Feature extraction of EEG based on data reduction

An important factor affecting the rate of BCI is the number of EEG features. To reduce the number of features is an important way to improve the speed. In this paper, a method of data reduction be described, features marked be used to discrete the continuous EEG, and then choose the features from the discrete data with the help of this method. The results show that classification accuracy has not been reduced but the number of features is reduction.

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