52 results about "Eeg analysis" patented technology

The invention relates to a closed-loop multi-guide multi-mode time domain interference electrical stimulation system and method. The method comprises the steps that a software part determines stimulation parameters; the automatic electrode position adjusting device adjusts the spatial position of the multi-conductive-electrode device, and the multi-conductive-electrode device stimulates the position of a target spot; the current output precision and impedance monitoring module monitors the precision and impedance information of electrode output current, and the core main control and functional circuit module adjusts a control signal according to the precision and impedance information; when the multi-conducting-electrode device conducts stimulation, the electroencephalogram device synchronously collects electroencephalogram signals and uploads the electroencephalogram signals to the electroencephalogram analysis algorithm module of the software part, the electroencephalogram analysis algorithm module evaluates and feeds back the effectiveness and the effect of stimulation, and the time domain interference electrical stimulation function module and the target area dynamic adjustment algorithm module optimize stimulation parameters according to the effectiveness and the effect of stimulation. Through the arrangement, a real-time closed-loop feedback control system is formed, continuous monitoring, real-time optimization and adjustment of electrical stimulation parameters and dynamic adjustment of a stimulated brain target region are realized, and the accuracy and safety of current output are ensured.

The present invention relates to an EEG analysis method and apparatus for diagnosing depression, and the method includes the steps of: generating sounds so that repetitive acoustic stimuli may be transferred to a subject; acquiring EEG signals measured from the subject in synchronization with the acoustic stimuli; calculating P300 latency using the acquired EEG signals; and providing depression diagnosis information for the subject on the basis of the calculated P300 latency.

The present invention describes an artificial intelligence (AI) enabled electroencephalography (EEG) system that integrates Pathway Hierarchical Adaptive Referencing (PHAR) for localized signal detection, large language models (LLMs) for automated EEG reporting, and Internet of Medical Things (IoMT) connectivity for adaptive neuromodulation control. The system can also deliver transcranial electrical stimulation (tES) pulses and function as an electrical impedance tomography (EIT) system. PHAR employs a multi-layered multiplexer hierarchy and adaptive referencing topologies to optimize EEG signal acquisition and spatial resolution. LLM integration enables automated generation of human-readable EEG reports. IoMT connectivity allows closed-loop neuromodulation, where real-time EEG analysis guides the adjustment of stimulation parameters. The system can deliver tES pulses and perform EIT expands its functionality, allowing for targeted neuromodulation and impedance-based brain imaging. This integrated system revolutionizes EEG-based diagnostics, treatment, and research in neurology and neuroscience, offering a comprehensive and versatile tool for understanding and modulating brain function.

Disclosed are a method and an apparatus for classifying patients with brain disorders based on EEG analysis. The method for classifying patients with brain disorders based on EEG analysis includes: (a) receiving an EEG data set with a clinical diagnosis label, wherein the EEG data set includes a plurality of EEG signals and age information; (b) augmenting the EEG data set through a deep learning-based screening model, and augmenting the EEG signals and the age information in different schemes; (c) training the deep learning-based screening model to classify EEG data of patients into a target clinical diagnosis label by using the augmented EEG signals and age information; and (d) predicting a brain diagnosis label of brain disorders by applying EEG data of patients to the trained deep learning-based screening model.

The invention provides a brain wave detection data processing method and system based on AI, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly, obtaining an original electroencephalogram signal set composed of a plurality of segments of electroencephalogram signal sequences which are continuously collected through multiple channels and have time stamps, and then carrying out the quality optimization processing of the original electroencephalogram signal set; the method comprises the following steps: acquiring an effective electroencephalogram signal set according with a detection standard, then performing feature extraction processing on the effective electroencephalogram signal set to obtain an electroencephalogram feature combination reflecting a neural activity mode, and then calling a pre-trained electroencephalogram analysis model to perform mode recognition processing on the electroencephalogram feature combination to obtain a neural activity model. An electroencephalogram detection result containing the abnormal activity time period identifier and the corresponding brain region positioning information is generated, finally, an electroencephalogram processing instruction containing space-time information is generated based on the electroencephalogram detection result and sent to the target device to trigger response operation, and the accuracy and efficiency of electroencephalogram detection are improved.

The invention discloses an EEG intelligent agent automatic analysis method based on a large language model, which takes the large language model as a strategy engine, autonomously understands user analysis intentions and intelligently decomposes tasks, and further dynamically schedules and collaboratively integrates analysis resources including traditional feature engineering, diversified deep learning models and an external knowledge base. According to the method, end-to-end automatic coordination and execution of complex EEG analysis tasks such as signal preprocessing, feature extraction, event positioning, classification diagnosis, emotion recognition, sleep staging and the like can be realized, the limitation of a single detection or classification task is broken through, and through context perception and flexible space-time analysis capability, the accuracy of the EEG analysis is improved. And multitask and continuous deep reasoning and interpretation can be carried out on the complex EEG data. According to the method, the general planning and reasoning capability of a large language model is deeply fused with a special analysis model in the EEG field, so that the automation level, flexibility and clinical application potential of EEG analysis are remarkably improved.

The invention discloses an intracranial electroencephalogram epileptic seizure early warning method based on multipath map brain network modeling, and belongs to the technical field of electroencephalogram analysis. The method comprises the following steps: providing a multi-path map brain network modeling framework for epileptic seizure prediction; the multi-channel brain map is constructed based on three cross-frequency coupling (CFC) relationships in neuroscience, a frequency band in each channel of the electroencephalogram signal forms nodes of the map, and the three CFC relationships form edges of the map; and designing a multi-path brain map learning network with a joint fusion module to learn modes in the multi-path brain map to form a final representation. According to the method, the iEEG signals can be analyzed, the modes in the iEEG signals can be mined by utilizing the designed multi-channel brain map learning network, uniform characterization is formed, and the accuracy of epileptic seizure prediction is improved.

Disclosed are a method and an apparatus for classifying patients with brain disorders based on EEG analysis. The method for classifying patients with brain disorders based on EEG analysis includes: (a) receiving an EEG data set with a clinical diagnosis label, wherein the EEG data set includes a plurality of EEG signals and age information; (b) augmenting the EEG data set through a deep learning-based screening model, and augmenting the EEG signals and the age information in different schemes; (c) training the deep learning-based screening model to classify EEG data of patients into a target clinical diagnosis label by using the augmented EEG signals and age information; and (d) predicting a brain diagnosis label of brain disorders by applying EEG data of patients to the trained deep learning-based screening model.

The invention provides an automatic safe driving cross-scene alertness detection method and device based on EEG analysis, and relates to the technical field of intelligent driving. The embodiment of the invention provides an automatic safe driving cross-scene alertness detection method based on EEG analysis, and the method solves the problem that the number of channels among different EEG devices is inconsistent by utilizing the capability of reconstructing the EEG information of a plurality of real channels of a pre-trained alertness detection network. The alertness detection network after fine tuning can reconstruct missing EEG information according to input EEG information of a small number of channels and corresponding brain region information, and a classification task is completed based on the reconstructed information.

The invention discloses an electroencephalogram intelligent analysis method and system for a neurological rehabilitation patient, and the method comprises the steps: dividing historical data into different time periods, calculating feature values, effectively utilizing the information change in the time dimension, and enhancing the evaluation fineness. A five-level rehabilitation state classification tree is constructed by using a random forest algorithm, and key features are screened as classification nodes in combination with a feature value standard deviation, so that the accuracy and robustness of a classification model are improved. Meanwhile, by constructing a time-feature value coordinate graph, the influence weight of each decision tree classification result is dynamically adjusted, and it is ensured that the assessment result can reflect the rehabilitation progress of the patient in real time. Besides, voting values are distributed based on similarity calculation, so that the finally obtained rehabilitation result is closer to the actual condition of the patient, and scientific basis and personalized guidance are provided for neural rehabilitation.

The invention relates to the technical field of disease analysis, and provides an acute-stage cerebral infarction detection system and method.The method comprises the steps that basic information, physical sign data and blood sample data of a brain patient are collected, and data integration is conducted to obtain integrated data; a brain scanning image is obtained, and a high-definition brain image is obtained after artifacts are removed; recognizing image semantics, performing image segmentation, evaluating a segmentation level, and obtaining a target image when a preset level is met; identifying brain lesion features by using the target image; an angiogram is collected, the brain blood vessel caliber is calculated, and the brain blood perfusion condition is analyzed; constructing a blood vessel network by using the angiogram, and analyzing a blood vessel variation degree; based on the blood vessel caliber, the blood perfusion condition and the blood vessel variation degree, analyzing the brain blood vessel lesion degree; collecting an electroencephalogram, and analyzing brain activity characteristics; and performing acute-stage cerebral infarction analysis based on the integrated data, the brain lesion features, the brain vascular lesion degree and the brain activity features. The invention aims to improve the rationality of acute cerebral infarction detection.

The present invention relates to a brain wave analysis device, a brain wave analysis program, a motion assistance system, and a motion assistance method. A brain wave analysis device (16) comprises a signal acquisition unit (40) that acquires a time series of brain wave signals of a subject (12), and a computation unit (58) that determines an intrinsic frequency correlated to a motion-intention of the subject (12) on the basis of a frequency characteristic related to the time series of the brain wave signals acquired when the subject (12) is at rest.

An object of a system according to an embodiment is to accurately grasp thoughts and feelings of a person to be cared for and a person to be medically treated and to appropriately perform communication.SOLUTION: A system includes an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires brain waves of a person to be cared for or a person to be treated. The analysis unit analyzes the brainwave data acquired by the acquisition unit and analyzes an emotion. The generation unit generates a linguistic expression based on the emotion data analyzed by the analysis unit. The display unit displays the linguistic expression generated by the generation unit on a monitor.SELECTED DRAWING: Figure 1

The present invention relates to the field of medical data analysis technology, and in particular to an automatic electroencephalogram analysis method and system. An automatic electroencephalogram analysis method comprises: taking original electroencephalogram signal data; sending the electroencephalogram signal data into an electroencephalogram signal noise reduction model for processing to output noise-reduced electroencephalogram signal data. The present invention performs noise reduction processing on the original electroencephalogram signal data through the electroencephalogram signal noise reduction model, which can reduce the influence of various noise sources in the original electroencephalogram signal data, thereby improving the quality of the expression of the electrical activity of the patient's cerebral cortex, and facilitating the subsequent use of the electroencephalogram signal data; and in the electroencephalogram signal noise reduction model, the features similar to the influence of noise patterns in the original electroencephalogram signal data are aggregated by dynamic routing, providing a reference for subsequent noise reduction operations, and can effectively adapt to dynamically changing noise to obtain a better noise reduction effect.

The present invention discloses a method for early warning of intracranial electroencephalographic epileptic seizures based on multi-path graph brain network modeling, which belongs to the technical field of electroencephalogram analysis. The method includes proposing a multi-path graph brain network modeling framework for epileptic seizure prediction; constructing a multi-path brain map based on three cross-frequency coupling (CFC) relationships in neuroscience, wherein the frequency bands in each channel of the electroencephalogram signal form the nodes of the map, and the three CFC relationships form the edges of the map; designing a multi-path brain map learning network with a joint fusion module to learn the patterns in the multi-path brain map and form a final representation. The present invention can analyze iEEG signals, use the designed multi-path brain map learning network to mine the patterns therein and form a unified representation, thereby improving the accuracy of epileptic seizure prediction.

The invention discloses an EEG report generation method combining a hybrid neural network and an analysis experience algorithm. According to the method, through multi-scale time sequence deep convolution, symmetric channel convolution, cross convolution, whole-brain feature convolution, sub-window self-attention fusion, self-attention window fusion and a hybrid neural network of a classification network, a window highly associated with a classification result is selected, and a pathological judgment result is obtained. A window is described in combination with a mode of gradually enlarging a description area from waves to a channel to an area in an EEG analysis experience algorithm system, abnormal waves are found, and background activities are described; and finally, connecting the basic information, the window EEG signal graph, background activity description and abnormal wave description generated by the EEG analysis experience algorithm system and a classification result provided by the hybrid neural network in series to form an EEG report. A scheme for generating the EEG report by combining a deep learning method with generalization characteristics and an EEG analysis experience algorithm with experience knowledge is provided.

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.

The present invention relates to a system and method for real-time screening of Attention Deficit Hyperactivity Disorder using electroencephalographic signals processed through a GPU-accelerated time-frequency inference architecture. The invention enables continuous acquisition of multi-channel electroencephalographic data, adaptive preprocessing for artifact suppression and signal stabilization, and parallel execution of time-frequency transformations to extract neurologically relevant features in real time. Extracted features are analyzed using an inference process configured to identify neurological patterns associated with Attention Deficit Hyperactivity Disorder, while continuous validation of signal quality and temporal consistency ensures diagnostic reliability. The system dynamically adapts analytical parameters based on evolving signal characteristics and regulates computational workload to achieve energy-efficient operation during prolonged monitoring. The invention provides a technically integrated, machine-implemented screening solution capable of delivering reliable, real-time neurological assessment suitable for clinical environments.

The application discloses an EEG intelligent agent automatic analysis method based on a large language model, takes the large language model as a strategy engine, autonomously understands user analysis intentions and performs intelligent decomposition of tasks, and then dynamically schedules and cooperatively integrates analysis resources including traditional feature engineering, diversified deep learning models and external knowledge bases. The application can realize end-to-end automatic coordination and execution of complex EEG analysis tasks from signal preprocessing, feature extraction, event positioning to classification diagnosis, emotion recognition, sleep staging, etc., breaks through the limitation of single detection or classification tasks, and enables multi-task, continuous deep reasoning and interpretation of complex EEG data through context perception and flexible space-time analysis capability. The application deeply integrates the general planning and reasoning capability of the large language model with the special analysis model in the EEG field, and significantly improves the automation level, flexibility and clinical application potential of EEG analysis.

An electroencephalogram analysis device includes a signal acquisition unit that acquires a time series of electroencephalographic signals of an analysis subject, and a computation unit that obtains an intrinsic frequency correlated with a movement intention of the analysis subject based on a frequency characteristic related to the time series of the electroencephalographic signals of the analysis subject during rest.

Systems and methods are described that provide electrical stimulation to a person while also facilitating EEG recording. The EEG records may be analyzed to identify cerebral hemorrhage. In some examples, the EEG analysis may identify increased slow wave activity within a delta frequency range of about 1-4 Hz and / or decreased alpha activity within an alpha frequency range of about 8-13 Hz. Systems and methods may include delivering electrical stimulation and / or notifying a person or a medical provider of cerebral hemorrhage in response to EEG analysis.

The present disclosure relates to an apparatus, a method, and a computer program for detecting cortical-cortical evoked potentials (CCEP) using transcranial magnetic stimulation (TMS). In particular, the apparatus comprises: a communication module for communicating with an external device; a memory storing at least one process for performing CCEP detection using the TMS; and a processor for performing CCEP detection using the TMS according to the process. The processor can be configured to: calculate noise intervals by collecting electroencephalogram (EEG) signals induced by TMS; cluster EEG channels excluding the calculated noise intervals; perform clustering EEG analysis on the basis of representative EEG signals of each cluster; perform individual EEG analysis on the basis of individual EEG signals within each of the clusters; and then detect CCEP signals on the basis of the clustering EEG analysis results and the individual EEG analysis results.

The application discloses a depression recognition method based on a multi-view capsule graph network, belongs to the field of electroencephalogram analysis in the field of biological medicine, and comprises the following steps: preprocessing an electroencephalogram signal; extracting node features; constructing a topological graph, a functional graph and a causal graph according to the node features; adaptively fusing the topological graph, the functional graph and the causal graph to obtain a fused graph; a bidirectional adjustment mechanism of a capsule graph network; constructing a loss function and a total training target of the capsule graph network; and a depression recognition process based on a dynamic routing mechanism of the capsule graph network.The application introduces the capsule graph network to structurally model topological structures and connection modes among electroencephalogram channels; the mutual relationship among the electroencephalogram channels is enhanced through adaptive fusion, so that the capsule graph network can more accurately capture the interaction and coupling features of brain regions; and the bidirectional adjustment mechanism is introduced to flexibly adjust the contribution degree of features, so as to enhance the information transmission capacity of the capsule graph network in a global context and the sensitivity and robustness of the capsule graph network to depression-related features.

The invention discloses a low-duration calibration electroencephalogram decoding method and system based on invariant feature extraction, and relates to the technical field of electroencephalogram signal decoding, and the method comprises the steps: obtaining electroencephalogram data with labels, and carrying out Fourier transform to obtain amplitude information and phase information; inputting the phase information and the tag into a teacher network for training to obtain feature output of the teacher network; performing random pairing and reconstruction on the labeled electroencephalogram data to obtain reconstructed electroencephalogram data, inputting the reconstructed electroencephalogram data into a student network, and learning phase information extracted from a teacher network through the student network to obtain feature output of the student network; and acquiring real-time data, and inputting the real-time data into a student network to obtain an electroencephalogram decoding recognition result. Similarity features among different individuals are extracted through an invariant feature extraction method, when electroencephalogram signals of a new user are analyzed, the similarity features can be directly used for electroencephalogram analysis of a new subject, and the use efficiency of a non-intrusive brain-computer interface and the electroencephalogram recognition accuracy are remarkably improved.

The present application discloses a method, device and equipment for analyzing multimodal signals, which collects video data of a first user's target part in motion, extracts the positions of key points of the human body from the data, and obtains a first muscle mechanical curve corresponding to the target part that changes over time. At the same time, the first EEG signal of the first user is collected and input into an EEG analysis model to obtain a classification result corresponding to the first EEG signal that changes over time. A mapping relationship is established between the first muscle mechanical curve and the classification result corresponding to the first EEG signal. The second EEG signal of the second user when imagining the target part in motion is collected and input into the EEG analysis model to obtain a classification result corresponding to the second EEG signal that changes over time. Based on the mapping relationship and the classification result corresponding to the second EEG signal that changes over time, a second muscle mechanical curve corresponding to the target part that changes over time is obtained. Quantitative analysis of muscle strength is generated through motor imagination, providing theoretical support for how to conduct exercise training for the target part.

The present invention discloses a brain signal decoding method based on the selection of regions of interest in the brain and continuous wavelet transform; this method establishes a signal conduction model by using a publicly available head anatomical template, obtains cortical electroencephalogram (EEG) using the standardized low-resolution tomography method, and then, based on the geometric parameters of source dipole points and the physiological basis of motor imagery EEG signals, more carefully divides the source regions after source imaging. Further, in order to better balance the characteristics of signals in the time domain and frequency domain, continuous wavelet transform is used for feature extraction, and finally, a convolutional neural network is used to complete the automatic classification and selection of features. The method of the present invention can achieve a high accuracy rate in the four-class motor imagery EEG task and has good physiological interpretability. The method for dividing regions of interest in the brain proposed in the present invention helps to improve the efficiency of EEG analysis by researchers, and at the same time, the proposed decoding method helps to establish a more efficient human-computer interaction system.

The invention discloses a motor imagery rehabilitation system and a motor imagery rehabilitation method based on a brain-computer interface. The system comprises an electroencephalogram acquisition module, wherein the electroencephalogram acquisition module is configured to acquire motor imagery state electroencephalogram data and resting state electroencephalogram data of a patient; the electroencephalogram analysis module is configured to perform motor imagery classification and recognition based on the motor imagery state electroencephalogram data to obtain a classification and recognition result; obtaining average imagination task energy and average resting state energy; determining a movement area change difference index according to the left-right movement brain area center lead electroencephalogram data energy and the left-right movement brain area center lead electroencephalogram data energy; determining whether the motion brain area of the patient is activated or not according to the motion area change difference index; the limb movement control execution module is configured to control the end executor to execute corresponding rehabilitation training actions according to a preset training mode requirement, the classification recognition result and / or whether the conveying brain area is activated or not. According to the method, the activity state of the exercise brain area can be accurately monitored, scientific quantitative indexes are provided for rehabilitation training, and the rehabilitation effect is improved.

The invention relates to the technical field of intelligent massage chairs, and discloses a massage chair control method and system based on real-time electroencephalogram analysis, and the method comprises the steps: collecting an electroencephalogram signal of a user; performing feature extraction on the electroencephalogram signals to obtain electroencephalogram features of the user; calculating a current user state according to the electroencephalogram characteristics of the user; and dynamically adjusting massage parameters of the massage chair according to the current user state until the user is in the target user state. According to the method, the electroencephalogram signals are collected in real time, the features are extracted, the parameters of the massage chair are intelligently adjusted to adapt to the user state, personalized massage is achieved, the comfort level and the effect are improved, the problem that a traditional massage chair cannot respond to the user requirement in real time is solved, excessive or insufficient massage is avoided, and the user can enjoy more accurate massage experience.

The application relates to a kind of based on magnetoencephalography stimulation combination electroencephalogram analysis method, method is by carrying out real stimulation and pseudo-stimulation control experiment, respectively obtains original mixed signal matrix and reference sensory artifact signal matrix;Then, the convolution kernel to be solved is introduced to construct convolution mixed mathematical model, and the modulation mechanism of sensory artifact is described in a mathematical way;Subsequently, two matrixes are input into the model, and the pure direct neural response signal matrix and individualized modulation convolution kernel matrix are separated out by applying joint iterative inversion calculation with space-time constraints;Finally, based on pure signal extraction multidimensional neurophysiological characteristics and combined with modulation mode analysis, generate comprehensive report.The method can effectively separate sensory artifact, and provide a new analysis approach for obtaining more reliable and direct cortical neuroelectrophysiological response.

This invention relates to the field of automotive electronics and intelligent control technology, and discloses an intelligent cockpit robotic arm system, including: a foldable robotic arm body installed in the cockpit, an end-effector sensing and interaction module, a driver state monitoring subsystem, and a central control unit. The central control unit controls the robotic arm's retraction or operating mode based on the driver's comprehensive fatigue index. In operating mode, visual gaze information is used as a gating signal to activate EEG analysis, and the operational intent is confirmed by combining visual prior probability with EEG signals. The robotic arm's impedance parameters are adjusted in real time based on the driver's fatigue state. This invention solves the problem of unstable single-modal control through Bayesian fusion of visual and EEG signals; by utilizing variable impedance control and momentum observer collision detection, it achieves active safety protection based on the driver's physiological state, providing a seamless and highly safe intelligent assistance experience within the limited cockpit space.