326 results about "Electro encephalogram" patented technology

The invention relates to a three-dimensional spiral high-density neural electrode and a preparation method and application thereof. The three-dimensional spiral high-density neural electrode comprises a probe structure and a plurality of electrode sites. The probe structure is formed by curling a planar flexible electrode precursor, and at least one end of the probe structure is provided with a spiral outer surface of a three-dimensional spiral line structure. Electrode sites are distributed along a spiral path, have a size of 5-1000 [mu] m, and are used for contacting biological tissues. On the planar flexible electrode precursor, electrode sites are arranged on one or more straight lines forming an inclined angle alpha with the axial direction of the probe, and the spatial distribution is matched with the edge. Compared with the prior art, the method has the advantages that the constraint of a traditional wiring mode is broken through, the integration of high-density three-dimensional channels is realized under a micro size, and a new generation of solution is provided for a high-precision brain-computer interface, deep brain stimulation and a three-dimensional electroencephalogram.

The invention relates to the technical field of electroencephalography, in particular to a sleep staging automatic identification method and system based on multi-mode electroencephalography, and the method comprises the following steps: extracting a waveform period to construct dominant distribution, correcting label boundary positioning mutation response, unifying a time axis to form an alignment structure, clustering mutation fragments to establish an alternating relation, and identifying the sleep staging based on multi-mode electroencephalography. And identifying a main channel range to construct an automatic identification scheme. According to the method, a reference channel is positioned through area distribution difference, a boundary change section is judged in combination with a frequency band energy change trend, a channel region with dense and stable mutation points is extracted as an anchor point, time axis unified adjustment is completed according to main response starting and ending time, and an alternating fragment graph is further constructed through a cross-channel time coverage relation of the mutation points. The sequential structure coordination ability and mutation form aggregation expression efficiency among multi-source signals are improved, and accurate recognition of boundary drift and asynchronous response and dynamic extraction of steady-state rhythm in sleep stage division are achieved.

The invention discloses a biomedical multi-mode signal anomaly detection and prediction method and system, and the method comprises the steps: modeling a biomedical multi-mode signal into a time-varying non-local dynamic system, capturing the long-time-history dependence characteristic of the signal through a memory mechanism of a Caputo fractional derivative, and introducing a time-varying input item to process interference; the method comprises the following steps of: carrying out high-precision numerical integration by adopting an Adams-Bashform-Module solver; optimizing model parameters in combination with a local domain normalization pre-training strategy and a fusion loss function; abnormal detection is realized by calculating comparison between signal reconstruction deviation and a self-adaptive threshold value; a future anomaly probability is generated based on the trajectory prediction. According to the method, the problems of insufficient non-local dependence capture, poor time-varying interference robustness, high false positive rate and the like in the prior art are effectively solved, the accuracy and real-time performance of abnormal detection and prediction of biomedical signals such as electroencephalogram and electrocardiogram are remarkably improved, and the method is suitable for wearable medical equipment and clinical monitoring systems.

Provided herein are systems, kits, and methods for monitoring brain activity. In some implementations, a system includes a plurality of wearable sensors having a housing with an extended, rounded shape are removably attached to the scalp of a patient and monitor electroencephalogram (EEG) signals. Approaches for instructing a user to position and active that wearable sensors are disclosed. Approaches for facilitating collection, synchronization, and processing of EEG signals are disclosed. Approaches for handing off control of the wearable sensors between portable computing devices are disclosed.

The invention relates to the technical field of electroencephalogram analysis, in particular to a domain-adaptive cross-subject electroencephalogram signal emotion recognition method, which comprises the following steps: acquiring an electroencephalogram signal, and preprocessing the electroencephalogram signal; inputting the preprocessed electroencephalogram signals into a trained electroencephalogram signal emotion recognition model to obtain an emotion classification result; the electroencephalogram emotion recognition model comprises a graph convolution feature extraction module, an attention module, a domain confrontation module and a classifier module. According to the method, the confrontation module formed by combining the gradient inversion layer and the domain discriminator is designed, and the change rule of the electroencephalogram of a person in positive, neutral and negative states is focused instead of the intensity of the reaction, so that the extracted emotional features have domain invariance, and the extraction accuracy is improved. And identification deviation caused by difference of different individuals in cross-subject emotion identification is eliminated.

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.

The invention provides a transcranial magnetoacoustic stimulation closed-loop feedback method and system based on electroencephalogram guidance. The method comprises the steps that original electroencephalogram signals of a target brain area are collected and preprocessed; target brain region activity features are extracted based on the preprocessed electroencephalogram signals, and the neural activity state of the target brain region is judged according to the target brain region activity features; according to the state judgment result, initial stimulation parameters are generated and optimized in combination with a preset stimulation response model; the generated initial stimulation parameters are converted into actual stimulation signals, the transcranial magnetoacoustic stimulation device is controlled to execute ultrasonic and magnetic field stimulation on the target brain area, and closed-loop feedback is conducted according to the activity state of the target brain area. According to the method, the limitation of a traditional transcranial stimulation method in the aspects of real-time feedback, individualized adaptation, artifact interference and safety control is effectively solved, accurate, controllable and individualized intervention of the neural activity of the target brain region is achieved, and a repeatable, safe and efficient technical means is provided for nerve regulation and control research and clinical application.

The invention discloses a digital electroencephalograph, and belongs to the technical field of brain-computer interface signal processing. An electroencephalogram signal processor of the digital electroencephalograph acquires digital neural signals of a plurality of channels for a target object, wherein the digital neural signals comprise LFP signals and pulse signals; performing feature extraction on the two signals based on a unified time window and a sliding step length, and integrating a time-aligned feature matrix into a first fusion feature tensor; performing dynamic normalization processing on the first fusion feature tensor, wherein the statistical magnitude is updated along with the change of the input data; and n parallel LSTM networks are used to decode the normalized second fusion feature tensor to obtain n decoding results of different dimensions, the n LSTM networks are mutually independent, and each LSTM network has an independent optimizer and an independent parameter. According to the method, multi-modal features can be aligned in time, signal drift is compensated, decoding robustness is improved, and inter-task optimization decoupling and personalized training are achieved through independent parameters and an optimizer.

In various embodiments, computer-implemented systems and methods for extracting pixel trajectories representing waveforms from a digital image, formed by lines and columns of picture elements, pixels, of a recording of an electrical activity of a human organ detected by on-skin electrodes, such as an electrocardiogram, ECG, or an electroencephalogram, EEG, are provided.

A method is provided. The method comprises obtaining, using electroencephalogram (EEG) sensors, a first set of EEG signals that comprises a plurality of first waveforms, and each of the plurality of first waveforms is associated with a frequency band from a plurality of frequency bands; training a plurality of machine learning-artificial intelligence (ML-AI) models using the first set of EEG signals, wherein each of the plurality of ML-AI models is trained for a different frequency band; obtaining, using the EEG sensors, a second set of EEG signals, wherein the second set of EEG signals comprises a plurality of second waveforms; inputting each of the plurality of second waveforms associated with the frequency band into a corresponding ML-AI model associated with the respective frequency band to generate a plurality of outputs; and performing one or more actions based on the plurality of outputs.

The invention provides a sleep stage method based on prototype data comparative representation learning, which comprises the following steps of: S1, acquiring an electroencephalogram signal related to sleep, and respectively performing strong enhancement processing and weak enhancement processing on the electroencephalogram signal to obtain a strong enhanced electroencephalogram signal and a weak enhanced electroencephalogram signal; s2, respectively carrying out coding processing to obtain strong enhancement coding features and weak enhancement coding features; respectively extracting time features to obtain a strong enhancement time view and a weak enhancement time view; s3, performing cross prediction on the strong enhancement time view and the weak enhancement time view to obtain time prediction features; introducing prototype data, and comparing the time prediction features with the prototype data to obtain comparison features; and S4, completing sleep staging based on the comparison characteristics. According to the method, the SPC model is designed by adding the prototype data into the context comparison module, so that the comparison learning efficiency is improved, the overall staging accuracy is improved, and the staging accuracy of each sleep stage is good.

The invention discloses a sleep staging method based on multi-view gating interactive attention fusion. The sleep staging method comprises the steps that a single-channel electroencephalogram signal is preprocessed; an original electroencephalogram sequence and a time-frequency graph obtained through continuous wavelet transform are generated and serve as multi-view-angle input; time sequence features are extracted from the original electroencephalogram sequence through a feature extraction module, and time-frequency features are extracted from the time-frequency graph; fusing the time sequence features and the time frequency features through a feature fusion module, including respectively applying convolution attention to highlight internal key features at an electroencephalogram view angle and a time frequency graph view angle, and performing interaction between view angles through cross attention; convolutional attention output and cross attention output are adaptively fused through a hierarchical expert hybrid mechanism; and outputting a sleep stage classification result through the time convolution network. According to the invention, more comprehensive feature representation is realized.

The invention provides an emotion recognition method, device and equipment based on electroencephalogram signals and a medium. The emotion recognition method based on the electroencephalogram signals comprises the steps that original electroencephalogram signals under all electrode positions are obtained; electroencephalogram characteristic data are extracted according to the original electroencephalogram signals; extracting frequency band and spatial features from the electroencephalogram feature data to obtain a space-frequency feature map; a bidirectional Mama network is adopted to extract time sequence characteristics of the space-frequency characteristic pattern in each time period, and a time sequence characteristic sequence is obtained; fusing each time sequence feature in the time sequence feature sequence on a time period dimension to obtain a fused time sequence feature; and recognizing the fusion time sequence features by using a feedforward neural network to obtain an emotion recognition result. According to the emotion recognition method and device based on the electroencephalogram signals, the equipment and the medium, deep feature fusion can be achieved, hidden features related to emotion changes in the electroencephalogram can be fully mined, and the accuracy of emotion recognition is improved.

The invention provides an electroencephalogram image reconstruction method based on frequency steering and bidirectional diffusion, and belongs to the technical field of brain-computer interfaces and computer vision. The method comprises the steps that EEG data and corresponding image data are acquired and preprocessed; constructing a reconstruction model comprising a frequency domain-space-time dynamic encoder and a bidirectional submerged space diffusion generator; wherein the frequency domain-space-time dynamics encoder adopts a frequency-oriented Mama architecture, explicitly models neural oscillation dynamics by constructing a block diagonal state matrix, and extracts features in combination with graph convolution and space-time convolution; the bidirectional submerged space diffusion generator comprises a symmetric EEG-to-image submerged space diffusion model and an image-to-EEG submerged space diffusion model, and training is carried out through generative cyclic consistency constraint; and finally, mapping the collected EEG data into image semantic features by using the trained model, and driving a pre-training generation model to reconstruct an image. According to the method, the problems that in the prior art, the electroencephalogram frequency domain specificity dynamic state is ignored, and cross-modal semantic alignment is weak are solved, and the semantic consistency of electroencephalogram decoding and the fidelity of image reconstruction are remarkably improved.

The embodiment of the invention provides an emotion detection method based on a multi-mode electroencephalogram eye movement signal. According to the method, a dual-path electroencephalogram modal encoder is used for determining time sequence characteristics of an electroencephalogram in a time domain path, determining frequency domain characteristics of an amplitude spectrum of the electroencephalogram in a frequency domain path, and obtaining dual-path time domain and frequency domain fused electroencephalogram characteristics based on the time sequence characteristics and the frequency domain characteristics; the dual-path eye movement modal encoder is used for determining pupil features of the eye movement data in the pupil diameter path, determining eye movement fixation features of the eye movement data in the fixation point path, and obtaining fused eye movement features based on the pupil features and the eye movement fixation features; and the multi-modal expert mixing module is used for dynamically performing modal calling on the multi-modal mixed characteristics which are subjected to modal alignment and are fused with the electroencephalogram characteristics and the eye movement characteristics, performing emotion detection by utilizing an expert with a modal corresponding to the multi-modal mixed characteristics, and outputting an emotion classification result. According to the embodiment of the invention, alignment is realized through multi-task pre-training, and the depressive emotion is accurately recognized.

The invention relates to the technical field of brain-computer interaction, in particular to a patient mental state assessment system for a clinician, which comprises a semantic manifold calibration module, a state trajectory projection module, a geometric curvature resolving module and an emotion transformation quantification module. According to the method, electroencephalogram data are collected and deeply analyzed, high-dimensional neural activity characteristics are converted into a visual three-dimensional state track in combination with titer and awakening degree information, continuous dynamic monitoring of the mental state of a patient is achieved, then the emotion conversion process is quantitatively analyzed according to the geometric curvature change rate of the state track, and therefore the mental state of the patient can be rapidly and accurately monitored. According to the method, rapid emotion fluctuation which is difficult to perceive in a traditional evaluation mode can be accurately recognized and marked, objective and fine data support is provided for clinical diagnosis, and the accuracy and instantaneity of mental state evaluation are remarkably improved.

In some examples, a system includes a device comprising one or more electroencephalogram (EEG) sensors. The device is configured to collect, from a subject, an EEG signal using the one or more EEG sensors. The system further includes one or more processors; and a memory storing instructions that, when executed by the processors, cause the one or more processors to receive, from the one or more EEG sensors of the device, the EEG signal collected from the subject; and apply a model to the EEG signal to determine a cognitive reserve of the subject. The model is trained using a plurality of sets of training data, each set of training data including a training EEG dataset collected from a training subject and a training cognitive reserve score corresponding to the training subject that is determined based on metrics separate from the training EEG dataset.

A system for estimating a subject's functional brain age using non-invasive electroencephalography (EEG), the system includes: an EEG acquisition module (101) configured to record multichannel EEG signals from the subject; a stimulation control module (102) configured to present a predefined sequence of cognitive and sensory tasks that specifically target the frontal, temporal, parietal and occipital brain regions, and to generate synchronized event markers; a data processing module (103) configured to prepare the recorded EEG signals by filtering, artifact removal, normalization and segmentation into task-oriented time windows; a machine learning module (104) comprising one or more self-monitoring contrastive learning encoders configured to transform the preprocessed EEG signals into latent feature representations; and a brain age estimation module (105) configured to process the latent feature representations to generate a BrainAge Score indicating a difference between a predicted biological brain age and the subject's chronological age.

The invention discloses a children propofol anesthesia depth monitoring method based on permutation entropy and heart rate change, and belongs to the technical field of anesthesia depth monitoring, and the method comprises the following steps: extracting a children electroencephalogram, and carrying out preprocessing and spectral analysis on the children electroencephalogram to obtain permutation entropy; child heart rate data are extracted and processed to obtain the heart rate change degree; inputting the permutation entropy, the heart rate change degree and the age into the mixed prediction model to obtain a composite index, and indicating the narcotic depth of the propofol for the children. According to the children propofol anesthesia depth monitoring method based on the permutation entropy and the heart rate change, the mixed prediction model is constructed by combining the electroencephalogram permutation entropy, the heart rate change degree and the child patient age, and higher accuracy, real-time performance and reliability are shown in children propofol anesthesia depth monitoring; the anesthesia state change can be quickly responded, and the problem of misjudgment caused by age dependence and insufficient linear processing in the prior art is effectively solved.

The invention discloses an information data processing system based on brain wave signals, and particularly relates to the field of medical signal processing and brain science application, and the system comprises a data importing and filtering module which supports the input of an original electroencephalogram file and carries out band-pass filtering on the original electroencephalogram file; the bad track detection module is used for automatically detecting and marking bad tracks based on a local outlier factor algorithm, and comparing local density differences between each channel and surrounding channels; the artifact suppression module is used for correcting non-engraving plate transient artifacts in the electroencephalogram; the data restoration and interpolation module is used for carrying out interpolation on the detected bad track and restoring the overall channel layout; the frequency band and ratio characteristic module is used for calculating energy, relative power and ratio of delta, theta, alpha, SMR, beta and betaH frequency bands; the spectrum-space-time characterization module is used for constructing the features into two-dimensional tensors; and the judgment module is used for outputting the mental state category and the attention score. The mental state can be identified based on the juvenile electroencephalogram.

A classification model for multimodal human physiological data, and a training method therefor, a classification method for multimodal human physiological data, and a device are provided. The classification model includes: a multi-headed self-attention module, a normalization module, a fusion expert system, and a decision module. The multi-headed self-attention module is configured to perform feature extraction on multimodal synchronous data. The normalization module is configured to generate normalized feature data based on the extracted feature data. The fusion expert system includes: an electroencephalogram expert subsystem, an electrocardiogram expert subsystem, an electrodermal activity expert subsystem, and a multimodal synchronous fusion expert subsystem each configured to perform a classification task based on the corresponding normalized feature data. The decision module is configured to calculate a final classification result based on the above four classification results.

The application provides a wireless and battery-free brain nerve stimulator and a preparation method. The brain nerve stimulator can be driven by a mobile phone loudspeaker, and a resonator designed can effectively amplify a piezoelectric signal. The brain nerve stimulator comprises two parts: a base provided with implantable nerve stimulation electrodes and a resonant cavity device connected through magnetic coupling. The design scheme of the brain nerve stimulator provides a general method for inhibiting epilepsy. The brain nerve stimulator can be extended to target brain deep layers or surface areas and peripheral nervous systems. A stimulation mode can be wirelessly transmitted and controlled. It is proved by electroencephalogram recording of free-moving mice that the wireless nerve stimulator can effectively relieve seizure events of the epileptic mice in vivo.

A medical system configured to detect and / or monitor one or more physiological parameters of the patient including an electroencephalogram (EEG) of the patient, a oxygen level of the blood patient (e.g., a jugular venous oxygen saturation (SjVO2)), and / or a blood flow of the patient (e.g., a cerebral blood flow). The medical system may include processing circuitry configured to monitor the physiological parameters and compare the physiological parameters to one or more thresholds. The medical system may be configured to indicate that the patient may have experienced a stroke based on the comparison of the physiological parameters with the thresholds. In examples, the processing circuitry is configured to indicate a type of stroke the patient may have experienced based on the comparison.

Disclosed are systems and methods that provide a novel computerized framework for a closed-loop, decision-intelligence (DI)-based computerized framework for automatically and dynamically managing and controlling a medical procedure, inclusive of administered medication and / or anesthesia to a patient and an intraoperative level of consciousness of the patient. The disclosed framework provides an improved electroencephalography (EEG) indices that adapts to specific patient needs, and dynamically adapts to factors of an ongoing procedure to ensure that the proper levels of anesthesia are administered, required and / or maintained. This provides computerized capabilities to maintain safe levels of the patient's consciousness, such that post-operative patient health is preserved and maintained. Thus, the disclosed framework provides an effective anesthesia management framework that can be leveraged to safely manage a patient's health during and after a medical procedure for which anesthesia is used.

The utility model provides an electroencephalogram anti-falling detection cap which comprises an auxiliary fixing frame, two head sleeves, an anti-falling mechanism, a mounting assembly and an electrode slice, and the two head sleeves are oppositely arranged in the auxiliary fixing frame; the anti-falling mechanism comprises a threaded rod, a connecting cylinder, a pushing rod, a first elastic piece, a limiting chuck and a movable limiting assembly, the threaded rod is arranged on the auxiliary fixing frame in a penetrating mode and connected with the connecting cylinder, the connecting cylinder movably sleeves one end of the pushing rod, the other end of the pushing rod is connected with the outer side wall of the head sleeve, the first elastic piece is arranged in the connecting cylinder, and the limiting chuck is arranged in the connecting cylinder. One end of the first elastic piece is connected with the top push rod, the other end of the first elastic piece is connected with the connecting cylinder, the threaded rod is sleeved with the limiting chuck, the limiting chuck is provided with a plurality of clamping positions, the movable limiting assembly is movably and adjustably arranged on the auxiliary fixing frame, and the movable limiting assembly is clamped in one clamping position. The size of the head circumference space can be adjusted so as to adapt to the sizes of the head circumferences of different patients.

An ear-worn device may include an accelerometer and / or an electroencephalography (EEG) sensor to provide recommendations based on user activity and / or physiological responses. The ear-worn device may use the accelerometer to capture acceleration data to assess the wearer's movement rate, and detect changes. Using data from the EEG sensor, the processor may detect eyelid blinks and other signals of fatigue. The processor may generate a treatment recommendation based on any combination of the movement rate, changes, eyelid blinks, and / or signals of fatigue.

The invention provides a brain wave epilepsy detection method based on Transform. The method comprises the following steps: acquiring EDF data of a multi-channel electroencephalogram from a database, processing the EDF data, generating a CSV file, and performing data preprocessing on the generated CSV file; defining a plurality of machine learning models, and independently training the models on the EEG feature data to obtain the classification performance of the models; predicting a probability vector by using a machine learning model to construct Transform model input data; the attention of the Transform model is used for dynamic training, and a trained Transform fusion model is obtained; and outputting a prediction result, and storing the trained Transform fusion model. According to the method, more efficient model fusion is realized through dynamic fusion, and the accuracy and generalization ability of epilepsy detection are improved.

This invention relates to the field of music signal recognition technology, and more particularly to a method for recognizing the degree of music enjoyment based on music-EEG feature fusion. The method includes: extracting the Mel-frequency cepstral coefficients of the music signal; preprocessing and performing Fast Fourier Transform on the Mel-frequency cepstral coefficients to obtain the spectral feature values ​​of the music signal; and sequentially filtering, reducing dimensionality, and decorrelating the spectral feature values ​​of the music signal; extracting the Mel-frequency cepstral coefficients of the EEG signal; optimizing and updating the order of the Mel-frequency cepstral coefficients of the EEG signal so that the comprehensive error value between the EEG feature reconstruction signal and the original EEG signal meets a preset condition; aligning the music feature coefficients and the EEG feature reconstruction signal in the feature dimension and time axis using the FastDTW algorithm; and recognizing the degree of music enjoyment of the subject based on the alignment result. This invention effectively improves the robustness and accuracy of music recognition.

A system can include a processing circuit. The processing circuit can receive an electroencephalogram signal from at least one electrode to sense brain activity of a neonatal patient. The processing circuit can execute a non-linear energy operator (NLEO) using the electroencephalogram signal to generate an NLEO signal. The processing circuit can generate at least one trajectory of a metric from the NLEO signal. The processing circuit can detect that the electroencephalogram signal indicates a seizure responsive to the trajectory of the metric matching a trajectory pattern.

The application provides an epilepsy prediction system based on multi-element weighted joint recursion and graph attention network, which can be applied to the fields of biomedical signal processing and artificial intelligence technology. The system comprises a brain function imaging module configured to acquire electroencephalogram signal data of a target object under authorization of the target object; a processor comprising a multi-element weighted joint recursion processing unit configured to obtain a phase space trajectory vector of each channel of the electroencephalogram signal data according to the electroencephalogram signal data, construct a recursion graph according to the phase space trajectory vector of each channel, obtain a channel correlation coefficient between any two channels based on the recursion graph of each channel, and construct a weighted adjacency matrix representing the brain function of the target object according to the channel correlation coefficient between any two channels; and a graph attention network processing unit configured to perform spatiotemporal feature processing according to the weighted adjacency matrix and the electroencephalogram signal data and output an epilepsy seizure prediction result.