32 results about "Eeg classification" patented technology

The invention discloses a mixed attention prototype network electroencephalogram classification method fusing feature weights. The method comprises the steps that electroencephalogram data are collected; the method comprises the following steps: preprocessing collected EEG original data, extracting differential entropy features, and carrying out standardization and sample sampling on the extracted features to generate a support set and a query set; inputting the preprocessed features into a prototype network, extracting an embedded representation of a source domain / target domain, and calculating a feature weight and a channel-frequency band weight through a double attention layer; calculating prototype representation, and fusing the feature attention weight and the channel-frequency band attention weight to obtain a final weighted distance; a Grad-CAM visualization mechanism is introduced, and the distribution consistency of weight distribution is analyzed; and inputting the fused representation into a classifier, calculating classification loss according to a prediction score, and outputting a cross-period EEG electroencephalogram classification result, the cross-period EEG electroencephalogram classification method solves the problems of traditional black box and weak domain adaptation, and effectively improves the cross-period EEG classification precision, robustness and interpretability.

The invention provides a brain wave detection and classification method and device, a program product and electronic equipment, and relates to the technical field of data processing. The brain wave detection and classification method comprises the following steps: acquiring to-be-detected electroencephalogram data, and determining convolution features of the to-be-detected electroencephalogram data under various downsampling rates; based on the pooling kernel corresponding to each down-sampling rate, pooling processing is carried out on the convolution features of the to-be-detected electroencephalogram data under each down-sampling rate, and pooling features of the to-be-detected electroencephalogram data under each down-sampling rate are obtained; wherein the pooling kernel corresponding to each down-sampling rate is negatively correlated with the down-sampling rate; and based on the pooling features of the to-be-detected electroencephalogram data under each down-sampling rate, brain wave waveform classification is carried out, and a classification detection result corresponding to the to-be-detected electroencephalogram data is obtained. The comprehensiveness and sufficiency of brain wave feature extraction can be improved to a certain extent, and the accuracy of brain wave classification result detection is improved.