EEG signal classification method based on fast multidimensional empirical mode decomposition
An empirical mode decomposition and EEG technology, applied in the field of EEG signal classification based on fast multi-dimensional empirical mode decomposition, can solve the problems of common multi-dimensional empirical mode decomposition algorithm, such as mode aliasing and low calculation efficiency, to improve the classification Accuracy, good source signal separation, strong interpretability
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[0071] Taking the motor imagery classification problem of Data Set I of BCI Competition IV as an example, the implementation plan and effect of the EEG signal classification method based on fast multidimensional empirical mode decomposition are described below. This embodiment is carried out under the Matlab2017a simulation environment.
[0072] The data of Data Set I of BCI Competition IV consisted of 7 healthy subjects (a~g) facing the corresponding prompts on the computer screen, performing two types of left hand / right hand / foot motor imagery, two of them chose left hand / Foot motor imagery, another five subjects chose left / right hand motor imagery. The present embodiment adopts the data of Data Set I experimenter g of BCI Competition IV.
[0073] Each set of data contains 59 channels of EEG signals, and 200 experiments are performed, such as figure 1 As shown, each experiment included 2s quiet preparation time, 4s motor imagery task execution time, and 2s black screen re...
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