141 results about "Biological signal processing" patented technology

The invention relates to the field of signal processing, and discloses a biological signal processing method and device, computer equipment and a storage medium, and the method comprises the steps: collecting a biological signal, and carrying out the preliminary filtering of the biological signal, and obtaining a preliminary filtering signal; performing noise characteristic analysis on the preliminary filtering signal, selecting a proper filtering mode according to an analysis result, and designing filter parameters through fuzzy logic processing; inputting the filter parameters into an adaptive filter to process the signal so as to obtain a filtered biological signal; in combination with a de-noising convolutional neural network model, noise identification and fuzzy logic processing are performed on the filtered biological signals, and filter parameters are dynamically adjusted; and performing multi-round iterative optimization until a target filtering effect is obtained, and outputting an optimized biological signal. According to the biological signal processing method, complex noise in the biological signal is effectively removed, the filtering precision is improved, meanwhile, the signal integrity is kept, and a reliable basis is provided for subsequent biological signal analysis and processing.

The invention provides a biological health system, a physiological state regulation and control method, wearable equipment and a storage medium, and the biological health system comprises a detection assembly which is configured to collect a biological signal of a user in real time; a processing component configured to calculate a physiological state regulation parameter based on the phase of the biological signal, and generate a stimulation signal based on the physiological state regulation parameter; and the output component is configured to output the stimulation signal, so that the coupling effect of the biological signal and the stimulation signal regulates and controls the physiological state of the user. The biological signal of the user is acquired in real time, and the personalized physiological state regulation and control parameter of the user is calculated based on the phase of the biological signal to generate the stimulation signal in real time, so that the physiological state of the user is regulated and controlled through the coupling effect of the stimulation signal and the biological signal.

The invention discloses a motor imagery electroencephalogram decoding method based on a multi-attention bridge structure network, and relates to the field of biological signal processing and motor imagery brain-computer interfaces. The method comprises three modules: 1) a depth separable convolution attention module; 2) bridge structure modules; and 3) a multi-scale parallel space-time convolution module. In order to solve the problems of poor robustness and weak generalization ability in a motor imagery electroencephalogram signal decoding process, a deep separable convolution attention module is utilized to focus important channel space topological characteristics of electroencephalogram signals, and a bridge structure module is utilized to effectively capture local and global time domain characteristics; and multi-dimensional attention on spatial-temporal characteristics of the electroencephalogram signals is realized. And finally, spatial-temporal features of different scales are extracted through a multi-scale parallel spatial-temporal convolution module, so that the decoding recognition rate, robustness and generalization ability of the network are improved, and an effective solution is provided for improving the stability and applicability of a motor imagery brain-computer interface.

The invention provides an electroencephalogram signal artifact removing method and device, equipment and a medium, and relates to the technical field of biological signal processing, collected electroencephalogram signal data is processed to obtain an optimal modal number and an optimal bandwidth parameter, and the optimal modal number and the optimal bandwidth parameter are used for conducting self-adaptive variational mode decomposition on the electroencephalogram signal data to obtain a plurality of modal components; then carrying out multi-dimensional feature analysis to obtain a plurality of feature indexes for judging an artifact suspicion mode and an effective mode component; performing short-time Fourier transform on the artifact suspicion mode, and constructing a time-frequency confidence map to guide weighted time-frequency independent component separation on the artifact suspicion mode to obtain an artifact component; suppressing the artifact component to obtain a suppressed independent component, and performing weighted reconstruction and multi-component fusion on the suppressed independent component and the effective modal component based on the time-frequency confidence map to obtain an artifact-removed electroencephalogram signal; and the fidelity, the robustness and the real-time performance of the electroencephalogram signal are improved.

The invention relates to the field of biological signal processing, and discloses an EEG signal fatigue detection method based on a time sequence enhanced hybrid architecture, which designs a time sequence perception extrusion-excitation module, adaptively performs attention weighting on a time dimension, effectively extracts key time sequence features, and improves the accuracy and reliability of fatigue detection. A fixed position code is replaced by an LSTM time embedded code, so that the attention of the model on important information in the time dimension is further enhanced; a mixed framework combining a space selection module and a Transform multi-head attention module is designed, and the long context sequence sensing ability and generalization ability are enhanced; a learnable activation function is introduced to replace a fixed linear weight, and the classification capability of the model is enhanced through nonlinear representation such as a spline function. According to the method, fatigue signs of EEG signals can be effectively captured, and the method has remarkable advantages in detection of different subjects and has important practical application value in reduction of traffic accidents caused by driver fatigue.

The invention provides a campus abnormal behavior detection method and device and electronic equipment, and belongs to the technical field of artificial intelligence and biological signal processing, and the method comprises the steps: obtaining the facial expression features, body motion features, voice emotion features and physiological signal data of a target person in a monitoring region in a campus scene; fusing the facial expression features, the voice emotion features, the body action features and the physiological signal data to obtain fused data; performing emotion recognition and behavior prediction on the fused data based on a preset time sequence model to obtain an analysis result; and predicting whether an abnormal behavior occurs based on the analysis result, and performing early warning when the prediction result is that the abnormal behavior occurs. The technical problem that potential safety hazards still exist in actual detection results due to the fact that application of emotion and physiological signals in abnormal behavior detection is not fully considered in the prior art is effectively solved.