5075 results about "Neuroscience" patented technology

The invention discloses an alimentary canal pathological diagnosis visual language large model construction method based on reinforcement learning and application thereof, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, extracting pathological information through layout analysis and adaptive threshold processing, and recombining the pathological information into a structured data set containing an inference chain; secondly, a visual encoder and a multi-branch classifier are used for extracting features and confidence coefficients, and dynamic structured cue words are generated; and finally, inputting the image and the cue word into a multi-modal large model, carrying out supervised fine-tuning hot start, and carrying out reinforcement learning training by adopting a group relative strategy optimization algorithm in cooperation with a composite reward function containing format, semantics and diagnosis dimensions. The problems that a general model is prone to generating illusion in a pathological scene and lacks reasoning logic are solved, and the accuracy and logicality of pathological report generation are remarkably improved.

A method for facilitating conversational interaction with users to help the users includes transmitting a conversational interaction interface for conversationally interacting with a user to a user device, receiving a request of the user through the conversational interaction interface from the user device, identifying an information based on the request, generating an input comprising the request and the information for a machine learning model based on the request and the information, processing the input using the machine learning model, generating a response for the request based on the processing of the input, transmitting the response through the conversational interaction interface for conversationally interacting with the user to the user device, and storing the machine learning model, the request, and the response.

The disclosure provides methods for treating a subject in need thereof comprising administering to the subject a therapeutically-effective dose of psilocybin. The methods described herein may be used to treat a variety of diseases, disorders, and conditions. For example, the methods may be used to treat anxiety disorders, eating disorders, and headache disorders.

Described herein are methods and systems for a conversational assistant that may be configured for use with one or more applications. The disclosed systems and methods may be configured as an outer loop / inner loop architecture incorporating one or more large language models (LLMs) and / or RAG functionality to support dialog-based interactions.

The invention relates to the technical field of cognitive impairment detection, and discloses a cognitive impairment early warning method and device based on an electroencephalogram micro state and an eye movement trajectory, and the method comprises the following steps: S1, data acquisition, S2, electroencephalogram preprocessing, S3, electroencephalogram feature extraction, S4, eye movement feature extraction, S5, feature fusion, and S6, early warning judgment. According to the method, through independent convolution branch of electroencephalogram and eye movement features, a receptive field is expanded by utilizing cavity convolution to capture multi-scale features, and long-distance dependence is modeled by a self-attention layer; after tensor splicing, time sequence information is dynamically fused through a gating cycle unit (GRU), and cross-modal time correlation is captured. The method has the advantages that the multi-modal feature hierarchical extraction and self-adaptive modeling capability is enhanced, the complementarity fusion efficiency is optimized, meanwhile, by means of cavity convolution sparse connection, self-attention parameter sharing and GRU lightweight design, the model complexity and the calculation efficiency are balanced, and efficient feature representation is provided for cognitive impairment early warning.

The present invention relates to a system for transforming static websites into artificial intelligence (AI)-enabled interactive multi-modal conversational platforms. The system comprises a computing device having a processor for receiving user queries as text or speech input through an input module cooperating with a speech-to-text module. A natural language processing (NLP) module interprets intent, classifies user context, and retrieves grounded information from multiple webpages. A persona adaptation module dynamically modifies vocabulary, tone, and avatar representation across roles such as sales assistant, recruiter, educator, healthcare professional, etc. A response generator module produces structured natural language output, transmitted to a text-to-speech synthesis module and an avatar generation module to render synchronized lifelike video responses. An output rendering module displays multi-modal responses include text, audio, and video, thereby enabling direct navigation and escalation beyond limitations of conventional static websites.

The invention discloses an old people cognitive ability assessment system and method based on multi-modal fusion, and relates to the field of old people cognitive assessment, and the method comprises the steps: firstly obtaining the current multi-modal data of a user, and fusing the current multi-modal data into a current feature vector; then, instead of being compared with the universality standard, the personal baseline portrait of the user is called, and the score of the difference degree between the current state and the historical baseline of the user is calculated; particularly, the core index of the drift speed is introduced, and the difference degree and the historical change rate are combined, so that quantitative modeling is carried out on the changed speed. By analyzing the amplitude and the speed of the change at the same time, normal aging with gentle change and low speed and pathological recession with violent change and high speed can be effectively distinguished, so that the key technical problem that the normal aging and the pathological recession are easy to be confused in the background technology is accurately solved.

A system that can deliver haptic feedback by applying an electrical stimulation to a first area of a user's body to induce a second area of the user's body to experience a level of natural sensation in response to an action occurring in a simulated remote environment and an intensity of the action is described. The system includes a controller to set parameters for the electrical stimulation based on the action occurring in the simulated remote environment and an intensity of the action. The system also includes a signal generator to generate the electrical stimulation comprising the parameters. The system also includes a skin surface electrode placed at a first location on a user's body remote from a second location on the user's body to deliver the electrical stimulation with the parameters to a nerve at or near the first area of the user's body.

The invention provides a transcranial stimulation control method, a transcranial stimulation control system and a computer readable storage medium, and the method comprises the steps: collecting an electroencephalogram parameter signal, and converting the electroencephalogram parameter signal into an electroencephalogram feature vector; inputting the electroencephalogram feature vector into a large language model, obtaining an initial electrical stimulation signal corresponding to the electroencephalogram feature vector from a stimulation electroencephalogram knowledge model according to the electroencephalogram feature vector, and generating an initial electrical stimulation signal scheme; the initial electrical stimulation signal is optimized through an attention mechanism according to historical response data of a user, a final electrical stimulation signal scheme is output, and the historical response data of the user comprises a historical electrical stimulation signal scheme and user feedback information; and outputting the final electrical stimulation signal scheme to a transcranial stimulation module. The method can improve the insomnia treatment efficiency of the user.

Described is a system for generating a virtual pet by determining participation in an interaction function by a first user of an interaction system with a second user of the interaction system; accessing profile data of the first user; determining, based on the profile data, whether the first user has a virtual pet for use in the interaction function; initiating display of the virtual pet with an avatar of the first user in an uncollapsed state in the interaction function to a computing device of the second user; determining that the first user is typing a message in the interaction function; and initiating display of the virtual pet with the avatar of the first user in a first state in the interaction function, the first state including a visual indicator indicating that the first user is typing the message.

An exemplary system and method are disclosed that is configured to detect cognitive impairment (e.g., early cognitive impairment) or assess cognitive function by analyzing, via machine learning and artificial intelligence analysis, behavioral metadata collected from a smart app during the course when a subject is using a cognitive test instrument for cognitive tests that incorporate motor activity (e.g., drawing or writing). The machine learning and artificial intelligence analysis can execute features associated with the test taker's metadata (e.g., time spent on task or questions, changing answers, referring back to the previous question), drawing qualities (e.g., line straightness, completeness), among others.

The invention relates to the technical field of medical image analysis, computer vision and deep learning, in particular to a texture perception and multi-scale feature adaptive fusion hip joint osteophyte detection method. According to the method, firstly, a bone texture feature extraction module is used for carrying out feature extraction and enhancement on a hip joint X-ray image, a parallel double-branch structure is adopted, gradient and space structure features are extracted through a Sobel operator and pooling operation, and feature maps are fused; then, the fused features are input into a double-backbone network, the first backbone network extracts local fine textures and long-range dependence features by combining convolution, self-attention and a gating mechanism, and the second backbone network extracts multi-scale semantic features through hierarchical grouping and depth separable convolution; and double-trunk output is dynamically weighted and fused through an adaptive fusion module, features are optimized through a multi-scale semantic fusion module, and finally the position and confidence of osteophyte are output.

A system and method for implementing a Persistent Cognitive Machine (PCMs) that extends beyond the traditional prompt-response paradigm of artificial intelligence are disclosed. A PCM maintains persistent cognitive processes regardless of external interaction, stores and organizes thoughts in a thought cache, retrieves relevant thoughts based on current stimuli, generates new thoughts through reasoning processes, and curates stored thoughts during periods of reduced external interaction. The PCM includes language and reasoning model components, a thought cache, an executive component, and an embedding system. The PCM remains continuously active, remembers previous experiences, learns from these experiences, creates new thought experiences independently, and initiates interactions without waiting for external prompts. The PCM enters sleep-like states during which it curates its thought cache, generalizes experiences, and performs other memory management functions. Applications may include but are not limited to synthetic cognitive colleagues, strategic war gaming platforms, and personal cognitive assistants.

The invention relates to a multi-modal virtual human real-time generation method based on an expression control embedding space, and belongs to the field of artificial intelligence. According to the method, an expression control embedding space is constructed and used for fusing voice semantics, a rhythm structure and multi-dimensional emotion information, and continuous and controllable multi-modal driving vectors are generated. The whole system has an end-to-end linkage mechanism from audio input to expression and action output. Semantic features, rhythm structures and emotional states jointly act on generation paths of lip and upper body postures and expression modalities, and all modal features are fused and expressed in a unified control space through a collaborative coding and time sequence alignment mechanism. And finally, a high-consistency and high-fidelity virtual human video is generated in real time through an output scheduling mechanism. The method has remarkable advantages in the aspects of modal fusion consistency, generation expression naturalness and emotion control flexibility, and can be widely applied to key scenes such as virtual human broadcasting, voice interaction agency and meta-universe digital identity construction.

The invention discloses an intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback, which comprises a multi-modal physiological signal acquisition module for acquiring electroencephalogram signals of a target child in real time and generating an original physiological signal set; the multi-modal fusion analysis module receives the original physiological signal set and generates a comprehensive evaluation signal representing the current neurocognitive state and the emotion awakening level of the child; the self-adaptive decision control module receives the comprehensive evaluation signal and generates a self-adaptive control signal containing a neural feedback parameter adjustment instruction and a game interaction strategy instruction; the neural feedback intervention module receives the neural feedback parameter adjustment instruction and generates an adaptive neural feedback stimulation signal to act on the child; and the intelligent interactive game module receives a game interactive strategy instruction and dynamically adjusts game scene contents. The intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback can solve the problems of poor individual adaptation of autism intervention, multi-modal data splitting and lack of dynamic adjustment.

The invention discloses a continuous attention neural feedback training method and system based on a brain-computer interface, and relates to the technical field of neural feedback, and the method comprises the steps: collecting a multi-channel electroencephalogram signal of a user in visual task training in real time; extracting power spectral density characteristics of the multi-channel electroencephalogram signals in a beta frequency band, classifying the power spectral density characteristics by adopting a support vector machine algorithm, and outputting a judgment result of an alert or non-alert state; and according to a judgment result, dynamically adjusting an information fusion proportion alpha value in the visual task through a reward-punishment mechanism, updating image information feedback in the visual task in real time, and adjusting the attention state of the user through an image information feedback result. Neural feedback and a dynamic reward and punishment system are fused, real-time excitation feedback is obtained by autonomously adjusting electroencephalogram activity, the problem of insufficient training power caused by traditional static tasks or single positive feedback is solved, and the long-term training effect is enhanced.

The invention relates to the technical field of man-machine interaction, and discloses an intelligent man-machine interaction system based on meta-universe, which is used for solving the problem of lagging caused by the fact that each virtual module possibly initiates response at the same time during man-machine interaction, and comprises the following steps: constructing a meta-universe scene, collecting multi-modal behavior information of a user in the meta-universe scene, and displaying the multi-modal behavior information of the user in the meta-universe scene. Performing unified formatting processing on the multi-modal behavior information to obtain formatted multi-modal behavior information, and performing joint reasoning on the formatted multi-modal behavior information by adopting a natural language processing engine and a multi-modal fusion model to obtain structured command data; the method comprises the following steps: obtaining a virtual module in a meta universe scene needing to make a response action according to structured command data, recording the virtual module as a driven module, obtaining priority information of all the driven modules, evaluating to obtain a priority index, and driving the modules to make the response action according to the priority index, thereby effectively improving the interaction response efficiency and the user experience continuity.

The disclosure provides methods for treating a subject in need thereof comprising administering to the subject a therapeutically-effective dose of psilocybin. The methods described herein may be used to treat a variety of diseases, disorders, and conditions. For example, the methods may be used to treat anxiety disorders, eating disorders, and headache disorders.

Methods and apparatuses are described for modulating multiple integrated neural networks to alter composite sensory processes, such as audition or hearing. These methods and apparatuses may be used for therapeutic and non-therapeutic uses, including enhancing entertainment and communication, by providing a cranio-cervical tuning apparatus worn in or around the outer ear or auricle. These methods and apparatuses may include functional neurosensory bias for neurosensory scrambling neuromodulation to influence composite or multi-modal sensory processes.

The invention discloses an abnormal sleep signal identification method for an intelligent sleep monitoring pillow, and relates to the technical field of abnormal sleep signal identification. Comprising the following steps of collecting multi-modal sleep physiological data, preprocessing the multi-modal sleep physiological data, extracting multi-dimensional features from the preprocessed data, constructing a fusion analysis model to judge abnormal sleep signals, triggering a hierarchical intervention mechanism and outputting associated intervention suggestions. According to the method, the time dimension alignment technology and the sliding window matching technology are adopted, the problem of data synchronization caused by multi-sensor sampling frequency difference in a traditional abnormal sleep signal recognition technology is solved, multi-source features are fused through deep learning, the accuracy of abnormal sleep signal recognition is remarkably improved, and the recognition accuracy of abnormal sleep signals is improved. An intelligent analysis model based on deep learning multi-network fusion is constructed, deep extraction and fusion judgment of complex sleep features are realized by utilizing cooperative processing of a convolutional neural network, a recurrent neural network and an attention mechanism, and the reliability of abnormal sleep signal recognition is improved.

Systems, devices, and methods for neurostimulation using a combination of implantable and external devices to treat pain are disclosed.

The disclosed system for interacting with objects in an extended reality (XR) environment generated by an XR system may include (1) neuromuscular sensors configured to sense neuromuscular signals from a wrist of a user and (2) at least one computer processor programmed to (a) determine, based at least in part on the sensed neuromuscular signals, information relating to an interaction of the user with an object in the XR environment and (b) instruct the XR system to, based on the determined information relating to the interaction of the user with the object, augment the interaction of the user with the object in the XR environment. Other embodiments of this aspect include corresponding apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

The invention provides a nerve stimulation system which comprises a stimulation device, the stimulation device comprises a signal generation circuit, a control module, a signal acquisition circuit and a reuse electrode, and the stimulation device is arranged to be implantable in a body, generates a stimulation signal and applies the stimulation signal to a target nerve through the reuse electrode; the signal generation circuit applies a titration stimulation signal to a target nerve according to a preset mode, the signal intensity of the titration stimulation signal in the preset mode is gradually increased to a peak value from an initial threshold value, and then the titration stimulation signal is generated after the peak value is kept for a period of time or some stimulation periods; gradually reducing to a termination threshold value and ending; when a titration stimulation signal is applied to a target nerve, the signal acquisition circuit can correspondingly acquire an evoked potential; and selecting the stimulation parameter with the lowest stimulation signal intensity in the evoked potential time period meeting the target threshold value as a parameter of a treatment stimulation signal for subsequent treatment stimulation signal application.

The invention relates to the field of traditional Chinese medicine, and discloses a traditional Chinese medicine composition for treating cognitive impairment of Parkinson's disease and application thereof.The traditional Chinese medicine composition is prepared from, by weight, 180-220 parts of vinegar tortoise plastron, 90-110 parts of morinda officinalis, 90-110 parts of angelica sinensis, 90-110 parts of radix curcumae, 55-65 parts of rhizoma acori graminei, 55-65 parts of polygala tenuifolia, 180-220 parts of calcined dragon bone, 180-220 parts of caulis spatholobi and 1-3 parts of borneol. When all the medicines of the traditional Chinese medicine composition are combined, the compatibility of monarch, minister, assistant and guide is performed, so that the traditional Chinese medicine composition has the effects of tonifying kidney, promoting blood circulation, dispersing blood stasis and inducing resuscitation, can relieve cognitive impairments such as space navigation, space exploration, working memory and the like of a Parkinson's disease cognitive impairment model mouse caused by chronic hypoperfusion, and realizes the treatment effect on the Parkinson's disease cognitive impairment; good application prospects are realized.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a multi-mode early cognitive impairment screening method and system based on medical history information.The method comprises the steps that a first electrode plate and a second electrode plate are arranged at the forehead position and the occipital position of the brain of a user to be detected in advance; the third electrode plate and the fourth electrode plate are arranged on the left side of the brain side by side, and the fifth electrode plate and the sixth electrode plate are arranged on the right side of the brain side by side. And obtaining a disturbance coefficient set of the to-be-detected user under the reference detection mode combination, inputting the disturbance coefficient set into a pre-trained early cognitive impairment screening model, and predicting to obtain a prediction probability value of the early cognitive impairment of the to-be-detected user, and by adopting the scheme to screen the early cognitive impairment, the accuracy is high, the universality is strong, and the accuracy is high. The method can be applied to screening of people in communities, nursing homes and the like on a large scale.

The invention belongs to the cross technical field of biomedical engineering and nerve regulation technology. According to the brain-heart linkage transcranial strong alternating current stimulation feedback control method and system, heart rate variability characteristics are obtained according to electroencephalogram signals, electroencephalogram signal characteristics are obtained according to the electroencephalogram signals, and a dynamic coupling index is determined according to the electroencephalogram signals and electrocardiosignals; when the heart rate variability characteristic is greater than or equal to a corresponding heart rate characteristic baseline threshold value, the electroencephalogram signal characteristic is greater than or equal to a corresponding electroencephalogram characteristic baseline threshold value, and the dynamic coupling index is greater than or equal to a set threshold value, judging that the emotional disorder is improved, and keeping the intensity of the stimulation current unchanged; otherwise, increasing the intensity of the stimulation current to a set threshold value until the emotional disorder is improved. Through combined calculation of the electroencephalogram signals and the electrocardiosignals, dynamic monitoring and accurate evaluation of the process of treating the emotional disorder through transcranial strong alternating current stimulation are achieved, and the accuracy of electrical stimulation is guaranteed.

The invention discloses a borborygmus detection system and method based on multi-sensor fusion and deep learning, and the method comprises the following steps: S1, constructing a multi-mode sensor array for collecting abdominal acoustics, vibration and motion signals; s2, adaptive filtering, noise suppression and data synchronization preprocessing are carried out on the collected signals; s3, performing multi-sensor data fusion and voting on the filtered signal; s4, constructing a deep learning classification model; according to the method, acoustics, vibration and motion signals are synchronously collected based on a multi-mode sensor, abnormal data are eliminated through a voting algorithm, a fusion result is optimized in combination with Kalman filtering, filter parameters can be dynamically adjusted according to priori knowledge of borborygmus, key frequency bands are reserved, and characteristics of different borborygmus types are extracted in a targeted mode. The classification accuracy is improved by more than 15%, high-precision classification is realized, and F1-score of normal / hyperfunction / weakening sound on a test set reaches 92.5%.