2 results about "Common spatial pattern" patented technology

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.