Voting strategy classification method of motor imagery EEG signal based on extremely fast learning machine
An extremely fast learning machine and motor imagery technology, applied in the field of pattern recognition and brain-computer interface, it can solve the problem of not reducing the randomness of sample prediction categories, and achieve the effect of improving the classification accuracy, reducing randomness, and reducing time-consuming.
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[0019] The present invention will be further described below in combination with specific embodiments.
[0020] Suppose there is a training data set TrainData and a set of test data sets TestData, the sample size of TrainData is N, and the dimension is D; the sample size of TestData is M, and the dimension is also D. Among them, the samples in TrainData and TestData belong to K categories.
[0021] Voting strategy classification method for motor imagery EEG signals based on extremely fast learning machine, the flow chart is as follows figure 2 shown.
[0022] Step 1: Divide the TrainData and TestData into S-segment EEG signals by means of fixed time window division. TrainData i Represents the i-th sub-signal in the training data set, and the dimension of each sub-signal is D i (i=1,2,...,S). TestData i Represents the i-th sub-signal in the test data set, and the dimension of each sub-signal is D i (i=1,2,...,S). Because a fixed time window is used, the window size is ...
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