Evaluation of ANN, LDA and Decision trees for EEG based Brain Computer Interface

Brain Computer Interface (BCI) is a communication system, which avoiding the brain's normal output pathways of muscles and peripheral nerves and allows a patient to control its external world only by means of brain signals. For successful implementation of BCI, dimensionality reduction and classification are fundamental task. In this paper, we used a publically available EEG signals data of the Upper Limb Motion. First the dimensionality of the data is being reduced by using Principal Component Analysis (PCA) followed by classification of the reduced dimensioned dataset by well-known classifiers e.g. Artificial Neural Networks (ANN), Linear Discriminant Analysis (LDA) and Decision trees (DT). To identify a classifier which does the classification task more efficiently, we compare their performances on the basis of Confusion Matrices and Percentage Accuracies. The experimental results show that ANN is the best classifier for the classification of brain signals and has the percentage accuracy of 81.6%.