DWT-based feature extraction for motor imagery classification

Brain computer systems interface (BCI) is a continuing and growing field in evolution. During the last decades, many laboratories have begun to explore technologies BCI as a new communication option for people with neuromuscular disabilities that prevent them from using conventional augmentative communication methods. In this work is presented a methodology for the classification of motor imagination, using features extracted from power spectral density (PSD) as feature extraction technique. Two approaches are evaluated, first computing PSD features from the raw data and the second, performing a decomposition by means of the discrete wavelet transform (DWT). Obtained classification results show that features obtained through PSD with DWT achieves an accuracy of 85%, a sensitivity of 90% and a specificity of 80% while the results obtained with PSD over the raw data has a precision of 70%, one sensitivity 60% and specificity of 80%. Demonstrating that the performance of the classifier with the proposed preprocessing stage was improved.