14 results about "Brain control" patented technology

A brain control interface system is disclosed. The brain control interface comprises: a brain control interface configured to detect brain signals indicative of brain activity of a user in an environment, an input configured to obtain data indicative of a current light scene of one or more lighting devices in the environment, a lighting controller configured to control the one or more lighting devices, and one or more processors configured to analyze the brain signals to identify a level of noise in the brain signals when the current light scene is active, and, if the level of noise exceeds a threshold, adjust the light scene while monitoring the level of noise until a target level of noise in the brain signals has been established.

The invention discloses a training method and device for an electroencephalogram data analysis model and electronic equipment. The method comprises the steps that electroencephalogram training data are collected; the electroencephalogram training data is electroencephalogram signals formed when the target object controls a cursor in a preset training graph to move according to a preset intention; analyzing the electroencephalogram training data based on the current analysis model to obtain a cursor brain control signal; the current analytical model is determined from a historical model library; determining a control result based on the current brain control proportion and the cursor brain control signal; training a preset model to obtain an electroencephalogram data analysis model, and storing the electroencephalogram data analysis model to a historical model library; meanwhile, the current brain control proportion is adjusted based on the control result, the process is repeated till training is finished, and the current analysis model after training is finished is determined as the target electroencephalogram data analysis model. According to the training method, the electroencephalogram data analysis model can be efficiently and conveniently trained, so that the model is periodically and rapidly updated, and the model in actual use can adapt to changes of physiological features of a user.

The invention discloses a grabbing method, device and equipment based on a motion intention and concentration degree and a medium, and the method comprises the steps: obtaining a training sample set of an intention level and a grabbing power level, and training a support vector machine model through the training sample set; performing standardization processing on the first energy ratio feature sequence, the first original feature sequence and the first intrinsic mode feature sequence by adopting a Z-Score algorithm to obtain a second energy ratio feature sequence, a second original feature sequence and a plurality of second intrinsic mode feature sequences; and performing classification decision by using the target support vector machine model to obtain an intention level, simultaneously obtaining a user concentration average value, and determining a target output holding power according to the user concentration average value and the intention level. And the stress state of the grabbed target is detected in real time, and PID operation is carried out. Fine hierarchical control of the grabbing force is achieved, the recognition precision of the grabbing intention is improved, and the problem that traditional brain control equipment grabs but cannot stably hold is solved by conducting anti-falling PID operation in real time.

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 embodiment of the invention provides a signal processing method based on a brain control system, the brain control system, a device and equipment, and relates to the technical field of brain-computer interfaces. The method comprises the steps that under the condition that a stimulation end displays a target interface, a user electroencephalogram signal is acquired; sending a to-be-executed instruction determined based on the electroencephalogram signal of the user to the controlled device, so that the controlled device collects a second image in real time in the process of executing an operation corresponding to the to-be-executed instruction, and sends the second image to the stimulation end; receiving a second image sent by the controlled equipment; and determining an execution result of the operation based on the second image, updating the second image as the first image under the condition that the execution result does not meet a preset condition, and returning to execute the step of acquiring the electroencephalogram signal of the user under the condition that the stimulation end displays the target interface until the execution result meets the preset condition. In this way, the visual demand of the user in a brain control related equipment scene can be effectively met.

The embodiment of the present disclosure provides a signal processing method based on a brain control system, a brain control system, a device and equipment, and relates to the technical field of brain-computer interface. The method comprises the following steps: acquiring a user electroencephalogram under the condition that a target interface is displayed at a stimulation end; sending a to-be-executed instruction determined based on the user electroencephalogram to a controlled device, so that the controlled device acquires a second image in real time in the process of executing an operation corresponding to the to-be-executed instruction, and sends the second image to the stimulation end; receiving the second image sent by the controlled device; determining an execution result of the operation based on the second image, and updating the second image to the first image and returning to the step of acquiring the user electroencephalogram under the condition that the target interface is displayed at the stimulation end if the execution result does not meet a preset condition, until the execution result meets the preset condition. In this way, the visual needs of the user in the brain control related device scene can be effectively met.

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 discloses a kind of transcranial magnetic stimulation magnetic therapy motor threshold test method and device, it is related to brain control technical field, the method includes: with the preset sampling rate to obtain the original electromyogram collected by electrode piece, original electromyogram is sequentially carried out high-pass filtering, low-pass filtering and the trap wave filtering of preset frequency, regression zero baseline processing;Control magnetic stimulation coil according to preset stimulation intensity to stimulate, determine the corresponding latency according to the data after this stimulation, determine the amplitude after this stimulation according to the wave crest and wave trough in the electromyogram after latency;According to current amplitude, determine the adjustment mode of stimulation intensity of next stimulation, determine the amplitude of adjusted stimulation intensity under the preset stimulation cycle, until the amplitude meets the measurement condition of motor threshold, and determine the corresponding motor threshold according to current stimulation intensity.The application solves the problem of low efficiency and low accuracy in the prior art during motor threshold test.

The invention provides a user intention recognition method and device and terminal equipment, and is suitable for the technical field of data processing, and the method comprises the steps: carrying out the space-time slicing and projection processing of a plurality of to-be-processed electroencephalogram signals, and generating the sequence information of a plurality of to-be-processed electroencephalogram signals; according to a preset mask ratio and a preset dynamic mask processing rule, performing mask processing on the multiple pieces of to-be-processed electroencephalogram signal sequence information to generate multiple pieces of electroencephalogram signal mask sequence information; and obtaining a target intention recognition model according to the multiple pieces of electroencephalogram signal mask sequence information and the initial intention recognition model. According to the method, the robustness and cross-user generalization ability of the model to complex noise such as vehicle vibration and electromagnetic interference are remarkably improved, low-delay and high-accuracy accurate recognition of the motor imagery intention of the driver is achieved, and stable and efficient decision support is provided for intelligent cabin non-inductive brain control and man-machine cooperation.

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.