The classification and identification technology plays an important role in the research of Brain-computer interface (BCI) systems. In this paper, we do fuzzy clustering disposal for the multi-channel electroencephalogram (EEG) during finger movement at first according to event-related desynchronization phenomena (ERD) in the event-related EEG. Then we classify signal-trial EEG with the feature extracted from EEG based on common spatial subspace decomposition (CSSD) algorithm. The averaged classification accuracy achieves 94.58% in the course of testing on the data from four subjects. Experiment results show that the combination of fuzzy clustering and neural network can greatly improve the rate of identification for finger movement.
['Lanlan Yu', 'Tianxing Meng', 'Jian Hu']
The Finger Movement Identification Based on Fuzzy Clustering and BP Neural Network