55 results about "Neural engineering" patented technology

The invention belongs to the cross technical field of rehabilitation engineering and neural engineering, and relates to a multi-modal signal fused fine exercise rehabilitation evaluation, regulation and control method and system, and each evaluation comprises the following steps: collecting a pressure distribution signal, a surface electromyogram signal and an electroencephalogram signal of a hand fine exercise; performing preprocessing, feature extraction and normalization processing on the three signals to obtain a pressure feature value, a surface myoelectricity feature value and an electroencephalogram feature value; calculating a conversion coefficient of a current rehabilitation stage based on the number of days that the fine motor function of the patient is damaged, and calculating weights of a pressure characteristic value, a surface myoelectricity characteristic value and an electroencephalogram characteristic value based on the conversion coefficient; performing weighted summation on the pressure characteristic value, the surface myoelectricity characteristic value and the electroencephalogram characteristic value to obtain an evaluation score; rehabilitation training parameters are regulated based on the assessment score and the number of days of impaired patient's fine motor function. According to the method, the limitation of traditional single-mode physiological signal evaluation can be broken through, and the comprehensiveness, the accuracy and the personalized adaptation capability of rehabilitation evaluation can be improved.

The invention relates to the technical field of neural engineering and brain-computer interfaces, in particular to a closed-loop multi-modal nerve stimulation system and method based on time interference, and the system comprises a multi-modal stimulation module which is used for integrating electrical stimulation, magnetic stimulation and optical genetic stimulation, and generating a time interference field domain; the neural state sensing module is used for collecting real-time electroencephalogram signals, blood oxygen concentration and neural metabolite level data; the neural control center is used for fusing neural state data and stimulation parameters, and dynamically adjusting time interference frequency and stimulation intensity through an adaptive algorithm; the time sequence cooperation engine predicts a neural response time phase based on a deep learning model, and optimizes a stimulation time sequence and a mode switching strategy; and the visual interaction platform is used for rendering the nerve activation thermodynamic diagram and the stimulation parameter adjustment curve in real time. Therefore, the problems of adjustment strategy solidification, low adjustment precision, insufficient energy conversion efficiency and the like in the prior art are solved.

The invention provides a cognitive function in-vitro simulation method based on a microelectrode array (MEA) and brain organs and application of the cognitive function in-vitro simulation method, and belongs to the crossing field of brain organ in-vitro model construction, neural engineering, brain-like intelligence and the like. In an in-vitro environment, external signal (such as sound) information is converted into electrical stimulation input capable of being perceived by brain organs in an MEA electrical stimulation mode, so that the brain organs gradually learn and identify external signals in repeated electrical stimulation; after elution stimulation for a period of time, the reduction condition of recognition accuracy is observed, namely, the important memory and forgetting process in the cognitive function is simulated. According to the method, the electrophysiological response and the biological mechanism change of the brain organs under external electrical stimulation can be quantitatively evaluated, and a new possibility is provided for exploring the advanced cognitive function by utilizing the brain organs.

The invention belongs to the cross technical field of rehabilitation engineering and neural engineering, and relates to an exercise rehabilitation system and method based on layered stimulation navigation and a closed-loop brain-computer interface, and the exercise rehabilitation system comprises a layered stimulation navigation module, a brain-computer interaction module, a multi-channel functional electrical stimulation module and an exercise feedback module. The layered stimulation navigation module outputs different stimulation schemes for the superficial layer muscle stimulation area and the deep layer muscle stimulation area; the brain-computer interaction module is used for outputting a decoding result capable of representing the motion intention of the convalescent; the multi-channel functional electrical stimulation module calls a stimulation scheme according to a decoding result, loads an injection current to a working electrode determined by the stimulation scheme, implements electrical stimulation and generates a motion response; the motion feedback module collects and fuses the multi-mode feedback signals under the motion response, generates a stimulation parameter adjustment signal used for adjusting a stimulation scheme executed in the previous rehabilitation period, and outputs a neural feedback signal used for guiding the rehabilitation person to adjust the motion imagination.

The invention relates to the technical field of rehabilitation medical treatment and neural engineering, and particularly discloses an epidural electrical stimulation lower limb rehabilitation system and method based on a brain-computer interface, the epidural electrical stimulation lower limb rehabilitation system comprises a weight reduction suspension device, an epidural electrical stimulation module and a brain-computer interface module, the epidural electrical stimulation module controls left and right electrical stimulation through a program controller, and the brain-computer interface module comprises a wireless electroencephalogram acquisition device and an interaction terminal and is used for acquiring electroencephalogram signals of a movement area of a patient and presenting a virtual gait scene and voice prompt at a computer end. The method comprises the steps of electroencephalogram signal collection and feature extraction, motion intention judgment, stimulation control and feedback backtracking, and by combining a motion intention decoding result with electrical stimulation output, activation of a lower limb target muscle group is achieved, a training closed loop is constructed, and the method is used for postoperative rehabilitation of a spinal cord injury patient.

The invention relates to the field of electroencephalogram signal processing and neural engineering, and discloses a multi-mode electroencephalogram feature fusion decoding method. The method comprises the following steps: carrying out noise reduction, calibration and standardization processing on an original electroencephalogram signal to generate a standard signal; extracting mu / beta rhythm time-frequency features and Hjorth time-domain features of the signals, and generating a fusion feature vector through PCA dimension reduction fusion; performing pre-classification and weight optimization by using an SVM classifier, performing space and time sequence feature extraction and classification through a CNN-LSTM network, and outputting a classification probability; model parameters are updated according to the probability and verified, and finally real-time control signals suitable for various communication interfaces are generated. According to the method, the accuracy and robustness of electroencephalogram signal decoding are improved, and efficient and self-adaptive motor imagery brain-computer interface control is realized.

The invention discloses an electroencephalogram signal decoding method and system based on characteristic decomposition and adversarial training, and relates to the technical field of brain-computer interfaces and neural engineering. Then, the extracted features are decomposed into target feature vectors and irrelevant feature vectors through a feature decomposition module; information related to a motor imagery task is reserved in a target feature vector through supervised learning of a classification module, meanwhile, task related information is not included in an irrelevant feature vector through confrontation training of a judgment module and a feature decomposition module, and therefore separation of target features and individual irrelevant features is achieved. In the test stage, only the target feature vectors are used for motor imagery task classification, individual differences are effectively overcome, and the generalization ability of the model across subjects is improved.

The invention discloses an instrument implantation safety analysis method under regulation and control of a brain-computer interface and spinal nerve, and relates to the technical field of crossing of biomedical engineering and neural engineering. According to the model, a BCI decoding instruction and an actually-collected multi-modal physiological signal are used as input, parameter identification continuous optimization is carried out, the difference between actual spinal cord response and expected response generated by the model is calculated, a predicted value and an actual value of a coupling dynamics model are compared in real time, a risk value is dynamically calculated, once a threshold value is exceeded, risk leading factors are locked immediately, and the risk leading factors are locked immediately. According to the method, a BCI-SCN system is constructed, a high-specificity self-adaptive adjustment mechanism is triggered, it is ensured that the BCI-SCN system always works in a safe interval in a complex and changeable human nerve environment, macroscopically observed abnormal phenomena are accurately traced to specific reasons of a microscopic level, and a decision basis is provided for subsequent accurate regulation and control.

The invention relates to the field of biological robots and neural engineering, and discloses an electrical stimulation control protection device for a turtle robot, which comprises a neck biocompatibility limiter, the neck biocompatibility limiter is of a sleeve structure and is composed of a rigid clamping part and a flexible limiting disc, the neck of the turtle robot penetrates through the rigid clamping part and the flexible limiting disc, and the flexible limiting disc is connected with the neck of the turtle robot. The flexible limiting disc abuts against the tortoise shell of the tortoise robot, the rigid clamping part abuts against the skull of the tortoise robot, the rigid clamping part limits head retraction of the tortoise robot, and the flexible limiting disc is used for ensuring the freedom degree of the tortoise robot in other directions. The mechanical protection, sealing performance and diversion performance of the waterproof sealing electrical stimulation sleeve module ensure the position stability of the electrode under long-term violent activity and water flow impact.

The invention belongs to the field of biomedical engineering, and particularly relates to an amino acid modified molybdenum disulfide artificial extracellular matrix and application. Preparing an amino acid modified molybdenum disulfide artificial extracellular matrix, inoculating the neuron in-vitro model PC12 cell suspension onto the artificial extracellular matrix, and constructing the nerve cell in-vitro differentiation model. The artificial extracellular matrix with good biocompatibility is obtained by constructing different kinds of amino acid modified molybdenum disulfide nano material artificial extracellular matrixes as the artificial extracellular matrix of nerve cells PC12, and the artificial extracellular matrix improves the activity of the PC12 cells and promotes the differentiation of nerve cell synapses; a neuron in-vitro model with high differentiation and higher activity is constructed, and the artificial extracellular matrix can be used for nerve regeneration and neural engineering.

The invention relates to the technical field of brain-computer interfaces and neural engineering, in particular to a mechanical arm control method based on electroencephalogram function signals and an artificial neural network. The method comprises the following steps: collecting a multi-channel electroencephalogram signal during motor imagery of a user; performing step-by-step spatial-temporal feature extraction and fusion on the signal by using a pre-trained cascade sparse self-encoding neural network to obtain a feature code; a motion intention is decoded through a Softmax classifier; and a control instruction is generated according to the decoding result to drive the mechanical arm to execute actions. Wherein the neural network training adopts sparsity constraint based on KL divergence to learn robust features; the system is also integrated with a graphical user interface, provides real-time visual feedback and can adaptively adjust task difficulty according to user performance to form closed-loop interaction. According to the method, high-precision and self-adaptive decoding of electroencephalogram signals and natural and smooth mechanical arm control are achieved, and the method is suitable for rehabilitation training and man-machine cooperative operation.

The present invention provides an in vitro simulation method for cognitive function based on a microelectrode array (MEA) and brain organoids, and its use, which belongs to the interdisciplinary fields of in vitro model construction of brain organoids, neural engineering, and brain-like intelligence. In an in vitro environment, the present invention uses MEA electrical stimulation to convert external signal information (such as sound) into electrical stimulation input that can be perceived by brain organoids, allowing brain organoids to gradually learn and recognize external signals through repeated electrical stimulation; after a period of washout stimulation, the reduction in recognition accuracy is observed, thereby simulating the important memory and forgetting processes in cognitive function. This method can quantitatively evaluate the electrophysiological responses of brain organoids under external electrical stimulation and changes in their biological mechanisms, providing new possibilities for using brain organoids to explore advanced cognitive functions.

The invention provides a multi-level team cooperation performance evaluation method and system based on EEG superscanning, and belongs to the field of neural engineering and human-computer interaction. The method comprises the steps that electroencephalogram signals and behavior performance signals are synchronously collected by designing a multi-person team cooperation task; calculating an average time difference based on the behavior signal to quantify a collaboration level and grade; the electroencephalogram signals are preprocessed; extracting multi-task-stage, multi-frequency-band and multi-index interbrain connectivity features to construct a high-dimensional feature set; single-stage and cross-stage fusion models are trained by adopting a machine learning method to perform collaborative level classification, and key neural features are identified by utilizing an interpretability method; and finally, constructing and analyzing a team-level brain function network based on a graph theory, and revealing neural network characteristic differences of teams with different cooperation levels. According to the method, multi-dimensional and dynamic analysis of a team collaboration neural mechanism is realized, and a new objective and quantitative approach is provided for team performance evaluation and collaboration capability improvement.

The invention discloses a portable non-invasive dream analysis and regulation system and method, and relates to the technical field of brain science and neural engineering. According to the system, an improved high-density electroencephalogram (HD-EEG) is combined with a miniaturized 7T ultrahigh-field-intensity functional magnetic resonance (fMRI) module, brain activity signals in a fast eye movement sleep (REM) stage are collected through a flexible bio-compatible nano sensor array, a brain activity-visual image mapping model is constructed by utilizing a machine learning algorithm, 720P-level dynamic visual image reconstruction is realized, and the dynamic visual image reconstruction is realized. And the dream emotion is regulated and controlled based on a dual-channel activation mode. The system solves the problems of large equipment size and strong invasiveness in the prior art, can be applied to the fields of psychotherapy, education and the like, and has the advantages of high portability, non-invasiveness, high analysis precision, accurate emotion regulation and control and the like.

The present application relates to a kind of based on motor imagination trigger's electric stimulation synchronous closed loop multimodal neuroregulation system, belong to medical rehabilitation engineering and neural engineering technical field.The system includes: electroencephalogram signal acquisition and processing module, for real-time acquisition user sensory motor cortex electroencephalogram signal, and extract the event-related desynchronization features of motor imagination task in the electroencephalogram signal by time-frequency analysis model;Motor intention recognition and trigger module generates synchronous trigger signal;Functional electric stimulation module, through multi-channel electrode device drive surface electrode output electric stimulation pulse to target area, drive target action;Transcutaneous auricular vagus nerve electric stimulation module, output and motor intention duration synchronous stimulation pulse;Central control and synchronization module, for coordination control timing.Formed " intention recognition-synchronous control-assisted movement" complete closed loop, provides a kind of efficient, accurate, individualized neural engineering solution.

The invention discloses a multi-modal sensor integrated module, which relates to the technical field of neural engineering and comprises a rubber surface template, a rubber surface rear cover fixedly mounted at the bottom of the rubber surface template, a collection mechanism mounted between the rubber surface template and the rubber surface rear cover, and a stimulation mechanism and a vibration module mounted between the rubber surface template and the rubber surface rear cover in a staggered manner. The collecting mechanism is installed between the rubber face template and the rubber face rear cover, the stimulation mechanism and the vibration module form a pillow detection part, and the control assembly is installed between the rubber face template and the rubber face rear cover. By means of the cooperative use mode of the collection mechanism, the stimulation mechanism, the vibration module and the control assembly and the multi-module embedded arrangement mode, signals of different brain areas can be automatically switched and collected according to sleeping postures, contacts of the head and the pillow are randomly changed, the problem that body burdens are caused by tedious calibration of a stroke or paralyzed patient is solved, and the safety of the patient is improved. The influence on rehabilitation experience and treatment compliance is reduced, and the signal acquisition quality can be improved.

The present application relates to the field of biological robots and neural engineering, and discloses an electric stimulation control protection device for a turtle robot, which comprises a neck biocompatible limiter in a sleeve structure and is composed of a rigid clamping part and a flexible limiting disc, the neck of the turtle robot passes through the rigid clamping part and the flexible limiting disc, the flexible limiting disc abuts against the carapace of the turtle robot, the rigid clamping part abuts against the skull of the turtle robot, the rigid clamping part limits the retraction of the turtle robot, and the flexible limiting disc is used for ensuring the freedom of the turtle robot in other directions. The mechanical protection, sealing performance and flow guiding performance of the waterproof sealed electric stimulation sleeve module guarantee the position stability of the electrode under long-term intense activity and water flow impact.

The invention relates to the technical field of medical implantable optical and electrical integrated devices, in particular to a composite functional degradable optical fiber and a preparation method and application thereof. The composite functional degradable optical fiber provided by the invention has the characteristics of biodegradability and high flexibility, permanent foreign body reaction can be avoided, and the biocompatibility is greatly improved. According to the composite functional degradable optical fiber provided by the invention, based on the unique physical characteristics of the up-conversion nanoparticles, the inherent synergistic treatment of PBM and PDT under the driving of a single near-infrared light source is realized. According to the composite function degradable optical fiber provided by the invention, a treatment-feedback closed-loop system architecture of minimally invasive monitoring is integrated, and a good hardware basis is provided for realizing self-adaptive treatment. The composite functional degradable optical fiber provided by the invention can be applied to the field of medical implantation instruments, the field of neural engineering or the field of biological materials, and particularly can be applied to brain implantation materials or brain degenerative disease self-adaptive treatment systems.

The application discloses a kind of artificial synapses based on layered multi-metal oxide and preparation method thereof, the artificial synapses with aluminum foil as bottom electrode, with layered multi-metal oxide Na 0.66 Mg 0.28 Mn 0.72 O2 As functional layer, with pectin composite film as solid-state electrolyte, with Au as top electrode.The artificial synapses disclosed in the application has simple structure, simple and easy-to-operate preparation method, uses green and non-toxic pectin composite film as solid-state electrolyte, has good biological compatibility, absolute short-range plasticity and high synaptic plasticity, high current response, and fast current reset speed.In the pectin composite film used as solid-state electrolyte of artificial synapse, NaCl is doped, which can further improve the current response and speed up the current reset speed.The application has wide application prospect in the fields of artificial intelligence, big data processing, pattern recognition, neural engineering and brain-computer interface.

The embodiment of the invention provides a visual dysfunction detection method and related equipment, and belongs to the technical field of medical signal processing and neural engineering. The method comprises the following steps: carrying out preprocessing and source space positioning on electroencephalogram signals, and reconstructing cortical neural activity; a dynamic k-nearest neighbor algorithm based on inverse distance weighting is adopted, cortex vertexes are adaptively allocated to a brain region of an AAL map, and the problem of space mismatching is solved; extracting time sequences of the visual network and the default mode network in a frequency band, and calculating dynamic function connection of the time sequences to construct a spatial-temporal feature tensor; the tensor is input into a multi-scale controllable graph convolutional network model for classification prediction, the network adopts similar Chebyshev connection and a double-gating unit to enhance the global feature extraction capability, and a residual cavity module is fused to capture multi-scale features. The accuracy, reliability and neurophysiological interpretability of visual dysfunction detection are effectively improved, and an objective basis is provided for clinical precise diagnosis and treatment.

The embodiment of the present disclosure provides a non-invasive brain-computer interface system based on earphone real-time electroencephalogram monitoring-auditory stimulation, and relates to the technical field of neural engineering. The system comprises an in-ear earphone device and an external control device. The in-ear earphone device is used for collecting the brain wave signals of a user, transmitting the brain wave signals to the external control device, generating a sound stimulation signal based on a sound stimulation trigger instruction, and delivering the sound stimulation signal to the ear of the user for sound stimulation. The external control device is used for detecting whether there is a characteristic sharp-slow wave signal corresponding to absence epilepsy attack in the brain wave signals. When the characteristic sharp-slow wave signal corresponding to the absence epilepsy attack is detected, the target sharp-slow wave of the brain wave signals is extracted, a sound stimulation trigger instruction is generated based on the sharp wave component of the target sharp-slow wave, and the stimulation trigger instruction is transmitted to the in-ear earphone device. Real-time and accurate monitoring and non-invasive closed-loop intervention of absence epilepsy attack can be realized, the system has high safety, and is portable and comfortable.

The invention discloses a holographic foreign language learning intelligent synergy system, and relates to the intelligent education hardware and neural engineering cross technology field, and the system comprises a head wearable communication device, an integrated visual display unit, an auditory feedback unit, a motion capture unit and a central processor; the cloud scene memory strengthening module is used for storing a multi-language dictionary, a grammar database, a scene simulation scene and a real-time interaction program; and the multi-sensory nerve collaborative activation module synchronously stimulates the nerve center of the user through the following paths, and the system has the effect of optimizing memory maintenance: in combination with a scenario review strategy of a memory rule, learning content retention can be reinforced at key nodes, and the frequency of repeated learning is reduced; the virtual scene training provides language interaction experience similar to reality, and the adaptation process from learning to practical application can be shortened.

The invention belongs to the technical field of crossing of neural engineering and sleep medicine, and provides a closed-loop transcranial electrical stimulation system and method based on cerebrospinal fluid and vascular resonance regulation, and the method comprises the steps: firstly collecting related data of the diameter of a blood vessel and related data of the flow velocity of the cerebrospinal fluid; then, according to the related data of the blood vessel diameter and the related data of the cerebrospinal fluid flow velocity, the phase difference between the blood vessel and the cerebrospinal fluid is obtained by means of a maximum independent variable point set function; determining an electrical stimulation frequency according to the phase difference between the blood vessel and the cerebrospinal fluid and a preset relationship between the phase difference and the stimulation frequency; and finally, performing transcranial electrical stimulation according to the determined electrical stimulation frequency. According to the invention, cerebrospinal fluid flow and blood vessel factors are considered, and real-time dynamic determination and adjustment of the electrical stimulation frequency are realized through the determined phase difference between the blood vessel and the cerebrospinal fluid, so that the electrical stimulation effect is improved, and the sleep quality under electrical stimulation is ensured.

The invention relates to the field of biological robots, neural engineering and multi-sensor data fusion, and discloses an experimental platform for constructing an electrical stimulation-behavior mapping relation, which comprises a behavior identification and data marking module used for identifying and judging the behavior of a tortoise robot according to a state vector sequence and marking the behavior of the tortoise robot according to the state vector sequence. Obtaining a behavior label under the current electrical stimulation parameter; and associating and recording the state vector sequence, the behavior tag, the current electrical stimulation parameter and the video timestamp to generate a structured database. The problems of low manual observation efficiency, high subjectivity and difficulty in data association in traditional research are solved, and an efficient, objective, high-throughput and high-fidelity data acquisition and analysis platform is provided for systematically researching a complex mapping relation between nerve electrical stimulation and animal behaviors.

The invention discloses targeted nerve regulation speech rehabilitation equipment and a use method, relates to the technical field of neural engineering crossing, and solves the problems that traditional speech rehabilitation depends on therapist guidance or single-point stimulation, lacks closed-loop control, is large in positioning error, heavy in equipment and uncomfortable to wear. A concentric annular array electrode is embedded into an adjustable silica gel ring belt, the electrode is connected with a main control stimulation box through an FPC wire, the electrode, a Bluetooth terminal and a microphone form a real-time voice-myoelectricity collection and self-adaptive stimulation system, and 0.5-1.5 mA pulses are output according to the comprehensive deviation S to precisely regulate and control speech related nerves. The width of the ring belt is 25mm, 24 [phi] 4mm Ag / AgCl electrodes are contained, the weight of the whole machine is 85g, and IPX4 is used for preventing sweat.

The invention relates to the cross technical field of integrated circuits and neural engineering, discloses an in-memory adaptive clock synchronization method and chip for a multi-mode brain-computer interface signal, and aims to solve the problems of large phase jitter, high power consumption and slow dynamic response caused by large sampling rate difference and dependence on an external non-adaptive phase-locked loop in the prior art. According to the method, instantaneous energy features of high-frequency electroencephalogram signals are extracted in real time in a storage and calculation integrated feature extraction array, phase adjustment control signals are generated through an energy-to-phase module, and a local ring oscillator is dynamically adjusted to generate a self-adaptive low-frequency sampling clock which is used for triggering sampling of low-frequency near-infrared spectrum signals and binding a high-precision timestamp. The system comprises a near infrared spectrum signal acquisition module, a storage and calculation integrated feature extraction array, an energy-to-phase module, a digital control ring oscillator, a timestamp generation module and a state synchronization control module. According to the invention, through a storage and calculation integrated closed-loop feedback mechanism, sub-picosecond-level synchronization precision, ultra-low power consumption and high-sensitivity response to key neural events are realized.

This invention discloses a flexible electrode array, system, and manufacturing method for closed-loop neural modulation, relating to the fields of neural engineering, medical devices, and biosensor technologies. The flexible electrode array includes a flexible biocompatible substrate, a recording electrode array, and a stimulating electrode array. The recording sites are arranged in a matrix, and the stimulating sites are coplanar with the recording sites, located between or within rows of the recording electrode array, achieving a spatially co-located layout of recording and stimulation. The system also includes an external control unit for receiving neural signals acquired by the recording electrode array and dynamically adjusting the stimulation parameters applied to the stimulating electrode array based on the neural signals. This application improves biocompatibility, achieves high-resolution signal mapping, and enables efficient closed-loop neural modulation by shortening the spatiotemporal distance between stimulation and monitoring.

The invention discloses a flexible micro electrical stimulator capable of being wound and adhered to peripheral nerve tracts and a preparation method and application thereof, and relates to the technical field of crossing of neural engineering, bioelectronics and medical instruments. A first insulation layer, a first friction electrode layer, a middle isolation layer, a second friction electrode layer and a second insulation layer are assembled layer by layer; the first insulation layer and the second insulation layer are self-adhesion polyurethane layers, and the first friction electrode layer, the middle isolation layer and the second friction electrode layer are packaged in the first insulation layer and the second insulation layer; the first friction electrode layer is a gold foil, and the surface of the gold foil is electroplated with a PEDOT: PSS plating layer formed by a 3, 4-ethylenedioxythiophene monomer and polystyrene sulfonate; the second friction electrode layer is gold foil or aluminum foil. According to the invention, macro-scale contact-separation type friction power generation is realized through ultrasonic driving, and the device has good output performance, long-term stability and nerve stimulation effect.

This invention belongs to the fields of medical image processing, neural engineering, and artificial intelligence-assisted diagnosis. It provides a Parkinson's disease identification system, medium, and product based on brain network control energy characteristics. Based on the time series of brain region functional activities, it extracts brain states at rest and calculates the temporal dynamic characteristics of each brain state. Using the whole-brain structural connectivity matrix as the network topology, it constructs a linear network control model to calculate the optimal control energy characteristics for brain state transitions and the energy regulation capacity characteristics of whole-brain regions. Based on brain dynamics feature sets, brain network energy feature sets, and brain network graph theory feature sets, it extracts a feature set significantly related to Parkinson's disease and constructs a multi-dimensional fusion feature matrix. Using this multi-dimensional fusion feature matrix as input, it trains the Parkinson's disease identification model. Based on the multimodal brain imaging data of the subject and the trained Parkinson's disease identification model, it outputs the Parkinson's disease identification result. This achieves non-invasive, accurate, and early identification of Parkinson's disease.

The present invention relates to wearable smart devices and neural engineering, disclosing a cognitive accessory that combines a brain-computer interface (BCI) with smart glasses, which includes an in-ear electrode module equipped with a plurality of EEG electrodes, a smart glasses frame with temples on both sides, a nose pad at the bottom of the frame, and two lenses installed on the frame. The smart glasses frame integrates a non-invasive brain signal acquisition module, an environmental perception module, an embedded processor, and an AR display module. The brain signal acquisition module uses flexible electrodes distributed inside the nose pad and the temples. By integrating flexible EEG electrodes and miniature fNIRS probes within the smart glasses frame, users can achieve efficient, non-invasive monitoring of brain electrical activity and cerebral blood oxygenation without wearing traditional complex headgear. This design makes the device more lightweight, comfortable, and convenient for long-term wear.