126 results about "Brain functioning" patented technology

The present disclosure relates to a multi-modal brain network computation method associated with structural function, apparatus, device, and medium. The method is applied to train a brain disease prediction model, and the brain disease prediction model includes an association perception dual-channel generation module, a disease feature regression module, a topological structure discriminator, and a time-space joint discriminator. In a model training process, by performing a multi-level interactive fusion learning on a high-order topological feature of brain functional magnetic resonance data and magnetic resonance diffusion tensor imaging data, a multi-modal time series activity signal of each brain region is obtained.

The invention provides a brain age estimation method based on a dynamic fuzzy learnable brain network, and belongs to the technical field of medical image processing and artificial intelligence. According to the technical scheme, the method comprises the following steps that S1, brain nuclear magnetic resonance imaging of a subject is collected, and preprocessing and data division are carried out; s2, constructing graph structure data, and performing feature extraction and position information embedding on the data; s3, constructing a dynamic fuzzy learnable brain network model comprising a main branch and a local branch, and respectively extracting global and local connection features; s4, introducing a dynamic fuzzy multi-head self-attention module into the main branch to realize effective modeling of global features; s5, a local branch dynamically models a dependency relationship between channels through a convolution filter and a learnable graph attention module; s6, after the features of the main branches and the local branches are fused, brain age prediction is carried out through a multi-layer perceptron. According to the method, the modeling capability of the brain function connection mode is improved, and the brain age prediction task can be more effectively completed.

The invention provides a voice recognition method and device based on a brain-like model, electronic equipment and a storage medium, and the method comprises the steps: obtaining a whole-brain network topological structure according to a brain function network generated by human brain image data, and carrying out the recognition of a whole-brain network through employing a multi-class neuron model as a node and a synaptic plasticity model as an edge, constructing a multi-brain-region pulse neural network as a brain-like model; constructing a speech recognition framework of the brain-like model; electromagnetic intervention is applied to different brain areas of the brain-like model, optimal electromagnetic intervention parameters are determined by analyzing the voice recognition accuracy of the brain-like model before and after electromagnetic intervention, and brain-like model voice recognition is carried out according to the optimal electromagnetic intervention parameters. According to the invention, the speech recognition performance of the brain-like model can be effectively improved, the biological interpretability and neural information processing capability of the brain-like model are further improved, and the development of brain-like intelligence in the application of a mode recognition task is promoted.

The invention discloses a brain function magnetic resonance imaging data analysis method based on a contrast graph neural network, and the method comprises the steps: firstly carrying out the data enhancement of a brain function connection graph, and simulating the heterogeneity of brain function magnetic resonance data, so as to improve the diversity of a data set; secondly, the hidden space embedding features of the brain function connection diagram are efficiently learned by fusing a double-Hough-Laplacian diagram convolutional network and a contraction incentive mechanism; the embedded features are mapped to a group of prototype vectors, and prototype allocation codes corresponding to the embedded features are calculated by adopting a Sinkhorn-Knopp algorithm; performing exchange optimization on prototype codes between different enhanced brain connection diagrams of the same subject through a contrast learning strategy, and compelling codes of homologous subjects to be aligned at the minimum cost in combination with a cross entropy loss function; and finally, applying the pre-training model to a functional magnetic resonance imaging data set of the Alzheimer's disease, and carrying out interpretability analysis on learning features to improve the classification efficiency and pathological analysis of the Alzheimer's disease under a limited tag condition.

The invention discloses an electroencephalogram multi-index network standardized modeling method which comprises the following steps: S1, adopting a multi-country electroencephalogram cross-spectrum data set based on a large-scale full life cycle as original electroencephalogram data, calculating a cross-spectrum matrix of the original electroencephalogram data, and preprocessing the original electroencephalogram cross-spectrum matrix; s2, constructing a weighted undirected network by using the preprocessed original electroencephalogram cross spectrum matrix to serve as a brain function network; s3, calculating network features of three dimensions of brain network function integration, function separation and centrality of the brain function network, and establishing an evolution trajectory of the network features; and S4, designing an interpretable lightweight neural network regression model, and reconstructing upper triangular elements of the brain function network to obtain a complete brain function network, namely a normalized brain function network corresponding to the age. The invention further discloses an electroencephalogram multi-index network standardized modeling system. The principle of the brain function network can be reflected more accurately.

The invention relates to the technical field of brain network construction, in particular to a group prior embedded double-flow space-time brain network analysis method, which comprises the following steps of: S1, preprocessing a brain function image; s2, dividing the brain into a plurality of brain regions, and extracting an average time sequence; s3, calculating edges of a connection weight construction brain map, and outputting a symmetric correlation matrix # imgabs0 #; s4, defining a graph isomorphic network under spatial features, taking the correlation matrix R as the input of the graph isomorphic network, and outputting to obtain a tag Z1 related to the spatial features; S5, collecting BOLD signals of brain function images, and inputting the BOLD signals into a # imgabs1 # model to a # imgabs2 # model to obtain a tag Z2 related to the time features; s6, performing dimensionality reduction and aggregation on the spatial feature tag Z1 and the time feature tag Z2 to obtain a same-dimensional tag Z; and S7, establishing a group-based attraction graph # imgabs3 # by using the same-dimensional label Z to realize classification and identification. According to the method, the spatial-temporal feature tags are utilized to construct the group graph Gp, and node feature updating is matched to obtain new tags embedded into group priori, so that the classification and recognition accuracy of the brain function image can be effectively improved.

The invention discloses an infantile autism diagnosis method based on brain function correlation structure modeling and a default mode network, which comprises the following steps of: firstly, preprocessing acquired infantile autism data, and extracting average time sequence data from the acquired infantile autism data to construct a whole brain function connection matrix and a DMN (default mode network) function connection matrix; a fusion weight parameter is initialized and is used for fusing the two constructed matrixes; constructing a cross-dimension dual adaptive attention module to extract features from a time dimension and a space dimension; a product-based embedded graph generation module is defined to generate a graph structure, the nodes correspond to brain regions, and the weights of the edges correspond to the similarity between the nodes; and high-order features are extracted from the generated graph structure by defining a graph convolutional network predictor, and node features are mapped to classification tags for subsequent diagnosis of autism. The method disclosed by the invention has the beneficial effects that the brain function association relationship of the autism patient is disclosed, the interpretability of clinical application is enhanced, and the autism diagnosis accuracy is improved.

The invention discloses a brain network quantitative analysis method based on near-infrared signal time sequence dynamic graph Fourier transform, which comprises the following steps: constructing a brain network based on fNIRS signals, optimizing time sequence alignment by using a dynamic time warping algorithm, and dividing dynamic network time periods by combining adaptive K-Means clustering with an elbow rule; a neighbor topology overlapping coefficient and feature vector centrality analysis are introduced, and a time sequence dynamic graph is constructed to quantify the connection stability between node layers; a time sequence dynamic graph Fourier transform method is provided, brain function signals are mapped to a time-space-frequency three-dimensional joint domain through spectral decomposition of a time-varying graph Laplacian matrix, and a dynamic spectrogram is generated to analyze a multi-scale spatio-temporal evolution mode; a filter is designed to separate low-frequency (global coordination) and high-frequency (local mutation) components, and a function-structure dynamic constraint model is established. According to the method, an efficient and accurate quantification tool is provided for revealing a brain network dynamic recombination mechanism and neural adaptability changes.

The invention discloses a federated learning-based functional magnetic resonance imaging data classification method. The method comprises the following steps of obtaining multi-site resting state functional magnetic resonance imaging data and performing preprocessing; dividing a brain region and extracting a time sequence; constructing a brain function connection network by adopting a Pearson correlation coefficient; a graph sampling aggregation neural network fusing a residual connection structure and a multi-head attention mechanism is trained at each site, and shallow brain region features are reserved; adopting a linear kernel maximum mean value difference loss function to align the brain region node feature distribution of each site, and minimizing the data distribution difference between the sites; and carrying out classification training by adopting cross validation, aggregating parameters of each station through federal weighting, and evaluating classification performance indexes to obtain a final classification prediction result. According to the method, the graph sampling aggregation neural network and the cross-network layer feature alignment method are combined, the heterogeneity problem of multi-site functional magnetic resonance imaging data can be solved, and therefore generalization and classification performance of a global model are improved.

The invention discloses a calculation method for integrating task induction and intrinsic spontaneous brain function activity. The calculation method comprises the following steps: calculating a brain activation mode when an individual executes a corresponding cognitive task based on task state functional magnetic resonance imaging data and a general linear model; identifying individual large-scale nerve avalanche with spatial continuity based on resting state functional magnetic resonance imaging data; the method comprises the following steps: performing principal component analysis on resting state functional magnetic resonance data of an individual to construct a low-dimensional state space; a task-induced brain activation mode and intrinsic spontaneous nerve avalanche are projected to an individual low-dimensional state space; calculating the Euclidean distance between the task-induced brain activity and the intrinsic spontaneous nerve avalanche in the low-dimensional state space; and detecting the prediction effect of the geometric distance on the performance of the tested task through the regression model. The method is verified on a real data set, and experimental results show that the method not only can integrate two basic brain function activities, but also can significantly predict individual cognitive performance differences.

The invention provides a cognitive competence evaluation method, device and system based on hemodynamics simulation in a low oxygen partial pressure environment. Comprising the following steps: acquiring first brain function connection data, second brain function connection data, first behavioral data, fifth brain function connection data and sixth brain function connection data of a subject under a first oxygen partial pressure condition; acquiring third brain function connection data, fourth brain function connection data, second behavioral data, seventh brain function connection data and eighth brain function connection data of the subject under a second oxygen partial pressure condition, and obtaining a resting state brain function connection evaluation result, a task state brain function connection evaluation result and a behavioral evaluation result; and obtaining a cognitive ability evaluation result based on the resting state brain function connection evaluation result, the task state brain function connection evaluation result and the behavioral evaluation result. Therefore, the adaptive capacity of the subject to the plateau can be accurately evaluated.

The invention provides a pathological target positioning method and system based on time domain interference stimulation. The method comprises the steps that electroencephalogram data, functional image data and structural image data of a patient are acquired; generating a spatial distribution diagram according to the electroencephalogram data and the structure image data; performing registration processing on the spatial distribution diagram and the functional image data to obtain the spatial distribution diagram and the functional image data after registration processing; the spatial distribution map after registration processing is matched with the spatial position of the functional image data; performing data fusion on the spatial distribution map and the functional image data after registration processing to generate a comprehensive brain function map; phase synchronism analysis is carried out on the comprehensive brain function map, the phase synchronism of different brain regions is evaluated, and the brain region with the phase synchronism larger than a preset threshold value and active in the cognitive task is determined as the pathological target region. According to the embodiment of the invention, the method can achieve the precise recognition and positioning of the AD core pathological target region, and facilitates the precise intervention of the AD core pathological target region.

The invention relates to an rs-fMRI classification method based on a residual block and Bayesian variational reasoning. The method comprises the following steps: firstly, acquiring rs-fMRI data and preprocessing the rs-fMRI data to obtain a time signal sequence; then, brain function connection is calculated based on the time signal sequence, and an original brain network is constructed; constructing a line graph based on the original brain network and performing line graph convolution; and finally, based on the line graph after convolution, Bayesian variation posteriori is adopted for classification, and an rs-fMRI classification result is obtained. The brain connection reconstruction model is used as a node, the importance of functional connection is highlighted, and the residual block is combined with Bayesian variational reasoning, so that accurate storage and analysis of brain network connection are facilitated, the problems of original noise and abnormal values are solved, and the accuracy and robustness of brain information extraction by the model for identification and classification are improved.

Systems and methods associated with mind / brain-computer interfaces are disclosed. Certain implementations may include or involve processes of collecting and processing brain activity data, such as those associated with the use of a brain-computer interface that enables, for example, decoding and / or encoding a user's brain functioning, neural activities, and / or activity patterns associated with thoughts, including sensory-based thoughts, determining user attention and / or intentions during interactions within virtual environment and in other applications. Consistent with various aspects of the disclosed technology, systems and methods herein include and / or involve features and functionality enabling hands-free selection of UI elements in virtual environment or on other media.

The invention discloses a neural development disorder co-disease identification system based on a residual image neural network. Belongs to the technical field of neurodevelopment disorder co-disease recognition, and particularly relates to the technical field of co-disease recognition based on a neural network. The system comprises a data acquisition module for acquiring a resting-state fMRI image; the data preprocessing module is used for extracting standardized time sequences of 116 brain regions of the set resting state fMRI image; the multi-band division and feature extraction module is used for performing frequency band filtering on the standardized time sequence of each brain region to obtain a time sequence after each frequency band filtering; in each frequency band, constructing a binary topological matrix based on the PLV matrix and calculating a PLV feature vector based on the PLV matrix; the neurodevelopmental disorder co-disease recognition module is used for analyzing the brain function connection diagram through a residual image neural network model to obtain diagnostic information of neurodevelopmental disorder co-diseases; and the interpretable output module is used for outputting an abnormal brain region by taking the amplitude low-frequency fluctuation as a reference index.

The present invention relates to the field of brain functional data augmentation, and in particular, to a generative artificial intelligence-based brain data augmentation method and device. The method and device comprise: correcting missing channels in low-resolution electroencephalogram data to obtain coarse-grained high-resolution electroencephalogram data; taking the low-resolution electroencephalogram data as input to output corresponding temporal pattern encodings and spatial pattern encodings of the electroencephalogram data; and taking the low-resolution electroencephalogram data and the temporal pattern encodings and the spatial pattern encodings of the electroencephalogram data as input to output fine-grained high-resolution electroencephalogram data, which is then superimposed with the coarse-grained high-resolution electroencephalogram data to obtain reconstructed high-resolution electroencephalogram data. Compared with existing methods, the method of the present invention is capable of preserving and restoring key information and local details in the data, thereby generating more realistic and higher-quality high-spatial-resolution brain data.

The invention provides a brain network construction and analysis method based on multivariable analysis. The method is mainly used for multi-view analysis of a brain structure and a functional network. The technical problem to be solved is that information loss is caused by neglecting necessary multivariate relationships among brain region nodes when univariate analysis is carried out on a brain function network and a brain structure network. According to the method, the canonical correlation analysis method based on data driving is used for constructing the single-mode brain network, the problem that a traditional univariate analysis method neglects necessary multivariate relations is avoided, the complex relation between the brain structure and functions can be reflected more accurately, and a more reliable basis is provided for deep research of a brain working mechanism.

The invention discloses a mild cognitive impairment detection method based on combination of local and global spatial-temporal characteristics. In recent years, detection of mild cognitive impairment by using the brain function connection state has achieved a certain effect, however, an existing detection method is relatively simple in mode of extracting and fusing spatio-temporal characteristics. According to the adaptive brain region signal segmentation method provided by the invention, brain region signal segmentation is regarded as a maximum likelihood estimation problem of Gaussian distribution, different segments are adaptively divided according to different brain connection states, and the problem that accurate segmentation cannot be realized by a sliding window segmentation method is overcome. The invention provides a graph Transform framework fusing local and global spatial-temporal features, and the designed framework can effectively perform modeling on the local and global spatial-temporal features so as to realize accurate mild cognitive impairment detection. Experimental results show that the method can fully utilize local and global spatio-temporal information to accurately detect the mild cognitive impairment.

The invention discloses a system for evaluating the cognitive function of a subject based on fNIRS and an interaction method. The head cap and the near-infrared light emitting / receiving probe in the system enable the imaging range to cover the frontal lobe, the temporal lobe and the occipital lobe; the cognitive assessment normal form interaction device sequentially executes a suppression control assessment normal form, an alert attention assessment normal form, a selection attention assessment normal form, a core attention network assessment normal form, a work memory assessment normal form and a cognitive control assessment normal form in a single block mode according to an execution sequence from simple to difficult; the signal acquisition device cooperatively acquires near-infrared brain function imaging time sequence data in the sequential execution process of each evaluation normal form; the processing device evaluates the cognitive function of the subject on the basis of the near-infrared brain function imaging time series data and behavioral data generated on the basis of the evaluation normal form. According to the method, the brain function data of more dimensions associated with the cognitive function can be acquired in a short time, and multi-angle evaluation and analysis on the cognitive function of the subject can be conveniently and clinically performed based on the fNIRS.

The invention provides a visual motion function test system and method based on full-view surround synchronous visual stimulation, and is applied to the technical field of medical data processing. Visual stimulation parameters and rotating bar motion mode parameters are combined, the rolling direction of a bar and the rotating direction of a rotating bar are synchronously matched, a synchronous presentation scene of visual stimulation and motion tasks is established based on a four-screen linkage control technology and a rotating bar-screen adaptive design, and full-view stimulation constraint conditions are generated; processing is carried out based on visual stimulation parameters, rotating rod reference data, synchronous control logic and dynamic parameter combination constraint conditions, and rod time, rotating speed during falling and motion trail data are collected; processing the collected kinematics data, and combining functional parameters including visual state grouping and brain region neuron activation counting to generate motion balance ability and brain function associated data; and processing by combining normality test and an inter-group statistical method to generate a vision-motion function evaluation result.

The invention discloses a multi-modal interactive brain training system, method, device and medium. The system comprises a multi-modal signal acquisition layer, a brain dynamics modeling layer, a cross-modal cooperative training layer, a three-dimensional evaluation layer and a brain function adaptation scene generation layer. The multi-mode signal acquisition layer synchronously acquires surface physiological, behavior and deep brain image signals; the brain dynamics modeling layer preprocesses the signal, constructs a brain region interaction model and positions a core brain region pair and an interaction strength threshold value; the cross-modal cooperative training layer generates and adjusts a training task according to the training task; the three-dimensional evaluation layer fuses related features to generate a comprehensive rehabilitation score; the brain function adaptation scene generation layer adjusts training scene elements based on brain function bias features. According to the invention, through synchronous acquisition of multi-modal signals and dynamic modeling of brain functions, accurate adaptation of training tasks, scenes and brain region functions is realized; three-dimensional evaluation guarantees effect quantification and mechanism explanation, and rehabilitation pertinence, scientificity and user compliance are improved.

The invention discloses a system for analyzing reverse eye jump and cognitive brain network dysfunction of a Parkinson's disease patient, and relates to the technical field of medical diagnosis. The reverse eye jump data acquisition module is connected with the data collection module; the cognitive data acquisition and evaluation module is connected with the data collection module; the nuclear magnetic resonance imaging module is connected with the data collection module; the data preprocessing module is connected with the nuclear magnetic resonance imaging module; the independent component analysis module is connected with the data preprocessing module; the brain function connection analysis module is connected with the data preprocessing module and the independent component analysis module; and the statistical analysis module is respectively connected with the reverse eye jump data acquisition module, the cognitive data acquisition and evaluation module, the independent component analysis module and the brain function connection analysis module. According to the invention, the medical personnel can explore the relationship between the anti-glancing test and the cognition and brain network dysfunction, and the blank of the prior art is filled.

The present invention relates to an electroencephalogram (EEG) emotion recognition method based on brain network and matrix learning, comprising: obtaining an EEG data set of a subject, reconstructing the EEG data by using wavelet packet transform and dividing it into N sub-bands, calculating the phase-locking value between the reconstructed EEG data of different brain regions in each sub-band according to the reconstructed EEG data of different brain regions in each sub-band during emotion fluctuation by using Hilbert transform, and creating a brain functional connectivity matrix corresponding to each sub-band according to the phase-locking value between the reconstructed EEG data of different brain regions in each sub-band; constructing an ensemble learning classification model by using N classifiers and training the ensemble learning classification model; using the trained ensemble learning classification model to identify the corresponding feature sets of the brain functional connectivity matrices corresponding to the N sub-bands, obtaining classification results, and obtaining the EEG emotion classification results. This method can extract rich brain network feature information and further improve the accuracy of EEG emotion recognition.

A multimodal-based interactive chatbot service method for degenerative brain function decline prediction and cognitive training and an apparatus for the same are provided. A method for supporting prediction for a user's degenerative brain function decline may include outputting first reference content, and at least one system conversation triggering at least one user conversation related to the first reference content to the user through a user interface device; receiving at least one user conversation for the first reference content from the user through the user interface device; and based on at least one of text information corresponding to the at least one user conversation for the first reference content, or voice feature information of the at least one user conversation for the first reference content, acquiring a first prediction result for degenerative brain function decline of the user.

A system may comprise hardware and software configured to perform computing functions that mimic at least one computing function of a human brain, wherein the hardware and software comprises: analog circuitry configured to perform signal processing of neural signals obtained from living brain tissue using at least one sensor, a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor digital circuitry and software configured to process the obtained neural signals to generate a representation of a brain function from the obtained neural signals, and to generate parameters for use by analog and digital circuitry to perform computing functions that mimic at least one computing function of a human brain, and the analog and digital circuitry configured to use the generated parameters to perform computing functions that mimic at least one computing function of a human brain.

The application discloses a spatio-temporal graph-based ADHD brain function connection dynamic representation system and method, belongs to the technical field of medical image processing and artificial intelligence, and solves the defects of existing deep learning methods in constructing an ADHD brain function connection dynamic representation. The system designs an end-to-end learnable adaptive graph construction module, automatically discovers individualized topology through node embedding learning and scaled dot-product attention, guarantees physiological rationality by combining Top-K sparsification and prior graph fusion based on an RBF kernel, proposes a double-branch spatio-temporal graph convolution, uses dilated causal convolution in the time branch and uses graph attention in the space branch, realizes spatio-temporal joint modeling through adaptive gating fusion, introduces a multi-view contrast learning framework, designs three types of enhancement (time domain, frequency domain and graph structure) guided by domain knowledge, and fully utilizes unlabeled data by adopting an InfoNCE loss and two-stage training.

The present application discloses a system and interactive method for evaluating the cognitive function of a subject based on fNIRS. The head cap and near-infrared light emitting / receiving probe in the system enable the imaging range to cover the frontal lobe, temporal lobe, and occipital lobe; the cognitive assessment paradigm interactive device sequentially executes the inhibitory control assessment paradigm, the alert attention assessment paradigm, the selective attention assessment paradigm, the core attention network assessment paradigm, the working memory assessment paradigm, and the cognitive control assessment paradigm in a single-block mode in an order of execution from simple to difficult; the signal acquisition device collaboratively collects near-infrared brain function imaging time series data during the sequential execution of each assessment paradigm; the processing device evaluates the cognitive function of the subject based on the near-infrared brain function imaging time series data and the behavioral data generated based on the assessment paradigm. The present application can obtain brain function data of more dimensions associated with cognitive function in a relatively short period of time, which is convenient for clinical evaluation and analysis of the cognitive function of the subject from multiple angles based on fNIRS.

The application discloses a brain function state analysis method and device, which comprises the following steps: collecting a brain image sequence combination of a target object, preprocessing, then performing feature extraction on the brain image to obtain image features, and selecting key image features; performing quantitative analysis on the key image features to generate a feature parameter set; inputting the feature parameter set into a brain function state prediction model to determine the brain function state of the target object; the brain function state prediction model is obtained by integrating and then training a semantic retrieval model and a fine-tuning generation model, the semantic retrieval model is trained based on a brain knowledge document library, and the fine-tuning generation model is trained based on reinforced learning parameters, brain information segments retrieved by the semantic retrieval model, and a labeled data set of a mapping relationship; statistical characteristic values corresponding to each brain function state are determined, and brain function physiological data of the target object is analyzed. The application can quantitatively and standardize overall analysis of the brain function state.

The invention discloses an autistic child electroencephalogram collection cap fusing severe game intervention and a signal processing method.The autistic child electroencephalogram collection cap comprises a structure module, an electroencephalogram collection module, a wireless communication module, a power module, a signal processing module and a game and report module, and the electroencephalogram collection module collects electroencephalogram signals through five electrodes and two ear patch type electrodes; the wireless communication module is used for communication between the electroencephalogram cap and user terminal equipment, the power supply module is used for storing electric energy and supplying power to other modules, and the signal processing module is used for processing and analyzing electroencephalogram signals in the severe game intervention process, including filtering, slicing and normalization processing, and outputting standardized electroencephalogram data. And then the data is analyzed through an MCNN network model, and the condition of the children is diagnosed, so that an effective tool is provided for brain function research and diagnosis while the adaptability of the children with autism is improved.

A driver assistance device is provided with: a driving state detection unit that detects at least one of a driving behavior of a driver with respect to a vehicle, biometric information during driving, and a behavior of the vehicle; a cognitive function score calculation unit that calculates a numerical value indicating the height of the cognitive function of the driver on the basis of the detection result of the driving state detection unit; a cognitive function characteristic analysis unit that analyzes the calculated values as cognitive function characteristics relating to one or more different brain functions; a cognitive function storage unit for storing, in time series, the numerical values for the same driver calculated by the cognitive function score calculation unit and the analysis results of the cognitive function characteristic analysis unit; a cognitive function decline factor estimation unit that estimates, on the basis of the stored content, a main factor among a plurality of variation factors that cause a decline in the driver's cognitive function; and a driver assistance unit that assists the driver on the basis of the estimation result or information corresponding to the estimation result.