27 results about "Brain control" patented technology

The invention discloses an integrated brain-computer control system and method for a non-programmable user, and the system comprises a stimulation normal form generation module which is used for providing a graphical user interface, receiving stimulation normal form configuration information inputted by the user through the graphical user interface, and generating a stimulation normal form which is used for visually inducing the electroencephalogram potential of the user, generating electroencephalogram characteristics which can be captured; the electroencephalogram acquisition module is used for acquiring electroencephalogram data; the central scheduling and management module is connected with the electroencephalogram acquisition module and is used for storing electroencephalogram data; the signal processing module is used for analyzing the read electroencephalogram data, identifying a control intention and sending the control intention to the central scheduling and management module; and the equipment control module is used for mapping the read control intention into an equipment control command in a predetermined format, and the equipment control command is used for controlling the controlled equipment. The invention aims to reduce the design and use threshold of a brain-computer control system, so that a non-programming user can quickly design a stimulation normal form, collect data and realize real-time brain control on external equipment.

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 brain control interface system 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 memory configured to store processing methods associated with different light scenes, one or more processor configured to: select, from the processing methods stored in the memory, a processing method in accordance with the current light scene, apply the selected processing method to obtain and / or process the brain signals, derive a control command and / or a mental state of the user from the brain signals, and control the controllable device based on the derived control command and / or the derived mental state.

The invention discloses a smart home interaction method and system based on non-intrusive electroencephalogram / brain magnetic signal identity authentication and a storage medium, and relates to the technical field of smart home. Constructing a smart home integrated interaction model; the method comprises the following steps: acquiring a control instruction signal of a current user in a multi-modal fusion non-invasive mode, converting the control instruction signal into a digital signal, extracting electroencephalogram / electroencephalogram signal features, extracting an electroencephalogram / electroencephalogram signal feature template of an authorized user from an electroencephalogram / electroencephalogram signal database, and comparing the electroencephalogram / electroencephalogram signal features with the electroencephalogram / electroencephalogram signal feature template; if the comparison result is higher than a preset threshold value, marking that the identity authentication of the current user is passed, receiving electroencephalogram / brain magnetic signal characteristics of the current user by a brain control instruction structure, and converting the electroencephalogram / brain magnetic signal characteristics into a smart home interaction instruction; and after receiving the instruction, the smart home integrated interaction model controls the smart home to start and stop, and feeds back an operation result to the user to complete a brain control operation process. According to the invention, safe, convenient and accurate non-invasive brain control of the smart home is realized.

The invention provides a multi-Agent cooperative control method and system, computer equipment and a storage medium, and belongs to the field of agent control, and the method comprises the steps: obtaining user intention data; determining an action parameter corresponding to the intention category based on a predefined intention-to-action mapping rule, and constructing a multi-agent brain control action instruction; obtaining a current environment state, retrieving memory entries in a brain-like memory library according to the current environment state, obtaining adjacent tracks and determining candidate actions; according to the current environment state and the candidate action, an intelligent control action instruction of the multi-agent is constructed, and memory entries associated with states, actions, track fragments and track scores are pre-stored in a brain-like memory library; and performing gating switching according to the brain control activation degree, and performing linear weighting on the intelligent control action instruction and the brain control action instruction to construct a fusion action instruction. According to the method, two action instructions are fused to realize multi-agent control, so that the robustness, the response speed and the environmental adaptability of the strategy are improved.

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.

Universal switch modules, universal switches, and methods of using the same are disclosed, including methods of preparing an individual to interface with an electronic device or software. For example, a method is disclosed that can include measuring brain-related signals of the individual to obtain a first sensed brain-related signal when the individual generates a task-irrelevant thought. The method can include transmitting the first sensed brain-related signal to a processing unit. The method can include associating the task-irrelevant thought and the first sensed brain-related signal with N input commands. The method can include compiling the task-irrelevant thought, the first sensed brain-related signal, and the N input commands to an electronic database.

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 invention relates to an electroencephalogram acquisition system supporting visual monitoring and real-time brain control interaction. The electroencephalogram acquisition system is used for overcoming the defects in the prior art in the aspects of high-quality acquisition, real-time processing, data visualization, external interaction control and the like of electroencephalogram signals. According to an acquisition module of the system, 16-channel electroencephalogram signals are synchronously acquired through cascade connection of two ADS1299 chips, the chips are connected in a daisy chain and standard cascade connection combined mode, the number of digital signal interface lines is reduced, and independent configuration and synchronous control capacity of each chip are reserved. The micro-control module is realized by using an embedded program based on a FreeRTOS real-time operating system, communicates with the acquisition module, receives and caches electroencephalogram data, packages the electroencephalogram data and wirelessly sends the electroencephalogram data to the upper computer module. And the upper computer module carries out real-time analysis, reconstruction and visual display on the received electroencephalogram data. And the interaction control module extracts and identifies electroencephalogram characteristics based on analysis and reconstruction results of the upper computer module, and sends a control instruction to external equipment to realize closed-loop brain control interaction.

A closed-loop system for asynchronous brain control of at least one task includes a brain state decoder configured to decode neural signals of a user into control signals for controlling the at least one task, a task interface module configured to transmit the control signals to the at least one task, store state information including a series of messages about each of the at least one task, and select one message of the series of messages about the at least one task for transmission to the user, and a brain state encoder configured to map the one message received from the task interface module into a brain state composite image for transmission to the user.

Universal switch modules, universal switches, and methods of using the same are disclosed, including methods of preparing an individual to interface with an electronic device or software. For example, a method is disclosed that can include measuring brain-related signals of the individual to obtain a first sensed brain-related signal when the individual generates a task-irrelevant thought. The method can include transmitting the first sensed brain-related signal to a processing unit. The method can include associating the task-irrelevant thought and the first sensed brain-related signal with N input commands. The method can include compiling the task-irrelevant thought, the first sensed brain-related signal, and the N input commands to an electronic database.

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 relates to a transparency modulation brain-computer interface control system based on cross-modal visual fusion, which is composed of a visual perception and stimulation module, a signal processing and intention analysis module and a control execution module, and the control method comprises the following steps: 1. environment perception; 2. stimulation generation; 3. electroencephalogram acquisition; 4. signal processing and intention analysis; 5. channel contribution degree weighted calculation; 6. sliding time window decision; and 7. control instruction output. The application has the beneficial effects that: through the fusion of environment perception and visual stimulation, the cognitive burden of the brain control system in the double-screen interface switching mode is avoided, intuitive and consistent human-computer interaction is realized; through the transparency modulation mode embedded stimulation signal, the visual information utilization rate is improved and the visual interference is reduced; the decoding method of channel statistical modeling and the time window fusion mechanism are introduced, the recognition stability and the anti-interference ability are improved under the condition of weak evoked signals; through the event driving and confidence gating mechanism, the reliability and safety of the system control are improved.

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 invention discloses a self-adaptive brain control artificial limb method based on space-time diagram decoding and online reinforcement learning. The method comprises the steps that S1, electroencephalogram time sequence data collected through a multi-channel electroencephalogram electrode are obtained; s2, constructing an electroencephalogram spatial-temporal feature map corresponding to the target angle sequence according to the electroencephalogram time sequence data; s3, constructing a space-time diagram convolutional network model; s4, taking the electroencephalogram spatial-temporal feature map as an input feature, taking the target angle sequence as a feature tag, and performing model training on the constructed spatial-temporal map convolutional network model to obtain an optimal spatial-temporal map convolutional network model; and S5, constructing a reinforcement learning model, and realizing adaptive control of the brain-controlled intelligent artificial limb according to the output of the optimal space-time diagram convolutional network model. The problem that according to an existing method, control is simplified into discrete instruction classification, and a continuous and fine movement track cannot be generated is solved. Meanwhile, the spatial topological characteristics of the electroencephalogram signals are not sufficiently utilized, and the self-adaptive capability depends on heavy manual calibration.

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 discloses a method and a device for testing a motion threshold value of transcranial magnetic stimulation magnetic therapy, and relates to the technical field of brain regulation and control, the method comprises the following steps: acquiring an original electromyographic signal collected by an electrode slice at a preset sampling rate, and sequentially performing high-pass filtering, low-pass filtering, notch filtering with a preset frequency and zero baseline regression processing on the original electromyographic signal; controlling the magnetic stimulation coil to stimulate according to preset stimulation intensity, determining a corresponding incubation period according to data after the stimulation, and determining the amplitude after the stimulation according to a wave crest and a wave trough in the electromyographic signal after the incubation period; and determining an adjustment mode of the stimulation intensity of the next stimulation according to the current amplitude, determining the amplitude of the adjusted stimulation intensity under a preset stimulation cycle until the amplitude meets a measurement condition of a motion threshold value, and determining a corresponding motion threshold value according to the current stimulation intensity. According to the invention, the problems of low efficiency and low accuracy during motion threshold testing in the prior art are solved.

The application discloses a brain control target control method and device based on an ECoG signal, and acquires real-time electroencephalogram signals collected through predetermined channels in ECoG electrodes, wherein the predetermined channels are selected according to the control contribution of the electroencephalogram signals collected by each channel to the brain control target; the real-time electroencephalogram signals are input to a preset coordinate decoding model and a speed decoding model to obtain decoded coordinates and decoded speed of the brain control target, wherein the coordinate decoding model and the speed decoding model are trained by the electroencephalogram signals of the predetermined channels and the corresponding coordinates and speed of the brain control target; and the running track of the brain control target is generated according to the decoded coordinates and the decoded speed, so that the control strategy optimization of the brain control target is completed according to the ECoG signal, and the accurate execution of the brain control instruction is ensured.

The invention discloses a neural signal processing method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring a tested neural signal; the neural signal is input into a first decoding model for decoding, a first decoding result is obtained, and the first decoding model is obtained through training according to historical brain control data of a subject; in response to the decoding demand change signal, training a second decoding model according to the neural signal and the first decoding result to obtain a target decoding model, the second decoding model being constructed based on the changed decoding demand; and in response to the obtained target decoding model, inputting the neural signal into the target decoding model for decoding to obtain a second decoding result. The technical problem that in the prior art, a decoding model trained based on historical data is low in accuracy is solved.

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.