21 results about "Electrical brain activity" patented technology

The invention discloses a disturbance of consciousness stimulation regulation and control system and method fused with electroencephalogram connection recognition. The system comprises a simulated electroencephalogram signal data acquisition stage, a connection recognition analysis stage, a stimulation parameter optimization stage and an executable stimulation instruction conversion stage. The method has the following advantages and effects that a whole-electroencephalogram activity distribution diagram is generated by simulating an electroencephalogram signal data acquisition stage, a key connection area is identified, and a brain function connection map is generated by utilizing a function connection analysis network and a phase synchronization algorithm in a connection identification analysis stage; in the stimulation parameter optimization stage, space-time correlation between a whole electroencephalogram activity distribution map and a brain function connection map is established, a multi-objective optimization algorithm is adopted to generate an optimized stimulation parameter set through a fusion network, and finally, in the executable stimulation instruction conversion stage, the optimized stimulation parameter set is converted into an executable stimulation instruction based on a self-adaptive control model. Therefore, the accuracy and the self-adaptive capability of electroencephalogram signal stimulation regulation and control are remarkably improved.

A user interface system enables communication through inaudible speech by detecting physiological signals associated with inner voice communication. The system includes a wearable device positioned in, on, or around a user's ear, housing one or more sensor modalities such as electromyography (EMG) sensors for detecting muscle movements, functional near-infrared spectroscopy (fNIR) sensors for brain activity, and / or detection-and-ranging systems using sonar or radar for micro-deformations. Pre-processing modules condition the sensor data, which is then analyzed by one or more machine learning models to reconstruct the content of the user's inaudible communication and produce output representations. The system enables high-bandwidth natural language interaction without audible vocalization, maintaining privacy while supporting applications including AI assistant interaction, real-time translation, secure authentication, and inter-party communication.

Embodiments described herein relate to systems, devices, and methods for monitoring brain activity and delivering electrical brain stimulation to a patient. In some embodiments, a system can deliver responsive electrical stimulation in a closed-loop manner and can offer real-time or near real-time monitoring of induced neurophysiological effects. After detecting a targeted brain pattern (i.e., an epileptic seizure), the system may deliver high-intensity ultra-short electrical stimulation impulses non-invasively or minimal-invasively to diminish or stop the neural oscillations underlying the epileptic seizure. The stimuli are delivered in time and space, relative to the emerging seizure rhythm patterns, such that they diminish or terminate the seizure. The system may include an implantable device including one or more electrodes electrically coupled to one or more processors. The processor(s) may be operatively coupled to a memory, one or more communication modules, and may optionally be coupled to a battery and one or more additional sensor(s).

The invention provides an EEG (electroencephalogram)-based self-sensitivity quantitative evaluation method, and belongs to the field of physiological signal processing and mode recognition. The method comprises the following steps: collecting resting state and task state electroencephalogram signals of a subject, and obtaining a self-efficacy feeling score of the subject in combination with a scale; the electroencephalogram signals are preprocessed, window segmentation is carried out on the preprocessed signals, and multi-dimensional features such as a time domain, a frequency domain, a power spectrum entropy and a micro state are extracted; and inputting the features and the performance feeling score into a deep learning model based on LSTM-Attention for training, and constructing an evaluation model. The model can output a quantitative score according to the electroencephalogram signal, and the evaluation result continuously iterates and optimizes the model through continuous feedback data, so that the evaluation precision is improved. According to the method, objective and accurate quantification of self-effectiveness is achieved by combining the electroencephalogram signal features and the deep learning technology, the internal association between the self-effectiveness and electroencephalogram activity is revealed, and high practicability is achieved.

EEG cap for recording electrical brain activity, comprising: a) a textile basic structure, b) a plurality of defined electrode positions, c) a central sensor module with amplifier, analog-digital converter and wireless data transmission unit, d) a mechanically and electrically contactable connection system between electrodes and module, e) an integrated energy supply using a lithium polymer cell.

Specifically, this invention relates to an acoustic stimulation electrophysiological system and control method based on real-time EEG feedback, comprising: an EEG acquisition module that acquires the user's raw EEG signals in real time via an electrode array, and transmits them to an EEG preprocessing and feature extraction module for filtering and noise reduction, extracting multi-frequency power feature data, and sending it to a sleep state recognition and safety monitoring module. The sleep state module identifies the user's state and transmits the result to an acoustic stimulation parameter decision module. This module generates acoustic stimulation control parameters based on the state recognition result and sends them to a sound field synthesis and rendering module via a communication module to generate multi-channel acoustic stimulation signal waveform data, which is then transmitted to an acoustic stimulation output module to be converted into analog signals to drive an acoustic device to generate a specific sound field. The safety monitoring module continuously monitors abnormal EEG activity. The advantage of this invention is that the entire system forms a closed-loop feedback path from signal acquisition to acoustic stimulation output and safety monitoring.

In some embodiments, the present invention provides an exemplary inventive system that includes: an apparatus to record: individual's brain electrical activity, a physiological parameter of the individual, and iii) an environmental parameter; a computer processor configured to perform: obtaining a recording of the electrical signal data; projecting the obtained recording of electrical signal data onto a pre-determined ordering of a denoised optimal set wavelet packet atoms to obtain a set of projections; normalizing the particular set of projections of the individual using a pre-determined set of normalization factors to form a set of normalized projections; determining a personalized mental state of the individual by assigning a brain state; determining a relationship between: the physiological parameter, the environmental parameter, and the personalized mental state; generating an output, including: a visual indication, representative of the personalized mental state, and) a feedback output configured to affect the personalized mental state of the individual.

The invention discloses an emotion recognition method, device and equipment based on brain waves and a storage medium, belongs to the technical field of artificial intelligence, and is applied to a production insurance renewal or old-age health detection scene. The method comprises the following steps of: acquiring an electroencephalogram signal of a user, accurately extracting key wave bands including an active wave band and a relaxed wave band in the electroencephalogram signal in combination with a spectrum analysis method, further calculating a power proportion, forming a representative key wave band proportion index, and further realizing accurate identification of an emotional state of the user. Firstly, the accuracy and timeliness of electroencephalogram signal processing are improved through spectral analysis, and subtle differences of electroencephalogram activities in different emotional states are effectively captured; secondly, the key wave band proportion serves as a core index, the physiological mechanism of dynamic change of brain waves of a user can be reflected, and the discrimination ability and personalized adaptability of an emotion recognition model are enhanced; and thirdly, the pre-training model is combined with large-scale diversified data, so that the generalization performance and robustness of recognition are improved, and the method is suitable for different users and environment conditions.

This invention provides an electroencephalogram (EEG) simulation device, comprising an interaction module for receiving and displaying user input information and feedback signals from a processing module, converting the input information into input signals and sending them to the processing module; a processing module for generating corresponding modulation control signals, power control signals, and feedback signals based on the input signals; a signal generation module for generating waveform signals; a signal modulation module for modulating the waveform signals according to the modulation control signals to generate simulated EEG signals for output; and a power supply module for supplying power to other modules according to the power control signals. The beneficial effects of this invention are that the precise modulation of the waveform signals and the application of bias voltage make the output simulated EEG signals more closely resemble real brain activity, which is beneficial for scientific research and clinical applications; and the design of common-mode filters and capacitors in the power supply module effectively reduces the impact of environmental noise on system performance.

A medical device system is configured to sense electrical brain activity and sense electrical cardiac activity. The system may detect a concerning heart rate from the electrical cardiac activity and determine that the concerning heart rate is a hemodynamically unstable cardiac rhythm based on the sensed electrical brain activity. The medical device system may generate an output for providing a response to determining that the concerning heart rate is a hemodynamically unstable rhythm.

The invention discloses a depression assessment method based on multi-view subgraph construction, which is applied to the field of electroencephalogram data processing and aims at solving the problem that the accuracy and reliability of emotion recognition are limited due to the fact that comprehensive brain function structure features cannot be effectively extracted from multiple dimensions in the prior art. According to the method, firstly, a graph construction mode based on Euclidean distance, time-frequency analysis and nonlinear features is introduced, and the model can perform deep abstraction on signals of the brain from multiple angles, so that feature loss possibly caused by a single-view-angle analysis method is effectively avoided, and the adaptability to the complexity and diversity of brain activities is improved. Wherein the nonlinear analysis helps to excavate a potential complex dynamic relationship in the signal, and helps to reveal the nonlinear change of the brain in a specific mental state. Finally, the construction of a multi-view image not only enhances the recognition of functional connection between brain areas, but also improves the sensitivity to different electroencephalogram activity states.

The invention discloses a brain monitoring method and system for a patient with disturbance of consciousness, and belongs to the technical field of medical monitoring. A brain monitoring system for a patient with disturbance of consciousness comprises a patient brain monitoring module and a patient demand recognition module. By continuously acquiring the electroencephalogram activity signals and combining with the effective connection analysis algorithm, the brain monitoring information flow graph of the patient with the disturbance of consciousness can be constructed, signal transmission and causal intensity between different brain function areas can be accurately reflected, the brain activity of the patient with the disturbance of consciousness can be monitored in real time, and accurate evaluation of the brain state of the patient is ensured; by combining the electroencephalogram activity signal and the body movement monitoring signal, the physiological state of the patient can be comprehensively analyzed, the body movement data of the patient during unconscious activity is captured, and the monitoring comprehensiveness and accuracy are further improved.

In some aspects, the present disclosure provides methods of treatment comprising zelquisintel or a pharmaceutically acceptable salt thereof. In some aspects, the present disclosure provides methods of treatment comprising a loading dose of zelquisintel or a pharmaceutically acceptable salt thereof. In some embodiments, the methods further comprise a maintenance dose of zelquisintel or a pharmaceutically acceptable salt thereof. In some aspects, the present disclosure provides methods of treatment comprising monitoring electrical brain activity in a subject in need thereof and administering zelquisintel or a pharmaceutically acceptable salt thereof or apimostinel or a pharmaceutically acceptable salt thereof.

In some embodiments, the present invention provides an exemplary inventive system that includes: an apparatus to record: individual's brain electrical activity, a physiological parameter of the individual, and iii) an environmental parameter; a computer processor configured to perform: obtaining a recording of the electrical signal data; projecting the obtained recording of electrical signal data onto a pre-determined ordering of a denoised optimal set wavelet packet atoms to obtain a set of projections; normalizing the particular set of projections of the individual using a pre-determined set of normalization factors to form a set of normalized projections; determining a personalized mental state of the individual by assigning a brain state; determining a relationship between: the physiological parameter, the environmental parameter, and the personalized mental state; generating an output, including: a visual indication, representative of the personalized mental state, and) a feedback output configured to affect the personalized mental state of the individual.

Devices and methods for monitoring, stimulating, and entraining electrical activity generated by the brain of a person are provided. Electroencephalography (EEG) and photobiomodulation (PBM) devices in the form of headphones for monitoring, stimulating, and entraining electrical activity generated by a person's brain are described, along with methods for monitoring and stimulating a person's cognitive and physiological state using the provided devices. PBM light is pulsed to stimulate an increase in targeted brainwave frequencies through entrainment and providing additional energy to mitochondria. PBM stimulation is combined with EEG sensors and neurofeedback. The neurofeedback may be used to assist users with stress, anxiety, fatigue, mood, creativity and mental focus and acuity.

The invention discloses a depressed emotional state LFP-EEG joint decoding method and device based on CBAM, and the method comprises the steps: employing discrete short-time Fourier transform to extract corresponding time-frequency features for electroencephalogram data of LFP and EEG modes; time-frequency features corresponding to the LFP and the EEG are used as input of the CBAM model, modal complementarity of the LFP and the EEG is utilized, and a channel attention module and a time-frequency attention module in the CBAM model are combined to realize frequency-time-channel three-dimensional feature fusion enhancement, LFP and EEG combined decoding is realized, emotion-related electroencephalogram activities of the depression patient are accurately captured, and the emotion-related electroencephalogram activity of the depression patient is accurately captured. The method is used for patient positive emotional state and negative emotional state classification tasks. According to the method, a lightweight model structure is realized, depression emotion state decoding can be better realized, and the method is used for an emotion state classification task.

The invention relates to the technical field of electronic medical record data classification, in particular to an electronic medical record data classification method based on electroencephalogram signals. The method comprises the following steps: firstly, acquiring an electroencephalogram signal of each patient under each channel, a diseased electroencephalogram signal slice and a non-diseased electroencephalogram signal slice in the electroencephalogram signal, and acquiring wave band components of each electroencephalogram signal slice in different preset frequency bands and associated physiological indexes of the wave band components; then, the attention electroencephalogram signals of each patient are screened out from the electroencephalogram signals of all the channels, the attention coefficient of each preset frequency band is obtained, then attention signal components are extracted from the attention electroencephalogram signals of the patients, and finally the electronic medical record data of different patients are classified based on the attention signal components. According to the method, important signal components in the electroencephalogram signals are analyzed and extracted, so that the classification difficulty of the classification model and the coverage interference of non-brain activity channel signals on electroencephalogram activities during morbidity are reduced, and the electronic medical record data classification effect based on the electroencephalogram signals is improved.

Disclosed herein is a pair of EEG glasses or other electroencephalographic eyewear comprising an eyewear frame with a frontpiece and two sidepieces (e.g. temples), wherein there is an arcuate arm which extends out from the sidepiece toward the person's head and an electrode on the arcuate protrusion. The electrode collects data concerning the person's brain activity.

The invention discloses a nose bridge external electrical stimulation olfaction induction device and method, and belongs to the technical field of biomedical engineering. According to the method, the problems of unstable stimulation intensity, high subject adaptability, high subjectivity in olfactory judgment and lack of objective quantitative indexes due to the fact that odor stimulation is generally used in an existing method are solved, olfactory related electroencephalogram activities are induced by applying electrical stimulation to the outside of the nose bridge, traditional odor stimulation is replaced, and the sensitivity of the olfactory is improved. The stability and controllability of olfactory stimulation are improved; according to the method, electroencephalogram signals of historical subjects and evaluation results of the historical subjects are classified through a linear discriminant analysis model, so that corresponding injury levels and causes are quickly classified according to component characteristics, amplitudes and incubation periods of olfactory evoked potentials OERPs of the current subjects, and therefore, the injury levels and the causes are more accurately classified; potential subjects can be found in the early stage of diseases, and early screening and intervention are achieved; interference of human factors is avoided, and objectivity and accuracy of diagnosis are improved.

Sleep performance systems and methods of using the same are disclosed. The sleep performance systems can improve the quality of sleep by making one or more recommendations to the subject for increasing a sleep quality score. The sleep performance systems can have one or more electroencephalography (EEG) electrodes configured to measure a subject's brain activity during sleep. The sleep performance systems can have a processor configured to quantify the quality of the subject's slow-wave sleep by determining one or more sleep performance scores associated with the measured brain activity. The sleep performance systems can recommend and / or activate sleep improvement programs based on various threshold scores.

According to the method and the system for analyzing and predicting the occurrence of the mouse oxygen convulsion based on the electroencephalogram signals, the proportion of rats with the oxygen convulsion in the high-pressure and high-oxygen environment with different simulation depths which are gradually increased in sequence is compared with the incubation period of the oxygen convulsion, and the situation that the proportion of the rats with the oxygen convulsion is remarkably increased along with the increase of the simulation depths is analyzed; the oxygen convulsion incubation period of the rats is obviously shortened, and the individual difference of the rats is gradually reduced; the behavior expressions of the rats with the oxygen convulsion in the high-pressure and high-oxygen environment with different gradually-increased simulation depths before the oxygen convulsion attacks are compared, and it is analyzed that the behavior expressions before the oxygen convulsion attacks are gradually diversified along with increase of the simulation depths; frequency bands showing remarkable statistical differences in electroencephalogram activity comparison before and after the oxygen convulsion attack under each simulation depth are screened out to serve as target frequency bands, and then the same target frequency bands, namely beta and gamma frequency bands, in the target frequency bands under each simulation depth are screened out to serve as electroencephalogram indexes for early warning of oxygen convulsion in the high-voltage and high-oxygen environment.