Feature Extraction Evaluation for Two Motor Imagery Recognition Based on Common Spatial Patterns, Time-Frequency Transformations and SVM

In recent years, motor imagery has been used as a communication alternative in brain computer interface systems. In this paper an evaluation of five different types of feature extraction methods to recognize two motor imagery signals based on common spatial patterns (CSP), and time-frequency transformations are evaluated; CSP, continuous Wavelet transform (CWT), Stockwell transform (ST), and the following combinations CSP+CWT and CSP+ST. The classifier employed to recognize between right-hand, and left-hand was a support vector machine. The proposed methods were evaluated in two know datasets and compared with other state of the art methods. The best performance achieved with the proposed methods, considering a correct recognition rate, was 79.87% +/- 10.73% and 73.25% +/- 08.04% with the datasets BCI Competition IV dataset 2a and EEGdataset, showing better performances than the reported works. Besides, the CSP+SVM method requires a processing time of only 1.73 seconds which make it suitable for real time applications.