Study on Identification Algorithm of EEG Imaginary Movements

Movement whether it is actual or imaginary can produce different electroencephalogram (EEG) signals. How to extract features of signals and accurately classify them is a key to brain-computer interface(BCI) system. In the paper, BCI competition data downloaded from BCI website are used as study object, through time-domain analysis and frequency-domain analysis, according to the attribute of event-related synchronization (ERS) and event-related desynchronization (ERD) during imagery movement, energy difference of lead C3 and C4 are selected as features and wavelet package is used to extract them. Probabilistic neural networks (PNN) is used as classification method. Compared with other two calssification methods such as support vector method (SVM) and liner classifier, the classification accuracy rate of PNN reaches to 89.2% steadily and is higher than them. It is proved that the method provided in the paper are effective for identifying imaginary movements.