CSP features extraction and FLDA classification of EEG-based motor imagery for Brain-Computer Interaction

A Brain-Computer Interface (BCI) is a revolutionising Human-Computer Interface system, which is in developing state. BCI research aims to develop systems that help disabled people to communicate using computers and their brain waves without any muscular action between a person and a computer. Motor Imagery (MI) is one of the popular paradigm to design the BCI system. Besides the BCI´s complicated architecture, the required computation load is heavy. BCIs based on electroencephalogram (EEG) are growing fast, and several EEGbased techniques have been proposed for this purpose. Although EEG signals are characterised by a low spatial resolution and a limited frequency range. Moreover, they are often contaminate by noise caused by a cardiac activity (electrocardiography-ECG effects) and/or ocular artefacts (electrooculography-EOG effects). To handle the problem, in this paper, we present an efficient approach based on Common Spatial Pattern (CSP) for spatial feature extraction and Fisher Linear Discriminant Analysis (FLDA) for classification. In this study, CSP and FLDA have been used to reduce common channels artefacts and to find projections that maximise the discrimination between different classes. A CSP feature extraction of EEG-based Motor Imagery is conducted, then an offline classification of Motor Imagery is performed. Simulation results demonstrate the efficiency and the accuracy of the approach which can be used in real-life applications.