59 results about "Brain waves" patented technology

An information processing system includes a brain wave detector and an information processing terminal. The brain wave detector detects a brain wave of a user, converts the detected brain wave into data in accordance with a conversion dictionary for converting characteristics of the brain wave into data processable in the terminal, and transmits the data to the terminal. In accordance with the received data, the terminal executes a corresponding processing corresponding to the data. Further, to the brain wave detector, the terminal transmits a group of data for instruction input used in software in operation in the terminal, during or after execution of a cooperation processing achieving a cooperation operation with the brain wave detector. The brain wave detector stores the received group of data into a storage, and executes a conversion processing by use of the group of data.

1. The name of the design product: brain wave detector (M2). 2. The use of the design product: for collecting brain waves. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1. 5. Perspective view 1 shows the shape of the bottom view, and the bottom view is omitted; perspective view 2 shows the shape of the top view, and the top view is omitted.

To provide an information processing device capable of appropriately evaluating the level of motor skill proficiency. [Solution] The control unit 12 (information processing device) according to this disclosure includes: a subject brainwave acquisition unit 121 that acquires subject brainwaves showing the brainwaves of a subject when the subject performs a predetermined movement; a subject motion acquisition unit 122 that acquires subject motions showing the actions of a subject when the subject performs a predetermined movement; and a proficiency evaluation unit 123 that, using a reference motion as the reference motion when performing a predetermined movement, evaluates the proficiency of the subject motion higher the smaller the amplitude of the subject brainwaves when the absolute value of the difference between the amount of movement of the reference motion and the amount of movement of the subject motion is less than a first threshold.

1. The name of the design product: brain wave data monitoring training instrument. 2. The use of the design product: instrument for brain wave data monitoring and recording, psychological monitoring, and brain wave feedback training. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The application discloses an electroencephalogram guided hip-knee-ankle orthopedic rehabilitation exoskeleton adaptive adjustment method, and relates to the technical field of intelligent rehabilitation engineering and biomedical engineering. On the basis of identifying the continuous electroencephalogram fluctuation characteristics in the intention transition period, the support response of the lower limb three joints is combined to construct a traceable transition identification zone. By extracting the driving dislocation point to set the joint response delay, the problem of early driving before the support is completed is avoided. The forward inclination trend is predicted in combination with the torso posture angle and the foot bottom pressure change, and a reverse support hierarchical matrix is dynamically arranged among the three joints to ensure the timing matching and compensation of the support forces among the joints when the abnormal driving trend occurs. Through the rhythmized inhibition and micro-assistance alternating mechanism, the joint output forms a flexible buffer gait, so that the gait stability, the neural participation degree and the safety of the patient in the rehabilitation training process are significantly improved.

1. The name of the design product: brain wave sensor. 2. The use of the design product: the design product is an electronic device, which is controlled by human brain waves. 3. The design points of the design product: shape and pattern. 4. The picture or photo that best indicates the design points: perspective view 1.

1. The name of the design product: non-invasive brain wave intelligent test ring. 2. The use of the design product: intelligent wearable device for detecting brain wave changes, using AI algorithm to analyze the collected signals through chips and sensors, and giving visual data. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

This application relates to the field of surgical diagnostic technology, specifically to a system for adjusting the ratio of intravenous and inhaled anesthetic drugs based on EEG trend-guided analysis. The system generates a standardized sequence set through data acquisition and preprocessing; an association feature construction module extracts association features between drug ratios and EEG trends based on time lag relationships; an influence strength coefficient calculation module combines the ratio structure entropy and EEG fluctuation coefficients to generate an EEG influence strength coefficient; a sensitivity judgment module calculates sensitivity features based on EEG intervals of patients with similar physical signs, combined with real-time fluctuations and influence coefficients; a parameter dynamic adjustment module dynamically optimizes the EEG analysis intervals and trend weights accordingly; and a ratio recommendation generation module generates multiple drug ratio options with expected EEG changes for selection. This system effectively overcomes the shortcomings of static thresholds in adapting to individual differences and intraoperative fluctuations, significantly improving the accuracy of drug ratio recommendations.

1. Name of the product in this design: Brainwave Tester. 2. Purpose of this design: For popularization, research and application of EEG bioelectrical testing and aEEG quantification technology. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This invention discloses an artificial intelligence-based method for simulating facial expressions in an interactive humanoid robot, belonging to the field of facial expression simulation technology. The method includes: acquiring and preprocessing user brainwave signals to generate power spectral density features; inputting the power spectral density features into a pre-trained emotion classification model to output the user's emotion category and intensity value; mapping the emotion category and intensity value to an emotional expression and outputting biomimetic muscle contraction commands; adjusting the facial muscle fibers of the humanoid robot according to the biomimetic muscle contraction commands to dynamically adjust the surface texture details of the humanoid robot and generate simulated micro-expressions; capturing user facial feedback data through camera eyes, evaluating the user's acceptance of the simulated micro-expressions based on the user's facial feedback data, and outputting an expression optimization strategy. This invention achieves bio-realistic micro-expressions in a humanoid robot through a dual closed-loop modeling approach of deformation matching and user feedback-driven expression optimization.

This invention relates to an intelligent sleep monitor. It uses detection devices to measure brainwave frequency, brain current, sleep posture, left and right eye voltage, and blood oxygen concentration. A central control device integrates and analyzes the data. The sleep cycle is segmented based on brainwave frequency and left and right eye voltage. Since different parts of the brain emit effective brain currents at different times during sleep, the device detects current at different brain locations based on different sleep periods, and weights the data used to determine sleep quality.

1. The name of the design product: brain-computer interaction decision-making cap (lightweight streamlined). 2. The use of the design product: equipment for brain wave signal acquisition and processing. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The embodiment of the present disclosure provides a non-invasive brain-computer interface system based on earphone real-time electroencephalogram monitoring-auditory stimulation, and relates to the technical field of neural engineering. The system comprises an in-ear earphone device and an external control device. The in-ear earphone device is used for collecting the brain wave signals of a user, transmitting the brain wave signals to the external control device, generating a sound stimulation signal based on a sound stimulation trigger instruction, and delivering the sound stimulation signal to the ear of the user for sound stimulation. The external control device is used for detecting whether there is a characteristic sharp-slow wave signal corresponding to absence epilepsy attack in the brain wave signals. When the characteristic sharp-slow wave signal corresponding to the absence epilepsy attack is detected, the target sharp-slow wave of the brain wave signals is extracted, a sound stimulation trigger instruction is generated based on the sharp wave component of the target sharp-slow wave, and the stimulation trigger instruction is transmitted to the in-ear earphone device. Real-time and accurate monitoring and non-invasive closed-loop intervention of absence epilepsy attack can be realized, the system has high safety, and is portable and comfortable.

The application discloses an intelligent learning suggestion recommendation method and system based on multi-modal emotion recognition, belongs to the technical field of intelligent learning monitoring, and through collection of multi-modal emotion feature data, brain wave fluctuation characteristics are supplemented by facial expression features and voice features, interference of pure brain wave fluctuation characteristics on emotion recognition is avoided, accuracy of emotion recognition is greatly improved, meanwhile, a plurality of emotion recognition reference sources are formed by using historical brain wave fluctuation data, brain wave fluctuation characteristic data of a target learner is compared with each emotion recognition reference source, the most similar target reference source is obtained, characteristic enhancement of the target reference source on the brain wave fluctuation characteristic data of the target learner is carried out, different target multi-modal emotion recognition models are adopted for specific emotion recognition for different emotion types, more accurate emotion state categories are obtained, and generalization ability of emotion recognition for various different emotions is greatly improved.

1. The name of the design product: intelligent head ring (star sense). 2. The use of the design product: for the collection and analysis of brain waves. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

This invention provides a method for improving the horizontal baseline of an ABR (Auditory Brainstem Sensor) electroencephalogram (ABR), relating to the field of EEG monitoring technology. The key feature is that the ABR-1 instrument is used, and the baseline is changed to a horizontal baseline via a 6-pin integrated circuit. The ABR-1 instrument can acquire seven peak images. The advantages of this invention are: changing the baseline to a horizontal position reduces the impact of carrier waves on the final image, thus providing a clearer view of the detected image, facilitating physician interpretation, and improving overall diagnostic efficiency.

The application discloses an emotion and concentration intelligent recognition method based on multi-frequency brain wave characteristics, comprising the following steps: collecting brain wave data, generating a brain wave data set through standardization processing; performing short-time Fourier transform, constructing a frequency spectrum sequence, calculating the coupling index of different frequency bands, and constructing a frequency band coupling matrix; proposing a frequency coupling clustering algorithm, performing a frequency spectrum clustering operation, and generating a plurality of frequency spectrum resonance clusters; inputting into an improved EEGNet model, performing a cluster state feature extraction operation, and generating a multi-cluster joint vector; constructing a fusion state vector, and outputting emotion recognition results and concentration recognition results through a state discrimination operation; constructing a frequency band correction instruction according to the result of judging state jump information, and outputting the corrected emotion recognition results and concentration recognition results. The application realizes deep perception and fusion modeling of multi-frequency brain wave data, and can accurately and stably recognize the emotional state and concentration degree of an individual in a non-static environment.

This invention introduces advanced wearable EEG technology designed for continuous monitoring and analysis of brain activity. It addresses key challenges in signal acquisition, noise reduction, motion artifacts, and long-term user comfort through the use of flexible, soft conductive materials and an innovative electrode architecture featuring an internal cavity that ensures stable skin contact when placed over the mastoid bone behind the ear. The system supports multiple lightweight and adaptable form factors, including ear-cuffs and EEG patches suitable for overnight and extended wear. Optimized shielding, flexible materials, and user-centered mechanical design enable reliable, high-quality brainwave data capture in real-world conditions, overcoming the limitations of traditional rigid EEG systems. This platform enables continuous brainwave monitoring for applications such as cognitive research, neurofeedback, emotional state detection, mental health monitoring, and preventive healthcare. By combining comfort, adaptability, and signal reliability, this invention represents a significant advancement in wearable brain health monitoring technology.

Systems and methods discussed herein can be used to augment or induce brainwave behavior, such as using auditory stimulus, in an example, ambient acoustic information can be received, selectively modulated, and presented to a user in a substantially continuous manner. A low frequency portion of received ambient acoustic information can be modulated and then combined with a high frequency portion of the ambient acoustic information to produce a combined signal. The combined signal can be provided to the user as an auditory stimulation signal.

The application provides a kind of antenatal education interaction method based on vocal singing feedback training interface, the method is based on the user brain wave data set, the activity of different frequency bands is analyzed, the intensity of each wave band is identified and quantified, and the current emotional state of the user is judged by comparing with the preset threshold, and the time point of emotional change is recorded, the emotional analysis result is obtained, the rhythm, volume and spatial sound effect setting of music are adjusted, the dynamic adjustment of music output parameter is realized, the emotional intervention demand is optimized, and according to the change of user brain wave, the emotional improvement and user satisfaction are evaluated, the improvement effect of music intervention on user psychological state can be measured, the effectiveness of feedback closed loop is ensured, and more efficient, accurate and psychological demand meeting music feedback training effect is provided for user.

The application discloses a sleep health management system, method and computer device. The sleep health management system comprises a data acquisition module and a sleep quality evaluation module. The data acquisition module is configured to acquire electroencephalogram data of a target object and a sleep quality index scale about the target object. The sleep quality evaluation module is configured to obtain a subjective sleep quality evaluation report about the target object according to the sleep quality index scale, obtain an objective sleep quality evaluation report about the target object according to the electroencephalogram data, and obtain a sleep quality evaluation report about the target object according to the subjective sleep evaluation report and the objective sleep evaluation report. The sleep health management system in the embodiment of the application combines a subjective and objective double evaluation system, and the objective evaluation is evaluated by using electroencephalogram data, so that the accuracy of positioning a sleep disorder type is improved.

The embodiment of the present disclosure provides a narcolepsy real-time monitoring and feedback device, and relates to the technical field of intelligent monitoring, which comprises a head-mounted device and a wearable device, wherein the head-mounted device is worn on the head of a patient and is used for collecting the brain wave signal of the patient; receives a motion signal and judges the drowsy state of the patient in combination with the brain wave signal and the motion signal, generates a stimulation trigger instruction based on the drowsy state, and sends the stimulation trigger instruction to the wearable device; the wearable device is worn on the skin of the patient and is used for collecting the motion signal of the patient and sending the motion signal to the head-mounted device; receives the stimulation trigger instruction and generates a stimulation signal based on the stimulation trigger instruction and stimulates the patient to wake up. The present application can realize narcolepsy real-time monitoring and immediate intervention.

1. The name of the design product: brain wave detector (S20). 2. The use of the design product: for collecting brain waves. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1. 5. Perspective view 1 shows the left view, so the left view is omitted; perspective view 1 shows the shape of the bottom view, so the bottom view is omitted; perspective view 2 shows the right view, so the right view is omitted; perspective view 2 shows the shape of the top view, so the top view is omitted.

The application relates to the technical field of physiotherapy, in particular to a non-contact electromagnetic physiotherapy method and system for brain waves. The non-contact electromagnetic physiotherapy system for brain waves comprises an electroencephalogram signal acquisition module, an electroencephalogram signal feature data construction module and a pulse electromagnetic field physiotherapy module. The electroencephalogram signal data is mapped to a pulse phase time axis, and the electroencephalogram signal data sequence is corrected in combination with the repetition component constraint of the pulse phase label, so that the periodic interference component introduced in the process of the action of the pulse electromagnetic field can be effectively inhibited, the influence on the electroencephalogram acquisition result and the frequency band energy calculation is reduced, and the accuracy and stability of the electroencephalogram signal feature data extraction are improved. Based on the corrected electroencephalogram signal feature data, a reinforcement learning method is used to construct a driving parameter set of an electromagnetic field construction unit, and a reward value is calculated in combination with a standard curve of brain wave sleep to update a driving parameter construction network.

An expression generation device includes a memory and processing circuitry configured to acquire, as processing targets, a face feature related to a face image of a person and a brainwave of the person, estimate an expression feature of the acquired brainwave by using a first model obtained by performing learning on a relationship between a brainwave and an expression feature related to an expression image, which is an image representing an expression, and generate a face image with an expression by using the face feature and the estimated expression feature.

This invention discloses a method and system for assessing the mental health of primary and secondary school students based on multimodal physiological data, relating to the field of mental health technology. The method includes the following steps: S1, data acquisition, which includes synchronously collecting multimodal physiological data of primary and secondary school students during the completion of a mental health scale test. The multimodal physiological data includes at least electroencephalogram (EEG) signals, heart rate signals, blood oxygen saturation signals, and facial video streams; S2, data synchronization, which includes segmenting the multimodal physiological data using the items in the mental health scale as time nodes, and using the time axis of the EEG signals as a reference. This method and system for assessing the mental health of primary and secondary school students based on multimodal physiological data, by integrating four modalities of data—EEG, heart rate, blood oxygen, and micro-expressions—comprehensively assesses mental health status from multiple dimensions, significantly improving the comprehensiveness and accuracy of the assessment.

1. The name of the design product: brain-controlled unmanned aerial vehicle control head-mounted device. 2. The use of the design product: a device for collecting and processing brain wave signals to control unmanned aerial vehicles. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The application discloses a state evaluation and closed-loop feedback regulation method and system, the method comprising: step 1: acquiring the multi-modal physiological data of a user; step 2: performing feature extraction and attention allocation, outputting an original prediction score; weighting calculation is performed on the dynamic correction coefficient and the original prediction score, and a continuous instantaneous state score is obtained; step 3: based on the instantaneous state score, a feedback guide instruction for the multi-sensory dimensions of the user is generated and executed; when the instantaneous state score reaches a preset target state threshold, a sensory self-adaptive extinction mechanism is triggered. The application collects the brain waves and multi-modal physiological signals of a user, performs comprehensive evaluation by using a special interactive feedback program, converts boring brain electrical data into visual, audible and tactile perception real-time dynamic feedback, and realizes closed-loop digital neural regulation.