7 results about "Brain control" patented technology

The application discloses a brain-computer interface smart home multi-device cooperative control method and system, acquires user brain electrical signals and home device state data for synchronous fusion processing to generate a brain control intention parameter set; intention feature extraction and classification are performed according to the brain control intention parameter set to determine an intention level, fatigue perception intention intensity analysis is performed to generate a device activation coefficient, and an intention trigger mapping table is constructed; device coupling analysis is performed on the brain control intention parameter set to determine a cooperative weight, multi-device state dependent linkage characteristics are identified, the intention trigger mapping table is dynamically adjusted to generate linkage correction parameters; conflict constraints are identified according to the linkage correction parameters, irreversible operation intention review is performed to determine an executable instruction set, and a device execution sequence is generated; the intention trigger mapping table and the linkage correction parameters are fused to generate cooperative output parameters, execution priority weights are determined in combination with the conflict constraints, multi-device linkage instructions are output, and safe and orderly brain-computer interface multi-device cooperative control is realized.

This invention discloses a brain-controlled wheelchair method, system, computer device, and storage medium based on multimodal bioelectrical signal fusion, relating to the field of human-computer interaction technology. The method includes: synchronously acquiring bioelectrical signals and environmental perception data and aligning their transmission; calculating attention entropy and brain-muscle coupling degree to generate an active control intention index, and constructing multimodal fusion features by combining reference signals synthesized from a generation model; decoding the user's intention distribution and constructing an environmental semantic map, and associating them to form a joint feature tensor; extracting brain state parameters to adjust the stiffness coefficient of the virtual clamp, and combining it with signal difference correction coefficients; inputting the joint feature tensor and the corrected stiffness coefficients into a decision module, generating motion control parameters through constraint optimization and outputting them; driving the wheelchair to perform actions, and using feedback data for model updates. This invention achieves a safe, adaptive, and long-term stable brain-controlled wheelchair through dual verification of attention entropy and brain-muscle coupling degree, spatiotemporal collaborative decision-making of intention and environment, and meta-learning lifelong adaptation.

The application provides a user intention recognition method and device and a terminal equipment, which are suitable for the technical field of data processing, and the method comprises: performing space-time slicing and projection processing on a plurality of to-be-processed electroencephalogram signals to generate a plurality of to-be-processed electroencephalogram signal sequence information; performing mask processing on the plurality of to-be-processed electroencephalogram signal sequence information according to a preset mask ratio and a preset dynamic mask processing rule to generate a plurality of electroencephalogram signal mask sequence information; and obtaining a target intention recognition model according to the plurality of electroencephalogram signal mask sequence information and an initial intention recognition model. The application significantly improves the robustness of the model to complex noises such as vehicle vibration and electromagnetic interference and the cross-user generalization ability, realizes precise recognition of the driver's motor imagery intention with low delay and high accuracy, and provides stable and efficient decision support for intelligent cabin non-sensing brain control and man-machine cooperation.

The application discloses a brain-controlled upper limb rehabilitation robot brain-machine fusion grasping system and method, relates to the field of brain-computer interfaces, and aims to solve the problems of single control mode of traditional robots and poor autonomy in a dynamic non-structural environment. The system comprises a brain control module based on fine hand movement, an intention reasoning module based on fine hand movement, and a rehabilitation robot body module. The brain control module based on fine hand movement is connected with the intention reasoning module based on fine hand movement through a first wireless communication module, the intention reasoning module based on fine hand movement is connected with the rehabilitation robot body module through a second wireless communication module, the brain control module based on fine hand movement sends brain control instructions to the intention reasoning module based on fine hand movement through the first wireless communication module, and the intention reasoning module based on fine hand movement sends control instructions to the rehabilitation robot body module through the second wireless communication module.

The application provides a kind of all-weather full autonomous brain-computer interface training system, the system is by distributing the weight of behavior instruction and brain control instruction, namely, completely behavior instruction judges the consistency of behavior action and behavior stimulation, phase transition to judge based on brain control instruction completely, and the realization of full autonomous smooth control with higher degree of automation provides convenience for long-term brain science, neurorehabilitation training, brain-computer interface control algorithm and the like research.

The application provides a weak electrical signal analysis method and system based on multi-level multi-modal fusion, and belongs to the field of weak signal processing and human-computer interaction. In view of the problems that weak electrical signals are easily disturbed by noise and have complex transmission paths, the application takes the typical weak electrical signal of speech electroencephalogram as a processing object, constructs a unified data analysis model on the three-layer space structure of the scalp-ear circumference-ear, and introduces time-frequency feature extraction, kernel principal component analysis, deep network fusion and nonlinear dynamic modeling methods, so as to realize high-robustness decoding of multi-region weak electroencephalogram signals, and provide a new analysis tool and technical path for brain control communication and rehabilitation training based on weak signals.