A new feature fusion based technique for classifying multi- class motor imagery problems

The benefit of a Motor Imagery-Based Brain Computer Interface (MI-BCI) is its great independence, which may rely on the user's spontaneous brain activity to run external equipment. However, MI-BCI still suffers from a lack of control effect, which necessitates the use of more efficient feature extraction techniques and classification approaches to extract distinctively separable features from Electroencephalogram (EEG) data. This study suggests a new framework based on bispectrum, entropy, and a similar spatial pattern. We extract MI-EEG signal characteristics using three methods: bispectrum in higher order spectra, entropy, and CSP, and then pick the most contributing features using a tree-based feature selection algorithm. SVM, Random Forest, Naive Bayes, LDA, KNN, Xgboost, and Adaboost classification results were compared, finally, we decided to employ the SVM technique based on the RBF kernel function, which produced the best classification results among them. The proposed approach is used to the BCI competition IV data sets 2a and IVa. The maximum accuracy on the evaluation data set is 85% on data set 2a. The experiment on data set IVa can also yield promising results. Our algorithm's performance has also increased when compared to other algorithms that employ the same data set.