14 results about "Electroencephalography" patented technology

1. The name of the design product: double-channel electroencephalogram head ring. 2. The use of the design product: used for collecting the double-channel electroencephalogram signals (such as the brain electrical characteristics related to concentration and relaxation) of the wearer, as well as physiological parameters such as blood oxygen saturation and heart rate, to provide data reference for daily health monitoring, concentration training, sleep quality tracking and other scenarios. 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 discloses a dance rehabilitation training system integrating EEG and EEG sensors. The system includes an EEG headband and EEG sensors, each with built-in EEG and EEG acquisition modules. The dance rehabilitation training process includes: device wearing and initialization; personalized physiological baseline acquisition; simultaneous acquisition and preprocessing of dual-modal signals; multimodal feature fusion and emotion recognition, obtaining emotional state through dual-model fusion and smoothing; personalized safety threshold decision-making and intervention level determination; multi-channel dance intervention execution; emergency stress handling mechanism; closed-loop iteration and real-time control; and finally, generation of an evaluation report. This invention solves the problems of static intervention parameters, low feedback accuracy, and easy triggering of stress responses in traditional dance training by using dual-modal signal fusion, personalized safety threshold modeling, and closed-loop real-time control, achieving real-time, precise, and safe personalized rehabilitation intervention.

1. The name of the design product: digital electroencephalograph (NL-4208). 2. The use of the design product: as a device for electrotherapy rehabilitation, for measuring brain electrical signals, and assisting in the diagnosis of brain diseases. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

This invention relates to a real-time assessment and prediction method for mental fatigue based on multimodal signals and deep learning, addressing the problem that existing mental fatigue monitoring technologies rely heavily on static feature analysis of single physiological signals. First, it integrates dynamic brain function network features from electroencephalography (EEG) with time-frequency domain features of EEG to achieve feature fusion driven by multi-domain EEG features. Further, it automatically associates PPG signals with fatigue stages based on weighted K-means clustering, using the center value of heart rate to calibrate the fatigue level, eliminating the need for subjective labeling and achieving objective recalibration of mental fatigue levels. Finally, it uses a temporal deep convolutional network model to achieve real-time assessment and prediction of mental fatigue levels. This invention can accurately assess and predict mental fatigue levels in real time, and issue warning signals to operators with high levels of mental fatigue, providing technical support for real-time monitoring and warning of mental fatigue, and further reducing the rate of operational errors caused by mental fatigue.

1. The name of the design product: EEG acquisition impedance detection graphical user interface of electronic equipment. 2. The use of the design product: for displaying interface content. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best shows the design points: front view. 5. The electronic equipment is a conventional design, and other views are omitted. 6. The use of the graphical user interface: for impedance detection graphical user interface in EEG acquisition. The human-computer interaction mode: human-computer interaction can be realized by mouse dragging, sliding, placing and clicking. The front view is the main interface of the EEG acquisition impedance detection operation interface, the interface change state diagram 1 is the electrode configuration file import interface displayed after clicking the Select option in EEG_V1.0-25fd0 in the front view state, the interface change state diagram 2 is the electrode configuration file selection interface displayed after clicking the Import option in the pop-up window in the interface change state diagram 1 state, the interface change state diagram 3 is the interface displayed after clicking a specific electrode configuration file in the interface change state diagram 2 state, the interface change state diagram 4 is the electrode configuration file import success interface displayed after clicking the select folder option or double-clicking the selected file in the interface change state diagram 3 state, the interface change state diagram 5 is the interface displayed after checking the square option box right of EEG_32_V1.0 in the pop-up window in the interface change state diagram 4 state, and the interface change state diagram 6 is the impedance preview interface displayed after clicking the preview icon right of EEG_32_V1.0 in the interface change state diagram 5 state.

This invention relates to the field of perioperative neurological monitoring technology, and particularly to a postoperative cognitive quantitative assessment system and method that integrates electroencephalography (EEG) and near-infrared spectroscopy. The system includes: a baseline anchoring module, which extracts preoperative scalp EEG and near-infrared spectral signal features, concatenates them into a first vector, and calculates the covariance to generate a preoperative baseline manifold; a matrix construction module, which generates a cross-modal manifold matrix based on real-time bimodal signals within a time window; a feature extraction module, which calculates the geodesic distance between the cross-modal manifold matrix and the preoperative baseline manifold, projects it onto the tangent space using a logarithmic mapping to generate a tangent matrix, and combines the geodesic distance to generate a topological feature vector; a quantitative assessment module, which inputs the vector into a softmax function layer to output a probability distribution and calculates a cognitive assessment index; and a closed-loop intervention module, which outputs an intervention command when the index meets preset conditions and provides a timestamp to reset the time window. This system achieves closed-loop manifold-based assessment and dynamic monitoring of postoperative cognitive status.

A system for testing and training a brain capability of planning and executing motion activity for a user who has sustained a functional movement deficit; said system comprising an electroencephalographic sensor arrangement attachable to the head of said user; a processor configured for receiving and analyzing electroencephalographic signals obtained from said user during visualization of an action; a memory storing instructions when executed by said processor for instructing said user to visualize executing said motion action; measuring electroencephalographic signals on said electroencephalographic sensor arrangement; calculating at least one characteristic selected from the following: a concentration index; a motor control index; an alertness index; providing said user with a feedback pattern based on at least one said concentration, motor control, alertness and motion readiness; recurring steps to be if needed.

A brain-controlled robotic prosthetic arm integrates a hybrid neural network model, combining Convolutional Neural Networks and Recurrent Neural Networks for advanced electroencephalography signal processing. The system enables users to control the prosthetic arm using brain signals, with supplementary integration of manual operation. The modular design allows for user-attachable extension parts, customizable configurations, and 3D-printed components tailored to the user's anatomy. Each finger is actuated by an independent micro linear motor, providing precise control and lifelike movements. The prosthetic arm incorporates a sensory feedback system, with flexible material on the fingertips embedded with sensors that detect tactile forces, temperature, and pressure. This feedback is delivered in real time to enhance the user's perception of interaction with their environment. The hybrid neural network utilizes transfer learning and Generative Adversarial Networks for data augmentation, addressing challenges in electroencephalography signal variability and data scarcity to improve classification accuracy.

The invention discloses a multi-modal brain function connection brain tumor postoperative post-traumatic stress disorder classification system and method. The system comprises a multi-modal data acquisition module, a multi-map brain network construction module, a multi-kernel map convolution feature extraction module, a dynamic prediction model construction module and an interpretability visualization module. Structural magnetic resonance, functional magnetic resonance and electroencephalogram multi-modal data are integrated, a functional connection network is constructed based on multi-graph division, and multi-scale features are extracted by using a multi-kernel graph convolutional network, so that high-precision PTSD classification and prediction are realized. The problems that existing brain tumor postoperative PTSD diagnosis depends on subjective evaluation and lacks objective biological markers are solved, an integrated solution of early recognition, individualized diagnosis and interpretable analysis is provided, and the diagnosis precision and clinical effectiveness of brain tumor postoperative mental complications are remarkably improved.

This application provides a method, system, and medium for decoding ultrasound-modulated electroencephalogram (EEG) signals, relating to the field of brain-computer interface technology. The decoding method for ultrasound-modulated EEG signals is applied to a decoding system for ultrasound-modulated EEG signals; it includes: acquiring scalp EEG signals from a specific target area to obtain a USMEEG signal; performing preprocessing, robust decoding, and signal reconstruction operations on the USMEEG signal to obtain the corresponding brain power signal; the robust decoding operation includes extracting the instantaneous complex response amplitude at the PRF of each frame obtained after framing and windowing. This system can improve the accuracy of decoding ultrasound-modulated EEG signals and enhance the spatial resolution of scalp EEG.

The application discloses a method and system for predicting the phase of electroencephalogram signals based on improved learning time domain. It comprises: collecting and preprocessing electroencephalogram signals, extracting alpha band signals; peak detection is performed on the alpha band signals, and an initial oscillation period estimation value is calculated based on the interval between adjacent peaks; in the continuous prediction process, the oscillation period estimation value is adaptively updated by using a dynamic updating mechanism such as recursive weighting according to the newly detected peak interval; and the target phase is predicted by period advancing based on the updated period and the time of the latest peak. The application also provides a system for implementing the method. By dynamically updating the period parameter, the application overcomes the defects of the prior art which relies on fixed frequency or period assumptions, significantly enhances the adaptability to non-stationary electroencephalogram signals, improves the phase prediction accuracy and stability, and has low computational complexity and high real-time performance, and is especially suitable for closed-loop neuromodulation applications such as transcranial magnetic stimulation phase locking.

This application provides a method and system for hierarchical artifact suppression and signal quality assessment of electroencephalogram (EEG) signals, including: synchronously acquiring multimodal signals in a vehicle-mounted motion scenario, including EEG signals, eye-tracking signals, and multi-axis motion signals; removing artifacts from the EEG signals according to a constructed three-level hierarchical artifact suppression architecture; dividing the EEG signals after hierarchical artifact suppression into time windows and calculating multi-dimensional quality indicators, and adaptively weighting and scoring the multi-dimensional quality indicators based on the vehicle motion state quantified by multi-axis motion signals, with the generated EEG signal quality score used to adjust artifact suppression parameters; and labeling or filtering EEG signal segments according to the quality score results to output EEG signals that meet the requirements of the vehicle-mounted scenario. This application achieves precise, dynamic, and practical vehicle-mounted EEG signal processing through the collaborative design of hierarchical artifact suppression, motion artifact quantification, dynamic quality assessment, and adaptive closed-loop optimization.

This invention relates to the field of mental health diagnosis and treatment technology. The invention provides a method and device for mental health monitoring and auxiliary diagnosis based on multimodal recognition. The method includes: a data processing unit receiving and preprocessing multimodal data from eye movement, electroencephalography (EEG), facial recognition, speech, scales, and text; constructing a comprehensive feature set reflecting attentional state, physiological arousal, emotional expression, and subjective risk by extracting quantifiable features from the multimodal data; concatenating the comprehensive feature set into an input vector, inputting it into a local multi-disease MLP, outputting five risk probabilities, and performing structured scale security calibration; calculating independent scores for each channel based on the multimodal features, and performing weighted fusion with the MLP output, combining risk thresholds and self-harm intention escalation rules to generate a risk level; and generating an auxiliary assessment report through a multi-agent collaborative review mechanism. This approach improves the accuracy, interpretability, and traceability of mental health auxiliary assessment results.

The application relates to the technical field of electroencephalogram monitoring and processing, in particular to a three-dimensional motion intention decoding method based on electroencephalography source localization. The method comprises the following steps: acquiring electroencephalogram data of a target user during three-dimensional motion imagination, wherein the three-dimensional motion imagination comprises imagined activities of three-dimensional limb movement; determining source estimation data of a motion brain area based on the electroencephalogram data of the target user during the three-dimensional motion imagination, wherein the motion brain area is part of all brain areas in the whole brain; combining the source estimation data of the motion brain area and a motion coordinate model of the target user during the three-dimensional motion imagination to decode corresponding coordinate information of the target user during the three-dimensional motion imagination; wherein the coordinate information serves as indication information for position movement of an external device. Through the motion intention decoding mode and the structured training scheme based on electroencephalography source localization, fine positioning and continuous movement of a mechanical arm can be finally realized under the support of an electroencephalography device.