Training method, and classification method and system for eeg pattern classification model
AssigneeWuyi University
InventorsHongtao Wang, Tao Xu, Guanyong Lu
Life of this patent
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Abstract
A training method for an electroencephalogram (EEG) pattern classification model, including: acquiring EEG data, pre-processing the EEG data, and labeling the EEG data to obtain a labeled training data set, wherein the training data set comprises the pre-processed and labeled EEG data; inputting each piece of EEG data in the training data set into an attention-mechanism-based convolutional neural network to extract pattern features of the EEG data; and modifying parameters for the EEG pattern classification model according to the pattern features and labels of the EEG data.
Claims10
A training method for an electroencephalogram (EEG) pattern classification model, comprising:
- acquiring EEG data, pre-processing the EEG data, and labeling the EEG data to obtain a labeled training data set, wherein the training data set comprises the pre-processed and labeled EEG data;
- inputting each piece of EEG data in the training data set into an attention-mechanism-based convolutional neural network to extract pattern features of the EEG data;
- and
- modifying parameters for the EEG pattern classification model according to the pattern features and labels of the EEG data.
A method for classifying electroencephalogram (EEG) patterns, comprising:
- acquiring EEG signals, and pre-processing the EEG signals to obtain an EEG data set, wherein the EEG data set comprises the pre-processed EEG signals;
- inputting each EEG signal in the EEG data set into an attention-mechanism-based convolutional neural network to extract pattern features of the EEG data;
- and
- classifying the pattern features of the EEG data to obtain an EEG pattern classification result.
Dependencies are inferred from the claim text.