Motion imagery electroencephalograph classification method based on multilayer extreme learning machine
An ultra-limited learning machine and EEG technology, which is applied in the field of motor imagery EEG signal classification to achieve the effect of low time consumption and improved classification accuracy.
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[0017] The present invention will be further described below in conjunction with the drawings and specific embodiments.
[0018] Suppose there is a training data set TrainData and a set of test data set 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. The samples in TrainData and TestData belong to K categories.
[0019] Classification method of motor imagery EEG signals based on multi-layer over-limit learning machine, the flowchart is as follows figure 1 Shown.
[0020] Step 1: Divide TrainData and TestData into S-segment brain electronic 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 of the fixed time window, the window size is fixed, so D 1 =...
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