86 results about "Prefrontal lobe" patented technology

The invention discloses a self-adaptive music intervention system based on multi-modal physiological feedback, and belongs to the technical field of monitoring and intervention of emotional states, the self-adaptive music intervention system comprises a multi-modal emotional state evaluation module and a self-adaptive music intervention module, the multi-modal emotional state evaluation module acquires multi-modal physiological information of a user, and the self-adaptive music intervention module performs music intervention on the multi-modal emotional state evaluation module; feature extraction and fusion are carried out on the multi-modal data, and the emotional state is evaluated according to the fused data; and the adaptive music intervention module generates music features matched with the current emotion according to the evaluated emotion state, the music features and preference data of the user, and selects music according to the music features to intervene the user. By the adoption of the method, the wearable forehead lobe three-lead electroencephalogram equipment and the intelligent bracelet are combined, physiological signals of the central nervous system and the autonomic nervous system are collected and analyzed in real time, music intervention is automatically adjusted, a personalized emotion adjusting scheme free of side effects is provided, and a closed-loop mental state regulation and control mechanism is formed.

The invention discloses a psychological health monitoring and self-adaptive nursing decision-making system and method based on electroencephalogram signals, and relates to the field, and the method comprises the following steps: S1, collecting electroencephalogram signals and eye movement characteristics, and generating a multi-modal original signal data set; s2, electroencephalogram alpha wave asymmetry and eye movement characteristics are extracted, and delta-alpha spectrum overlapping pathological interference is eliminated; s3, inputting an evaluation model and a rule base, matching a DLB pathological marker and a symptom association rule, and marking a mental health risk level; and S4, matching an intervention strategy, and dynamically updating the evaluation model and the symptom association rule. The multi-modal fusion strategy effectively covers an association path of a DLB patient from molecular pathology to clinical symptoms, further provides a high-reliability data basis for subsequent risk level marking, realizes extraction of pure alpha wave energy and correction of prefrontal lobe alpha wave asymmetry aiming at frequency spectrum overlapping interference of delta waves and alpha waves in electroencephalogram signals, and improves the accuracy of risk level marking. Emotion abnormal degree misjudgment caused by false increase of the FAA value is avoided.

The invention discloses a multi-modal feature combined depression auxiliary diagnosis system. The system comprises a sampling unit which is used for constructing a multi-modal depression data set by acquiring a depression screening scale, an electroencephalogram, a magnetoencephalogram and functional magnetic resonance imaging based on acquisition equipment; the feature extraction unit is used for extracting multi-modal brain features based on the depression data set, and the multi-modal brain features comprise power spectral density obtained by electroencephalogram signals, event-related potential, micro-state, prefrontal lobe gamma frequency band power spectral density obtained by magnetoencephalogram and event-related magnetic field; gray matter volume and resting state functional connection density are obtained through functional magnetic resonance imaging; a data preprocessing unit; the diagnosis model unit is used for constructing a multi-modal depression diagnosis model and training the model on the basis of the multi-modal brain features in combination with a fusion strategy; and an analysis and prediction unit. The extracted features are comprehensive and reasonable, the defect of each mode is overcome by the feature fusion method, and the fused features are advanced.

The invention discloses a transcranial electrical stimulation control method and system for enhancing working memory, and the method comprises the steps: collecting and preprocessing a multi-channel endogenous electroencephalogram signal of a target object during the execution of a cognitive task, and obtaining a to-be-analyzed electroencephalogram signal; aligning the to-be-analyzed electroencephalogram signal with a behavior expression label generated by the target object during execution of the cognitive task to obtain an associated signal; according to the correlation signal, obtaining a channel pair with the highest phase synchronism between the forehead lobe and the apical lobe of the target object as a target channel pair; based on the associated signal data of the target channel pair, extracting frequency band features to generate stimulation parameters of radial and cranial electrical stimulation; and outputting transcranial electrical stimulation according to the stimulation parameters, and collecting endogenous electroencephalogram signals during stimulation in real time, so that the stimulation parameters are dynamically adjusted, and closed-loop regulation and control on the brain area of the target object are realized. Therefore, by the adoption of the method and device, accurate control over the endogenous phase difference between the prefrontal lobe and the apical lobe can be achieved, and therefore the work memory performance is enhanced.

The invention belongs to the technical field of biological medicines, and particularly relates to application of an expression inhibitor of an NR4A1 gene in preparation of a medicine for treating wine-dependent withdrawal. It is found that after wine-dependent withdrawal, mice have anxiety behaviors, the NR4A1 level of the prefrontal lobe is increased, and apoptosis-related protein in the prefrontal lobe is increased. Knockout of the forehead NR4A1 gene intervenes the expression level of the NR4A1 gene in the forehead lobe of the mouse, it can be proved that down-regulation of the expression of the forehead lobe NR4A1 gene can rescue apoptosis and damage, caused by alcohol, of cortical neurons of the forehead lobe, relieve anxiety behaviors caused by alcohol withdrawal of the mouse, and have the effect of protecting the brain of the mouse; the inhibitor DIM-C-pPhOH of the NR4A1 protein can reduce the expression of the NR4A1 gene of the prefrontal lobe of an alcohol withdrawal mouse and anxiety-like behaviors caused by alcohol withdrawal, and provides related therapeutic targets and new therapeutic drugs for anxiety dysfunction caused by clinical alcohol dependence and alcohol dependence withdrawal.

The invention discloses an emotion analysis method and system based on multi-mode electroencephalogram eye movement fusion. An emotional induction stimulation sequence is alternately or synchronously displayed through a stimulation presentation module according to preset time sequences such as 5-second vision and 3-second auditory sense, a 64 conductive electrode cap (10-20 system layout, the sampling rate is larger than or equal to 1000 Hz) is triggered to collect electroencephalogram data of a prefrontal lobe, a temporal lobe and the like, and meanwhile eye movement data are obtained through an infrared pupil tracking technology (the sampling frequency is larger than or equal to 120 Hz). 0.5-70 Hz band-pass filtering and ICA artifact removal processing are carried out on the electroencephalogram data, smooth interpolation and Kalman filtering optimization are carried out on the eye movement data, and then electroencephalogram alpha / beta / gamma wave power spectrums, eye movement pupil change rates and other characteristics are extracted respectively. A Transform model based on an attention mechanism is adopted, deep fusion of electroencephalogram eye movement features is realized through feature embedding and multi-head self-attention calculation, and emotion recognition accuracy greater than or equal to 85% is achieved through confusion matrix optimization and five-fold cross validation in combination with an SVM or DNN classifier.

The invention relates to the technical field of medical health data information decoding analysis and processing, and particularly discloses a dynamic pain nerve decoding and intervention system based on a prefrontal lobe time sequence characteristic window, which comprises a signal acquisition module, a dynamic window control module, a phase prediction module, an intervention execution module and an event driving controller. The dynamic window control module intelligently adjusts the duration of a signal analysis window through real-time calculation of the energy ratio of a # imgabs 0 # frequency band to a # imgabs 1 # frequency band, and accurately locks a transient characteristic window in a pain outbreak period; the phase prediction module realizes millisecond-level synchronization of stimulation pulse and a rising edge of action potential of a neural cluster based on a two-factor mapping mechanism of an individualized conduction velocity level and an oscillation frequency; the event-driven controller reduces the power consumption to a wearable device level while maintaining 10 ms-level real-time response through a dynamic resource allocation strategy.

The invention discloses a transcranial electrical stimulation control method and system for regulating mental fatigue, and belongs to the technical field of mental state regulation. The system comprises an EEG collecting and processing module and an electrical stimulation module which are in wireless communication to form a closed loop. The EEG collecting and processing module collects a forehead lobe three-lead EEG signal, after preprocessing (baseline drift and high-frequency interference are removed through FIR filtering, and eye movement artifacts are removed through combination of wavelet transform and Kalman filtering), the mental fatigue state is classified through an onboard lightweight convolutional neural network (CNN) algorithm, and a control command is generated; the electrical stimulation module receives the command, generates bipolar pulse width modulation transcranial direct current stimulation (tPWMDCs) current, and stimulates the prefrontal lobe. Local low-power-consumption reasoning of EEG signals is achieved, tPWMDCs avoid skin injury, a closed-loop system dynamically adapts to individual differences, portability, safety and accuracy are achieved, and the system is suitable for driving, office and other scenes.

The invention discloses an English classroom attention regulation and control method combined with brain wave detection, and relates to the technical field of English classroom attention regulation and control, and the method comprises the steps: obtaining a multi-channel brain wave signal of a student in English classroom learning through a brain wave collection device, collecting visual gazing track data through an eye tracker, and obtaining a brain wave signal; capturing a face image sequence through a camera; performing frequency band separation on the multi-channel electroencephalogram signal, extracting energy of a theta wave and a beta wave of a prefrontal lobe, and calculating a theta / beta power ratio; performing de-noising and clustering processing on the visual gaze track data, identifying an effective gaze interval, calculating a reciprocal of a gaze point distribution variance in unit time, generating a visual focusing stability parameter, performing action unit identification on a facial image sequence, extracting smile frequency, eyebrow intensity and blink period characteristics, and outputting a cognitive emotion state score; and constructing a multi-modal attention fusion evaluation model based on the theta / beta power ratio, the visual focusing stability parameter and the cognitive emotion state score.

The invention discloses a robot motion track planning and control method and device based on multi-channel fusion, a storage medium and a program product, and the method comprises the steps that emotion information is determined according to the current joint angle and the current joint angular speed of a mechanical arm of a robot, the current position and the current speed of an obstacle and collision information, the emotion information represents the degree of pressure or fear emotion caused by the obstacle approaching and colliding with the robot; based on the emotion information, a fast path or a slow path is selected to obtain action information of the robot, the fast path simulates a striatum function to control the robot to achieve fast emergency obstacle avoidance, and the slow path simulates a forehead lobe function to achieve motion track planning of the robot to a target position; controlling a mechanical arm of the robot to move for a first preset duration according to the action information; determining whether a mechanical arm of the robot reaches a target position; and returning to the step of determining the emotion information in response to the fact that the mechanical arm of the robot does not reach the target position.

The invention discloses a three-electrode brain nursing instrument and a control method thereof.The three-electrode brain nursing instrument comprises a body, a plurality of electrodes are arranged on one side of the body, the three-electrode brain nursing instrument is characterized in that the electrodes comprise a first electrode cover, a second electrode cover and a third electrode cover, a circuit board is arranged in the body, a control module is arranged on the circuit board, and the control module is connected with the first electrode cover. The control module is electrically connected with the electrodes, and the control module achieves the functions of direct current forward stimulation, direct current reverse stimulation and alternating current stimulation by arranging cathodes or anodes in the first electrode cover, the second electrode cover and the third electrode cover. The control module can control the current magnitude, the stimulation time and the stimulation frequency of the electrodes according to different stimulation modes, and the functions of direct-current forward stimulation, direct-current reverse stimulation and alternating-current stimulation can be achieved through polarity conversion of the second electrode cover, the first electrode cover and the third electrode cover. Excitation or inhibition stimulation can be performed on the left dorsal outer side forehead lobe, the right dorsal outer side forehead lobe and the forehead center of the brain.

The invention belongs to the technical field of auxiliary diagnosis of depressive disorder, and particularly relates to a functional near infrared spectrum and electroencephalogram combined photoelectric bimodal depressive population judgment system. The system comprises a functional near infrared spectrum and electroencephalogram synchronous acquisition module, a signal preprocessing module, a feature extraction and fusion module and an audio experiment paradigm stimulation module. The functional near infrared spectrum and electroencephalogram synchronous acquisition module is used for acquiring a prefrontal lobe region functional near infrared spectrum signal and a prefrontal lobe region electroencephalogram signal of the crowd under the audio stimulation played by the audio experiment paradigm stimulation module; the signal preprocessing module is used for removing interference in the near infrared spectrum signal and the electroencephalogram signal; the feature extraction and fusion module is used for performing feature extraction and fusion on the near infrared spectrum and electroencephalogram data subjected to artifact noise removal, and performing depression population judgment on fusion features by adopting a machine learning classifier; and the audio experiment paradigm stimulation module is used for playing audio clips containing different stimulation attributes.

The invention provides a transcranial light adjusting method and system based on a laser light source and transmitting energy through an optical fiber bundle, relates to the technical field of physiotherapy, and mainly comprises a power supply, a controller, a laser, an optical fiber coupler, the optical fiber bundle, a lens group and the like. The optical fiber coupler couples laser output by the laser into the incident end of the optical fiber bundle; the emergent end of the optical fiber bundle is set as one or more sub optical fiber bundles, and the tail end of each sub optical fiber bundle is selectively butted with one lens group; when the emergent end of the optical fiber bundle is provided with a plurality of sub optical fiber bundles, the plurality of sub optical fiber bundles are respectively and correspondingly arranged at the back of the prefrontal lobe, the belly of the prefrontal lobe, the temporal lobe, the apical lobe and other physiotherapy positions which are effective for light; and when the tail ends of the plurality of sub-optical fiber bundles are not butted with the lens group, the emergent end of each sub-optical fiber bundle is set as an optical fiber array, and the optical fiber array is embedded into the light supply effective head position and is in contact with the scalp, so that the absorption rate and the reflectivity of the hair to near-infrared light are reduced.

The invention discloses an optimization method for senile cognitive impairment intervention. The optimization method comprises the following steps: S1, a multi-modal evaluation stage: acquiring neurophysiology, behavior and language data of a patient through a multi-source sensing module; s2, personalized prescription generation: calculating a comprehensive cognitive impairment index (CCI), and generating a personalized cognitive training scheme; s3, the intervention module executes the steps that a virtual training scene is generated in the AR environment; and S4, effect verification and iteration: generating a neuroplasticity evaluation report after training, and quantifying the parahippocampal gyrus-prefrontal cortex function connection enhancement degree. Neurophysiology, behaviors and language data of a patient are synchronously collected by adopting a multi-source sensing module, and a personalized cognitive training scheme is generated based on a comprehensive cognitive disorder index. Meanwhile, a virtual scene is constructed to execute an intervention module, and the parahippocampal gyrus-prefrontal lobe function connection enhancement degree is quantitatively analyzed after intervention, so that an evaluation-decision-intervention-verification closed-loop system is constructed, and the intervention effect of the senile cognitive impairment is remarkably improved.

The invention relates to a behavior decision-making method for simulating a hippocampus-prefrontal lobe memory planning playback mechanism in the technical field of artificial intelligence, and the method comprises the steps: constructing the closed-loop interaction of a hippocampus planning playback network and a prefrontal lobe strategy evaluation network, and fusing the meta-reinforcement learning and a model-based strategy optimization technology; the bionic neural mechanism realizes the unification of dynamic planning and strategy optimization, and realizes the dynamic coupling of experience playback and prospective simulation in the brain-like decision process, so that the robot can learn in planning. The method comprises the following steps: acquiring environment basic data by using a model element-free reinforcement learning method, planning a certain number of steps by using a model by using a strategy optimization method based on model element reinforcement learning and taking the current basic data as a planning starting point, and training the model by using a planning result. The problems that an existing robot algorithm is limited by the complex degree of an actual environment, sample collection is difficult and the like are solved, the learning ability is effectively improved, and therefore the application requirements of the mobile robot in diversified complex scenes are met.

The invention relates to the technical field of intelligent medical adjuvant therapy, in particular to a depression targeted nerve regulation method and system based on fNI RS dynamic brain entropy, and aims to overcome the defects that an existing nerve regulation method is fixed in stimulation parameter, lacks real-time feedback, depends on subjective evaluation and the like. According to the method, a multichannel near infrared spectrum optical brain imaging device is adopted to collect a time sequence signal of prefrontal lobe resting hemoglobin; calculating the time sequence signal of the prefrontal lobe static hemoglobin to obtain a static entropy feature and a dynamic entropy feature; calculating a brain entropy ratio according to the static entropy feature and the dynamic entropy feature; and matching the brain entropy ratio with a preset brain entropy threshold value, and adjusting nerve regulation and control stimulation parameters according to the corresponding brain entropy threshold value. Through the brain entropy real-time feedback closed-loop TMS regulation and control technology, the quantitative relation between the brain entropy and the stimulation parameters is established, and a stimulation parameter dynamic adjustment method is provided for the nerve regulation and control technology.

The invention provides a method and system for evaluating the instant curative effect of acupuncture head acupoints on cerebral stroke cognitive impairment based on electroencephalogram signals, and the method comprises the steps: collecting the electroencephalogram signals before, during and after acupuncture in stages, and synchronously combining a MoCA / MMSE scale and a cognitive task test to obtain subjective curative effect data; an insulating acupuncture needle is adopted to reduce interference on electroencephalogram signals, and 4-8-conduction portable dry electrode electroencephalogram equipment is matched to collect signals in an area avoiding acupuncture points of the head, so that acupuncture operation and electroencephalogram collection are ensured not to influence each other. A condition vector is generated by using acupuncture point parameters and acupuncture manipulation, and a space-time Transform-GNN model is driven to dynamically focus alpha / theta / beta frequency band time sequence characteristics and a brain network connection mode of cognitive related brain regions such as a prefrontal lobe, a parietal lobe and a temporal lobe. According to the method, a multi-modal feature vector containing electroencephalogram frequency domain features, time domain components and clinical scale scores is constructed through an electroencephalogram-acupoint-curative effect triple data training model of at least 100 patients, and an obvious / effective / invalid level is output.

The invention relates to a depression individualized TMS positioning method and system for a targeted dorsal-lateral prefrontal lobe maximum deviation brain region, and the method comprises the steps: calculating a brain region function connection strength value of a healthy individual based on the MRI data of the healthy individual; constructing a healthy individual brain region function connection strength value standard model based on the healthy individual brain region function connection strength value; on the basis of MRI data of the depression subject, calculating a brain region function connection strength value of the depression subject; based on the brain region function connection strength value of the depression subject, measuring a deviation value of the brain region function connection strength value of the depression subject in the healthy individual brain region function connection strength value standard model; and based on the obtained deviation value of the brain region function connection strength value of the depression subject, finding out the brain region with the maximum deviation in the left dorsal and outer prefrontal lobes, and taking the brain region as an individualized TMS target. The healthy individual standard model is used for measuring the deviation of the brain region function connection strength of the depression subject, and the targeted most abnormal brain region individualized TMS positioning method is achieved.

The invention belongs to the technical field of educational nerves, and particularly relates to a student brain activity monitoring and learning progress adjusting system based on fNIRS, comprising: an fNIRS signal acquisition module configured to acquire blood oxygen signals of a prefrontal cortex and a parietal brain region of a student in real time through a multi-channel near-infrared light source and a detector; the data processing and analyzing module is used for carrying out preprocessing, feature extraction and cognitive state classification on the blood oxygen signal and outputting a cognitive load level and an attention state; the adaptive learning engine is used for learning content difficulty and rhythm based on a cognitive state dynamic adjustment strategy, and the dynamic adjustment strategy comprises adjusting task difficulty according to a cognitive load level and controlling learning rhythm according to an attention state; according to the method, blood oxygen signals (HbO / HbR) of brain areas such as the prefrontal lobe and the apical lobe are collected in real time through fNIRS, cognitive loads and attention states are quantified in combination with a machine learning algorithm (SVM + LSTM), and hysteresis and subjective deviation of a traditional method (such as test and observation) are avoided.

The present invention relates to a device for preventing, alleviating or treating degenerative brain diseases by inducing brain wave synchronization through acoustic vibration tactile stimulation and bone conduction music, and a method thereof. More specifically, the present invention relates to a wearable device which is worn on the head of a wearer, stimulates the brain by providing Vibroussis composed of 40 Hz vibration and sound to the forehead lobe and the temporal lobe of the wearer, and recovers the gamma oscillation of the brain to normal by increasing the cerebral blood flow. Therefore, the device and the method thereof can prevent, relieve or treat degenerative brain diseases (such as mild cognitive impairment or early Alzheimer's disease (dementia)).

The present invention relates to a method for cognitive ability testing and a testing system using the same. According to the method and testing system of the present invention, a cognitive ability test related to prefrontal lobe activity can be performed very effectively and with high reliability, and the test results may be utilized to provide information for the diagnosis of diseases that cause cognitive ability decline.

The application discloses a transcranial electrical stimulation target parameter optimization method, system, device and computer readable storage medium, and belongs to the technical field of neural regulation and medical auxiliary equipment. The optimization method comprises the following steps: S1, acquiring a head medical image, and constructing a head finite element simulation model based on the medical image; S2, in the head finite element simulation model, defining a target stimulation brain area as a ventromedial prefrontal cortex area; S3, simulating transcranial electrical stimulation in the finite element simulation model, and through simulation calculation, taking the concentration of current density in the ventromedial prefrontal cortex area as an optimization target, and determining at least one transcranial electrical stimulation parameter; wherein the transcranial electrical stimulation parameter comprises an electrode position and / or an electrical stimulation waveform parameter. The optimization method changes the parameter selection from experience dependence to scientific calculation based on a biophysical model, improves the accuracy of the stimulation parameter, and thus realizes precise brain area targeted stimulation.

The invention relates to the technical field of medical instruments, in particular to a head-mounted phototherapy system for a forehead lobe brain area, which comprises an information acquisition unit, a signal processing unit, an information output unit and a head fixing device, the information acquisition unit is used for acquiring physiological signals, physical signals and optical signals in the treatment process; the signal processing unit is used for receiving and processing the signal sent by the information acquisition unit, generating an optical parameter regulation and control instruction and a fixing device regulation instruction according to a processing result, and sending the instructions to the information output unit; the information output unit converts the instruction into a corresponding treatment scheme to implement treatment and sends an adjusting instruction to the head fixing device; the head fixing device is fixed at the forehead part of the user, and adjusts the own state according to the adjusting instruction of the information output unit to adapt to the user; the invention aims to realize the goal of long-term home treatment of the central nervous system disease patient, so that the treatment cost is reduced, the treatment efficiency is improved, and the application prospect is good.

The invention relates to the technical field of biology, in particular to a microwave exposure method for reducing excitability of glutamate neurons. The special microwave exposure condition provided by the invention can significantly reduce the co-expression proportion of medial prefrontal lobe glutamate neurons and c-Fos, the calcium activity of glutamate neurons and the activity of glutamate neural circuits, so that the expression and calcium signal level of immediate early protein c-Fos of glutamate neurons are significantly reduced.

The invention provides a head brain wave feature recognition method and a computer program product, relates to the field of biological feature recognition processing, and solves the problem that brain wave feature extraction and classification accuracy is affected due to the fact that the quality of brain wave signals is poor. Obtaining a processed signal to be processed; performing feature extraction to obtain feature information; based on a deep learning classifier, performing automatic classification according to the feature information, and identifying the brain waves to obtain a brain wave classification result; the step of obtaining the brain wave signals of the head comprises the substeps that at least two electrodes are used for collecting the brain wave signals in the head area, and the electrodes are distributed in the head area in the mode of combining partial symmetry and partial asymmetry. According to the scheme, the electrodes are designed in a symmetrical and asymmetrical combined mode, tiny differences of head areas of left and right brain hemispheres can be captured, functional activities of left and right frontal lobes are effectively distinguished, and the accuracy of brain wave feature extraction and classification is improved.

The invention provides an EEG (electroencephalogram)-based cerebral arterial thrombosis detection and evaluation system, which is used for solving the problems of low resolution, poor portability, injury and the like of existing equipment. The main part of the system comprises an STM32 minimum system board, a Bluetooth module and a TGAM electroencephalogram module. The two TGAM modules are connected in parallel, and signals of the left and right forehead lobes and the left and right earlobes of the brain are collected. According to the method, the collected signals are preprocessed by adopting a cubic spline interpolation method under artificial bee colony optimization and a kurtosis-based wavelet transform algorithm, so that the signal quality is further improved, and subsequent feature extraction is more convenient. Meanwhile, a composite model of a time convolutional network and a bidirectional long and short memory network, a composite model of a convolutional neural network and a least square support vector machine and a time-frequency-space three-dimensional modeling are constructed to guide an EEG individualized detection and evaluation scheme, and a detection and evaluation system for cerebral arterial thrombosis is proposed and developed.

This invention belongs to the field of intelligent transportation and autonomous driving technology, and discloses a dynamic decision-making method and system for unprotected left turns of unmanned vehicles based on neuromorphic neural networks. The method constructs a traffic scene graph and introduces a neuromorphic neural network for prefrontal cortex-like collaborative spatial reasoning to generate scene-level semantic representations. Based on this, a structured left-turn action graph is constructed, and feasibility and risk assessments are performed on candidate driving behaviors. An action mask is generated to dynamically compress the decision space. Within the framework of a proximal policy optimization algorithm, a risk budget mechanism is combined to achieve an adaptive balance between safety and efficiency. Simultaneously, a scene graph memory is constructed, and historically successful strategies are reused through similarity retrieval to improve cross-scene generalization ability. This invention effectively solves the problems of excessively large decision space, unstable behavior, difficulty in balancing safety and efficiency, and insufficient generalization ability of existing unmanned vehicles in complex intersection scenarios, significantly improving the stability, safety, and adaptability of decision-making.

The invention relates to the technical field of aviation safety monitoring, provides a flight personnel operation state identification method and system based on multi-modal data fusion, and solves the problems of high misjudgment rate and poor environmental adaptability in the prior art. The method comprises the following steps: synchronously acquiring electroencephalogram signals of a flight personnel, angular velocity of a head inertial measurement unit, an eye movement track and pitch angle and roll angle parameters of an aircraft; dynamically segmenting the electroencephalogram signal to construct an electroencephalogram dynamic topological graph, and generating a head stability index; extracting wave persistence features of the forehead lobe brain region, and performing weighted fusion on the wave persistence features and the head stability index to generate a cognitive conflict feature vector; the eye movement track coordinates are converted into a cabin three-dimensional coordinate system to generate space codes, and space-time feature bodies are constructed through space-time convolution kernel fusion space distribution and track density gradients; and identifying the operation state to be normal, early-warning or out-of-control three levels. According to the invention, accurate grading early warning of the operation state of the flight personnel is realized, and the real-time performance and the accuracy of flight safety monitoring are remarkably improved.

The present invention discloses a method for classifying depressive electroencephalogram based on a dual-branch fusion model in deep learning, comprising the following steps: (1) obtaining electroencephalogram signals of the Fp1, Fpz, and Fp2 electrodes in the prefrontal lobe of the brains of a number of healthy individuals, (2) obtaining electroencephalogram signals of the Fp1, Fpz, and Fp2 electrodes in the prefrontal lobe of the brains of a number of patients with mild depression, (3) obtaining electroencephalogram signals of the Fp1, Fpz, and Fp2 electrodes in the prefrontal lobe of the brains of a number of patients with moderate depression, (4) training and learning the dual-branch fusion model with the input forms of healthy controls, patients with mild depression, and patients with moderate depression in steps (1), (2), and (3), and (5) converting the electroencephalogram signals of the window to be analyzed into corresponding wavelet time-frequency diagrams and inputting them into the dual-branch fusion model trained in step (4) to complete the analysis of the electroencephalogram signals. This method has good effects and can distinguish between depression and health as well as the degree of depression.

Disclosed are a fragrance recommendation device and system. An aspect of the present embodiment provides a fragrance recommendation system including: a test device attached or mounted to a preset part of a test subject to examine the functional activity of the left and right prefrontal lobes; a fragrance recommendation device that analyzes the results obtained by the test device, and thereby analyzes the subject's preference for or aversion to each of tested fragrances; and a test container that stores each of fragrances that are provided to the test subject in order to be tested, and provides the fragrances to the test subject.