Human movement intentions based on EEG using brain computer interfaces

This paper proposes classifying the signal of movement intention and identifying feature selection and translation algorithms. Furthermore, this paper will select the most appropriate algorithms for the feature classification of the signal of movement intentions. The study uses signals previously recorded in the BCI lab. Feature selection and classification were based on the Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). The results of classification show that LDA classifier recorded the highest accuracy in 3 and 4-class of movement in comparison to the SVM. LDA classified the 4-class of movements at central channel and single channel with the average accuracy of 43.75% and 42%. Overall, LDA performed better result in 3-class of movement, with an average accuracy 62%.