12 results about "Event-related potential" patented technology

The invention discloses a device for awareness level evaluation and a storage medium. The apparatus comprises: a processor; the device realizes the following operations: collecting clinical information of a person to be assessed and a task state electroencephalogram signal under a target stimulation normal form; extracting frequency domain characteristics of a specific frequency band and spatial-temporal characteristics of a target event related potential based on the task state electroencephalogram signal, and combining the frequency domain characteristics and the spatial-temporal characteristics into a corresponding electroencephalogram topographic map; inputting the corresponding electroencephalogram topographic map into a multi-modal large language model, and performing image feature extraction by using an image encoder to obtain electroencephalogram features; inputting clinical information into the multi-modal large language model, and performing text feature extraction by using a text encoder to obtain text features; and performing cross-modal attention calculation fusion on the electroencephalogram features and the text features by using a cross-modal fusion module to realize consciousness evaluation so as to output a consciousness evaluation result. By means of the scheme, the consciousness level of the patient can be automatically and accurately evaluated.

The application discloses a kind of cognitive promotion method and system based on multi-modal data.The cognitive promotion method includes: obtaining the first multi-modal data before treatment of patient;Amyloid load grouping is carried out to patient based on the amyloid load in the brain of patient, to obtain the initial cognitive training scheme corresponding;Cognitive training is carried out based on initial cognitive training scheme during the treatment of patient, and the second multi-modal data during treatment is collected;Based on multi-modal data before and after treatment of patient, in combination with brain network group atlas, obtain the multi-modal change data of patient;The multi-modal change data of patient is input into preset model, to output the cognitive promotion scheme that cognitive index of patient is most improved, to push to patient and carry out cognitive training.The method will be integrated by A beta-PET data, functional nuclear magnetic resonance imaging data and event-related potential data, real-time evaluation of the neuroplasticity change of patient, to dynamically generate personalized cognitive promotion scheme.

The application discloses a brain-computer interface method and system based on real-time closed-loop vibration enhancement, relates to the technical field of brain-computer interface, and comprises the following steps: processing EEG signals through a filtering and feature extraction algorithm, generating a regression vector and identifying an event-related potential as an expected signal; constructing a distributed EEG signal decoding network, optimizing node cooperation through task clustering; adopting an adaptive algorithm update node estimation of a diffusion strategy, optimizing signal decoding through dynamic adjustment of combination coefficients and inter-node cooperation; generating a virtual feedback signal according to a decoding result, fusing the virtual feedback signal with the EEG signal and feeding back to the system, adopting machine learning to optimize classification output, and adjusting model parameters to enhance brain rhythm. The method disclosed by the application can accurately capture the change characteristics of the electroencephalogram signal, dynamically adjust the cooperation mode between nodes, and improve the signal decoding accuracy. The decoder parameters can be adjusted in real time, the electroencephalogram rhythm synchronization is enhanced, and the recognition effect of the motor imagination task is improved.

The invention discloses an electroencephalogram signal processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an electroencephalogram signal collected under intermittent task stimulation, and based on an event mark corresponding to each task stimulation in the electroencephalogram signal, extracting an event related potential corresponding to each event mark; for each event-related potential, performing multi-level feature extraction on the event-related potential in a time dimension to obtain a target time sequence feature; feature extraction is carried out on the target time sequence features based on the channel dimension, and target spatial-temporal features are obtained; and determining a brain activity mode when the target object executes the cognitive task based on the target spatial-temporal characteristics of the multiple event-related potentials. The electroencephalogram signals in the task state enrich the amount of information related to the specific task, multilevel feature extraction is carried out on the event related potential extracted from the electroencephalogram signals in the time dimension, the decoding capacity for the cognitive task is improved, and then the accuracy of electroencephalogram information processing is improved.

The invention provides an anesthesia depth monitoring method, system and device based on auditory response and a medium, and the method comprises the following steps: constructing and outputting a composite auditory stimulation sequence formed by alternate circulation of a steady state evoked segment and a cognitive probing segment, the cognitive probing segment being a rapid presentation with a stimulation interval smaller than evoked potential duration; the continuous scalp electroencephalogram of the subject is acquired. Intercepting steady-state evoked section data, extracting steady-state auditory evoked response features and calculating a first anesthesia depth index; intercepting cognitive exploration section data, extracting event-related potential by adopting an overlapped signal separation algorithm, and calculating a second anesthesia depth index. And determining two confidence coefficients weights based on the signal quality of the steady-state section and the residual noise of the cognitive section, carrying out weighted fusion on the two indexes according to the weights, and generating and outputting a comprehensive anesthesia depth index for real-time and robust anesthesia depth evaluation and prompt. By implementing the technical scheme provided by the invention, the problem of low updating rate of traditional cognitive potential monitoring data can be solved.

Systems and methods for assessing cognitive function are described that use tracked electrical activity of the brain of the individuals in response to a specific sequence of stimuli in generating data sets, which, for example, can be encapsulated as a data structure. The data sets can include tracked specific response types, at different times and amplitudes, including, but not limited to, event related potential signal components. Brainwave features including, event related potentials, are tracked in relation to both pre-attentive brain responses and consciously controlled attention responses.

This invention discloses a rapid field brain injury diagnostic system based on electroencephalogram (EEG) data, belonging to the field of EEG data analysis and diagnostic technology. It includes an external stimulation module that utilizes sound waves and electrical impulses to stimulate limb muscles to induce specific event-related potentials and motor cortical activity rhythms for detection; an EEG data analysis module that performs multi-scale time window preprocessing and deep learning algorithm analysis; and an injury assessment module that determines the type and quantifies the degree of injury. This system can be implemented using a portable EEG acquisition helmet, a portable pulse stimulation device, and a chip-level handheld terminal to achieve a method from stimulation to detection to diagnosis. The overall device is lightweight, suitable for deployment in field scenarios, and can operate stably in complex environments.

The invention discloses a visual identification method and system based on electroencephalogram signal enhancement, and the method comprises the steps: S1, carrying out the grouping of a multi-class image data set, and generating a sequence image through employing a dual randomization strategy; s2, presenting an image sequence to a subject by adopting an ERP normal form, and randomly inserting a task target irrelevant to the main task into the sequence; s3, collecting EEG (electroencephalogram) signals with high time resolution of the whole brain and preprocessing the EEG signals; s4, respectively extracting features by using an image encoder and an electroencephalogram encoder, and carrying out joint training by adopting a supervised contrast learning target containing a hard negative sample weighting mechanism to generate feature representations aligned in a unified semantic space; and S5, inputting the aligned feature representation into a Transform-based fusion model, performing depth information interaction through a cross attention mechanism to generate a final fusion feature representation, and outputting an identification result by a classifier. According to the invention, the mixed granularity visual identification performance is improved.

The present invention relates to systems and methods for facilitating product preferences and / or product recommendations. These systems and methods take into account biometric data of a subject in order to determine product preferences and / or product recommendations. Although other factors of the subject are optional, these factors can also be taken into account when determining product preferences and / or product recommendations for the subject. These product preferences or recommendations can be presented to the subject automatically via a display device or with the assistance of a product consultant. In order to obtain the biometric data, the subject will be exposed to a variety of stimuli, such as a fragrance / aroma stimulus. Biometric data will then be collected from the subject based on the subject's response to this fragrance / aroma stimulus. In some instances, the biometric data collected is related to the subject's event-related potentials (ERPs), i.e., the measured brain responses that are a direct result of a specific sensory, cognitive, or motor event.

The application discloses a self-supervised auditory electroencephalogram decoding method under a low signal-to-noise ratio condition and application thereof, and comprises the following steps: S1) collecting multi-channel original electroencephalogram signals generated by an auditory perception paradigm of a subject under a low signal-to-noise ratio condition of -9 dB, wherein the auditory perception paradigm simultaneously induces an auditory steady-state response (ASSR) and an event-related potential (P300) through a complex sound; and S2) sequentially performing a 0.3-75 Hz band-pass filtering and a 50 Hz notch filtering preprocessing operation on the original electroencephalogram signals to obtain denoised electroencephalogram signals. The self-supervised learning framework is constructed for the auditory electroencephalogram decoding task under the low signal-to-noise ratio condition, the ASSR and P300 features are synchronously induced by using the -9 dB low signal-to-noise ratio complex sound paradigm, compared with the traditional high signal-to-noise ratio experimental paradigm, the self-supervised learning framework is closer to the real complex acoustic application scene, and the applicability and practicability of the method under strong noise interference are significantly improved; the self-supervised learning framework can continuously provide the model with a reconstruction target with appropriate difficulty, and the discriminability and learning efficiency of the self-supervised representation are significantly improved.

The invention provides a processing system based on electroencephalogram feedback, which comprises a signal processing and calculating module, and the signal processing and calculating module comprises a time synchronization unit and a feature extraction unit, and is used for extracting event-related potential components induced by each electrical stimulation pulse from electroencephalogram signals collected in a target stimulation period, the peak moment of each event related potential component is determined; the nerve conduction time calculation unit is used for calculating the average nerve conduction time of all the electrical stimulation pulses and the average amplitude of all the event-related potential components in the target stimulation period according to the emission time of each electrical stimulation pulse and the peak time of the corresponding event-related potential component; and the path state evaluation unit is used for calculating a period evaluation value of the target stimulation period according to the average nerve conduction time and the average amplitude. According to the invention, fusion evaluation between the electrical stimulation pulse and the evoked electroencephalogram response is realized.

This invention belongs to the field of biomedical engineering technology. It discloses a stem cell therapy efficacy evaluation system based on brain-computer interfaces and genetic information, comprising: acquiring multi-channel EEG signals and genomic polymorphic site data from patients; constructing a priority mapping matrix for repair needs in target brain regions; extracting micro-functional domain gene neural function coupling strength distribution data; identifying neural plasticity-sensitive regions based on changes in coupling strength gradients; calculating the regulatory enhancement coefficient and migration retardation coefficient of stem cell differentiation signals; constructing a multi-scale pathway synergy map; generating a composite scheme of treatment efficacy characterization parameters based on the map and mapping matrix; monitoring event-related potential waveform features to identify characteristic plasticity response peaks; constructing stem cell therapy efficacy evaluation results; and significantly improving the accuracy of stem cell therapy efficacy evaluation.