61 results about "Prefrontal lobe" patented technology

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 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 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 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 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.

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.

The invention discloses a child twitch disorder detection and evaluation system based on a task normal form, and belongs to the technical field of medical detection. The system comprises a task stimulation presentation and evaluation device which is used for executing a standardized Go / No-Go cognitive task and synchronously collecting behavior indexes of a subject; the near-infrared brain function imaging device is used for synchronously monitoring the blood oxygen concentration change of the key brain area of the prefrontal lobe of the tested brain during the execution period of the task; the data processing module is used for preprocessing the brain function signals and extracting physiological indexes from the brain function signals; and the result interpretation module is used for performing fusion analysis on the behavior indexes and the physiological indexes based on a quantitative interpretation model pre-established through clustering analysis, and automatically outputting a specific cognitive function subtype interpretation result. According to the method, objective and quantitative evaluation and typing of the core cognitive function defect of the twitch disorder are realized through multi-dimensional information fusion and standardized analysis of behavioral performance and cranial nerve activity.

The application discloses a closed-loop system for attention regulation based on electroencephalogram signals, which is composed of a hardware part and a software part. The hardware part includes a head-mounted device equipped with high-sensitivity EEG electrodes for collecting electroencephalogram signals of the frontal lobe, and the collected signals are transmitted in real time to a computer or a mobile device through wireless communication technology for further processing and analysis. A multi-lead programmable electrical stimulation device dynamically adjusts the parameters of electrical stimulation according to algorithm feedback and provides a personalized stimulation plan. The personalized attention improvement plan: the application can accurately capture the attention changes of individuals in different states by analyzing the electroencephalogram (EEG) signals of individuals in real time, especially the ratio of theta wave to beta wave (TBR). Using these data, the system can customize a personalized stimulation plan to adapt to the current needs of individuals, rather than using a universal method suitable for all people.

The invention provides a cognitive disorder rehabilitation evaluation training method and device and terminal equipment, the terminal equipment comprises electroencephalogram acquisition equipment and a training terminal, and the cognitive disorder rehabilitation evaluation training method comprises the following steps: S1, acquiring prefrontal lobe electroencephalogram signal data of a patient; s2, the patient carries out training through a training terminal, and the training terminal automatically collects behavior data in the training process; s3, in the training process, the concentration index of the patient in the training process is calculated in real time based on the collected electroencephalogram signal data; s4, performing fusion analysis on the collected behavior data and the calculated concentration index; s6, after training is completed, calculating an average concentration degree value and judging whether the average concentration degree value meets requirements or not, if the average concentration degree value meets the requirements, entering a step S7, and if the average concentration degree value does not meet the requirements, entering a step S8 and a step S9; s7, directly generating an evaluation result according to the collected behavior data; s8, starting a compensation algorithm for correction; and S9, outputting a corrected evaluation result.

The invention discloses a helmet type multi-target transcranial magnetic stimulation system based on a time interference theory. The helmet type multi-target transcranial magnetic stimulation system comprises a power supply module, an interaction module, a control circuit module, a stimulation main circuit module and a magnetic stimulation coil module, the interaction module is connected with the control circuit module, the control circuit module is connected with the stimulation main circuit module, the stimulation main circuit module is connected with the magnetic stimulation coil module, and the power supply module is connected with the interaction module, the control circuit module, the stimulation main circuit module and the magnetic stimulation coil module. The magnetic stimulation coil module is a coil array composed of 15 magnetic stimulation coils, the coil array is arranged in a partitioned mode according to the frontal-temporal-apical brain area, four coils are densely arranged in the outer side area of the back of the prefrontal lobe, three coils are distributed on the upper portion of the temporal lobe, and for an auditory sense and memory integration function area, eight coils are intensively arranged on the rear portion of the central groove of the apical lobe. And preset focusing and induced electric field intensity are realized in a target region, and accurate transcranial magnetic stimulation of an individual is finally realized.

The present invention relates to an individualized TMS localization method and system for depression targeting the brain region with the largest deviation in the dorsolateral prefrontal lobe. Based on the MRI data of healthy individuals, the functional connectivity strength values of the brain regions of healthy individuals are calculated; based on the functional connectivity strength values of the brain regions of healthy individuals, a normative model of the functional connectivity strength values of the brain regions of healthy individuals is constructed; based on the MRI data of depression subjects, the functional connectivity strength values of the brain regions of depression subjects are calculated; based on the functional connectivity strength values of the brain regions of depression subjects, the deviation values of the functional connectivity strength values of the brain regions of depression subjects in the normative model of the functional connectivity strength values of the brain regions of healthy individuals are measured; based on the obtained deviation values of the functional connectivity strength values of the brain regions of depression subjects, the brain region with the largest deviation in the left dorsolateral prefrontal lobe is found, and this brain region is used as the individualized TMS target. The present invention realizes an individualized TMS localization method targeting the most abnormal brain region by measuring the deviation of the functional connectivity strength of the brain regions of depression subjects using the normative model of healthy individuals.

The invention relates to the technical field of transcranial alternating current stimulation for brain synchronization of two persons, and particularly discloses a transcranial alternating current stimulation device for enhancing brain synchronization of two persons and an application method of the transcranial alternating current stimulation device. The present invention relates to an emotional training system (100) suitable for situations such as co-emotional regulation, cooperative training, relation modeling, and the like, comprising: a first positive channel (110A) and a second positive channel (110B), which are respectively connected to forehead lobe region electrodes of a first individual and a second individual, for outputting an alternating current at a set frequency, the frequency being set as an intermediate frequency of an emotional-related alpha wave frequency band, i.e., a 10Hz alpha wave frequency band, and the frequency being set as an intermediate frequency of the emotional-related alpha wave frequency band; and the induction synchronization capability is relatively high. According to the transcranial alternating current stimulation device for enhancing brain synchronization of two persons and the application method thereof, the phase synchronization of alpha waves of the two prefrontal lobes can be remarkably enhanced in practical application, and the cross-brain emotion cooperation capability is improved.

The application discloses the use of a reagent for detecting ANT4 or SLC25A31 in the preparation of a kit for screening depression, and belongs to the technical field of disease screening. The application finds that the expression level of Slc25a31 in the prefrontal lobe of an adolescent depression mouse is significantly up-regulated compared with a control group, indicating that the expression level of Slc25a31 is significantly related to the risk of adolescent depression. By detecting the expression level of ANT4 or SLC25A31 in the human body, the application can be used for screening of clinical adolescent depression, early warning and risk diversion.

The invention discloses a relapse risk monitoring and early warning method based on heart and brain behavior intelligent fusion, and belongs to the technical field of drug banning and health monitoring. Three-mode data of HRV sample entropy (electrocardio), prefrontal lobe theta wave coherence (electroencephalogram) and motion fractal dimension (behavior) of a patient abstained from drugs are collected in a non-stimulation mode through a wearable device; motion interference is suppressed by adopting a motion-driven cascade filtering technology (kurtosis detection and self-adaptive band elimination filtering); inputting the data into a space-time diagram convolution-federated learning hybrid model (ST-FedGCN), and outputting a relapse risk level (1-5 levels) and expected relapse time; when the risk probability value is Rgt; and when the risk level is 0.82 or the risk level is greater than or equal to 4, the portable tDCS equipment is automatically triggered, and 1.5 mA regulation and control current is applied to the front frontal lobe on the back and outer side of the right side. Laboratory dependence is broken through, community dynamic monitoring is achieved, the prediction accuracy is larger than 90%, the relapse rate is reduced, and powerful support is provided for drug banning work.

The application relates to the technical field of brain-computer interfaces, and discloses a brain-computer interface terminal device. The brain-computer interface terminal device comprises a flexible main body frame, a frontal lobe collection array, a dual-function ear clip module, a magnetic suction modular electrode and a master control integrated module; the frontal lobe collection array is arranged on the flexible main body frame and adopts a multi-point potential distribution design; the dual-function ear clip module integrates a reference electrode and an environmental noise sensor and is used for synchronously collecting brain electrical reference potential and environmental electromagnetic interference data; the magnetic suction modular electrode adopts a magnetic suction female buckle interface design to be compatible with dry electrodes and flexible patch electrodes and to realize plug and play; the master control integrated module is electrically connected with the frontal lobe collection array, the dual-function ear clip module and the magnetic suction modular electrode respectively, so as to realize brain electrical signal collection and interference elimination, adaptive adjustment of electrode collection parameters, dynamic switching of a collection channel and a sampling rate, linkage configuration of an electrode working mode and a collection point.

The invention discloses a transcranial electrical stimulation method and device, and the method comprises the following steps: S1, collecting functional magnetic resonance imaging, carrying out the preprocessing, selecting a region of interest, extracting an average value of all voxel time sequences in the region of interest, obtaining a seed time sequence of the region of interest, and carrying out the calculation of the average value; calculating a Pearson correlation coefficient between the seed time sequence and other voxel time sequences of the whole brain so as to obtain a whole brain function connection diagram; s2, collecting an electroencephalogram signal, and extracting the relative power of a prefrontal lobe delta wave after preprocessing; the functional magnetic resonance imaging provides information with high spatial resolution of cerebral blood flow and metabolism, and the electroencephalogram reflects time sequence characteristics of cerebral electrical activity. The two kinds of data are combined, a target brain area is recognized by analyzing the mode and characteristics of brain activities in the sleep process, and then different brain state changes are triggered according to the transcranial electrical stimulation field intensity, so that the optimal stimulation method is obtained.

The present invention addresses the problem of providing a therapeutic agent for frontotemporal lobe degenerative disease (FTLD), a composition for treating FTLD, and a method for treating FTLD, which have not been developed so far. The inventor of the present invention finds that: a prefrontal lobe cerebral cortex neuron is differentiated and induced by an iPS cell derived from an FTLD patient; a therapeutic agent for frontotemporal lobe degenerative disease (FTLD), comprising a compound represented by formula (1-1), a compound represented by formula (2-1), or a compound represented by formula (3-1), a pharmaceutically acceptable salt thereof, or a solvate thereof, is administered to a prefrontal cerebral cortex neuron exhibiting a pathological state of FTLD. Therefore, the pathological state of FTLD of the forehead lobe type cerebral cortical neurons is improved.

The invention provides a training method, system and device for improving the execution ability. The method comprises the steps that forehead lobe electroencephalogram data, task execution response data and eye movement data are collected; according to the prefrontal lobe electroencephalogram data, the task execution response data and the eye movement data, a power feature map, a response feature map and a fixation point thermodynamic map are determined respectively; processing the power feature map, the response feature map and the fixation point thermodynamic map to obtain a corresponding same-time map segment sequence, further processing the same-time map segment sequence to obtain a plurality of single-mode images, and fusing the single-mode images to obtain a multi-mode image; inputting the multi-modal image into an execution capability evaluation model to obtain an attention index, an execution response index and an eye movement fixation index; and according to the attention index, the eye movement fixation index and a preset rule, adjusting a visual stimulation parameter and / or an auditory stimulation parameter of the current task, judging an execution response index, and adjusting a task type and / or a difficulty level of the next task so as to realize real-time double feedback in the tasks and between the tasks.