency range of mu waves. Common Spatial Patterns were used for feature extraction. We used the 80/20 method to split the data for training and testing the algorithms. Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) were trained by using these extracted features, and Convolutional Neural Networks (CNN) were trained using the preprocessed data. Results show that the 8-16Hz frequency range is the most suitable for investigating the presence of mu waves in MI BCI experiments, as the classification accuracy of all three algorithms increased significantly in this range compared to the other two ranges. The study highlights the importance of selecting the appropriate frequency range for investigating the presence of mu waves in MI BCI experiments, and the results presented in this paper can aid in designing and optimizing BCI experiments and developing more accurate and reliable BCI systems in the future.