Application of Energy Entropy in Motor Imagery EEG Classification

Feature extraction and classification of EEG signals is core issues on EEG-based brain computer interface (BCI). Typically, such classification has been performed using signals from a set of selected EEG sensors. Because EEG sensor signals are mixtures of effective signals and noise, which has low signal-tonoise ratio, motor imagery EEG signals can be difficult to classification. In this paper, the energy entropy was used to preprocess motor imagery EEG data, and the Fisher class separability criterion was used to extract features. Finally, classification of four types motor imagery EEG was performed by a method based on the statistical theory or multilayer backpropagation neural networks (BPNN) and support vector machines (SVM). The results showed that classification accuracy using our method was significantly higher then using back-propagation neural networks or support vector machine in any type combination for the three subjects.