6 results about "Portable electroencephalogram" patented technology

The application particularly relates to a multi-channel portable electroencephalogram (EEG) acquisition device based on FPGA, which comprises a plurality of EEG electrodes, an analog acquisition front end, a digital processing back end based on FPGA and ARM, and a host computer. The analog acquisition front end adopts a multi-channel integrated EEG front end chip to perform amplification, conditioning and analog-to-digital conversion on a plurality of analog EEG signals in parallel, and transmit the digital signals to the digital processing back end through an SPI bus. The digital processing back end comprises an FPGA processing unit and an ARM processing unit, the FPGA internally realizes adaptive notch filtering, FIR low-pass filtering and baseline correction in a parallel pipeline structure, and performs real-time parallel processing on the multi-channel EEG signals; the ARM unit configures parameters through an AXI-Lite bus, and utilizes a DMA mechanism to high-speed carry data to the host computer. The analog domain and the digital domain are partitioned and laid out, independently powered and single-point common-grounded, thereby effectively suppressing digital noise coupling.

The invention relates to the technical field of biomedical signal acquisition, and discloses a portable electroencephalogram acquisition system, which comprises an inertia sensing unit, a signal processing unit, a signal processing unit, a signal processing unit and a signal processing unit, and is characterized in that the inertia sensing unit is used for monitoring head movement data and outputting interruption when detecting a movement event; the main control module responds to the interruption to calculate an instantaneous motion intensity index, triggers an impedance monitoring mechanism when the index exceeds a threshold value, and controls the analog front-end module to inject alternating current test current; and the main control module calculates an equivalent impedance module value according to the collected mixed signal, and judges whether the interference type is friction interference or contact failure in combination with a motion index. And the master control module sends an instruction to control the analog front end to execute hardware strategies such as right leg driving gain adjustment or input channel transient short-circuit protection. According to the invention, through a multi-dimensional physical quantity joint judgment and hierarchical hardware anti-interference mechanism, source suppression of noise of different causes is realized, saturation of the amplifier is prevented, and the signal stability of the portable equipment in a motion state is improved.

A new lower limb rehabilitation training system based on electroencephalogram acquisition device and application, including host computer, embedded controller, portable electroencephalogram acquisition device, pressure sensor connected with host computer respectively, and lower limb rehabilitation training module wirelessly connected with embedded controller, the pressure sensor is also connected with lower limb rehabilitation training module, and power module for supplying power to the whole system is also arranged, the host computer is used for processing the received electroencephalogram signal collected by the portable electroencephalogram acquisition device and the pressure signal collected by the pressure sensor of the lower limb rehabilitation training module respectively, and generating control signal, which is wirelessly transmitted to the lower limb rehabilitation training module through the embedded controller, the embedded controller also feeds back the position information of the lower limb of the user output by the lower limb rehabilitation training module to the host computer. The application can realize man-machine interaction well, can dynamically aggregate electroencephalogram signal channel information and extract time domain features at the same time.

Portable Electroencephalogram medical device

The present application relates to the technical field of electroencephalogram acquisition equipment, and discloses a portable electroencephalogram acquisition equipment conversion device, which comprises a loading box, a handle fixed at one end of the loading box, a mounting plate arranged above the loading box, a connecting assembly arranged on the loading box and used for connecting the mounting plate, a conversion device arranged on one side of the mounting plate, an electroencephalogram acquisition equipment slidably connected in the conversion device, a protective shell arranged on the outer side of the conversion device and the electroencephalogram acquisition equipment, an installation groove arranged in the protective shell, a connecting gear rotatably connected in the installation groove, and a connecting rack slidably arranged in the installation groove and engaged with the connecting gear. The present application can help the conversion device and the electroencephalogram acquisition equipment to be quickly connected, so that information conversion work can be performed, the conversion device and the electroencephalogram acquisition equipment can be limited, the pressure of the male port and the female port can be reduced, and the connecting ports of the conversion device and the electroencephalogram acquisition equipment can be blocked and protected.

The embodiment of the invention provides a few-channel electroencephalogram emotion recognition method based on full-channel reconstruction and related equipment, and belongs to the technical field of electroencephalogram signal processing and artificial intelligence. The method comprises the steps that few-channel electroencephalogram signals are acquired, and a feature matrix is constructed; inputting the current sample into a signal reconstruction model which dynamically generates a whole brain function connection diagram adapted to the current sample through a sample adaptive connectivity sampler and generates low-dimensional compression features through a compression feature encoder; further, in a complete feature generator, combining the connection graph and the compression features, and utilizing a graph neural network to reconstruct a full-channel electroencephalogram feature matrix; and finally identifying the emotional state based on the reconstructed features. According to the method, the information bottleneck of few-channel equipment is broken through, global information loss is made up through generative reconstruction, the accuracy and robustness of emotion recognition are remarkably improved, and a core technology is provided for development of portable electroencephalogram equipment.