3 results about "Common spatial pattern" patented technology

The application provides a small sample electroencephalogram recognition method, system and device based on multi-feature fusion, which comprises the following steps: slicing a to-be-tested electroencephalogram according to a time axis to obtain a to-be-tested data set; performing micro-state feature extraction and common spatial pattern (CSP) feature extraction on each to-be-tested slice in the to-be-tested data set; splicing the micro-state feature and the CSP feature of each to-be-tested slice to form a fusion feature of the current to-be-tested slice; using a pre-trained classifier model to classify the fusion feature of each to-be-tested slice to identify whether each to-be-tested slice belongs to electroencephalogram of a target disease patient; and when the number of to-be-tested slices in the to-be-tested data set that are determined to belong to the electroencephalogram of the target disease patient is greater than a preset proportion of the total number of slices in the to-be-tested data set, the to-be-tested electroencephalogram is recognized as the electroencephalogram of the target disease patient. The application can accurately recognize the electroencephalogram of a depression patient and provide a diagnosis aid for doctors.

The invention relates to the technical field of brain-computer interfaces, and discloses a lower limb movement brain-computer interface signal acquisition method based on target guidance. According to the method, when a target-oriented lower limb motor imagery task is executed, multi-channel electroencephalogram signals and task event marks are recorded synchronously, sensory motor rhythm frequency band signals are extracted through band-pass filtering, and electro-oculogram and myoelectricity artifacts are removed through independent component analysis. And training a spatial filter by using a common spatial pattern algorithm, and carrying out dimension reduction processing on the signal to obtain an optimal source signal. And by calculating the power value of the optimal source signal in the sensory movement rhythm frequency band, constructing an electroencephalogram feature vector representing the lower limb movement intention. According to the method, the signal purity and the recognition accuracy are improved, and the anti-interference capability of a brain-computer interface system is enhanced.

The method comprises: acquiring an electroencephalogram signal to be classified; performing feature extraction on the electroencephalogram signal to be classified in parallel through a one-to-many filter bank common spatial pattern network and a residual convolutional neural network, and outputting first feature information and second feature information of the electroencephalogram signal to be classified; performing feature relearning on the first feature information and the second feature information in parallel through a dynamic graph convolutional neural network and a gated recurrent neural network, and outputting third feature information and fourth feature information of the electroencephalogram signal to be classified; performing feature fusion on the third feature information and the fourth feature information simultaneously through an attention network to obtain target feature information of the electroencephalogram signal to be classified; and obtaining a target classification result of the electroencephalogram signal to be classified according to the target feature information through a prediction network.