A Method and System for Assessing Motor and Cognitive Synergy Based on Electroencephalogram Signals

Abstract

This invention relates to the fields of neuroscience and brain cognitive function assessment, specifically to a method and system for assessing motor and cognitive synergistic function based on electroencephalogram (EEG) signals. The method includes: collecting EEG signal data from subjects performing motor imagery and cognitive tasks; extracting motor imagery features, cognitive task features, and brainwave propagation features from the EEG signal data; inputting these features into an assessment model to evaluate the subject's motor function, cognitive function, and synergistic function; and outputting a motor function score, a cognitive function score, a brainwave propagation score, and a comprehensive assessment index from the assessment model. This invention enables synergistic assessment of motor and cognitive functions, improving the objectivity and accuracy of assessment results, and is applicable to rehabilitation assessments for stroke and neurodegenerative diseases.

Description

Technical Field

[0001] This invention relates to the fields of neuroscience and brain cognitive function assessment, specifically to a method and system for assessing motor and cognitive coordination function based on electroencephalogram (EEG) signals. Background Technology

[0002] Stroke, traumatic brain injury, and neurodegenerative diseases often result in patients experiencing both motor and cognitive impairments. These impairments not only affect patients' daily living abilities but also significantly reduce their quality of life. Therefore, scientific and objective rehabilitation and assessment are of great importance.

[0003] With the development of brain-computer interface technology, clinicians have begun to use electroencephalogram (EEG) signals to objectively assess motor and cognitive dysfunction. However, existing EEG-based assessment methods mostly focus on single functional dimensions, analyzing only motor or cognitive functions and lacking systematic research on the synergistic relationship between the two. While the dual-task paradigm can assess an individual's processing ability under multi-task conditions by alternating between motor and cognitive tasks, it is essentially based on behavioral indicators and lacks analysis of neural mechanisms based on EEG signals, especially quantitative assessment of synergistic activities between brain regions and functional connections in brain networks. Therefore, the evaluation of the synergistic relationship between motor and cognitive functions is inaccurate. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for assessing motor and cognitive coordination functions based on electroencephalogram (EEG) signals. This invention enables objective quantitative assessment of a subject's motor function, cognitive function, and coordinated rehabilitation status. To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a method for assessing motor and cognitive coordination function based on electroencephalogram (EEG) signals is provided, including: Collect electroencephalogram (EEG) data from subjects while performing motor imagery and cognitive assessment tasks; Motor imagery features, cognitive task features, and brainwave propagation features are extracted from the EEG signal data. The motor imagery features, cognitive task features, and brainwave propagation features are input into the assessment model to evaluate the subject's motor function, cognitive function, and coordination function. The assessment model outputs motor function scores, cognitive function scores, traveling wave propagation scores, and a comprehensive assessment index.

[0005] Secondly, a motor and cognitive coordination function assessment system based on electroencephalogram (EEG) signals is provided, including: The EEG signal acquisition module is configured to acquire EEG signal data of subjects performing motor imagery tasks and cognitive assessment tasks. The feature extraction module is configured to extract motor imagery features, cognitive task features, and brainwave propagation features from EEG signal data. The scoring module is configured to input motor imagery features, cognitive task features, and brainwave propagation features into the assessment model to evaluate the subject's motor function status, cognitive function status, and coordination function status; the assessment model outputs motor function score, cognitive function score, brainwave propagation score, and comprehensive assessment index.

[0006] Thirdly, an electronic device is also provided, comprising: Memory, used for non-transitory storage of computer-readable instructions; and Processor, for executing the computer-readable instructions, When the computer-readable instructions are executed by the processor, they perform the method described in the first aspect above.

[0007] Fourthly, a computer-readable storage medium is provided having a program stored thereon that, when executed by a processor, implements the method described in the first aspect above.

[0008] Fifthly, a computer program product is provided, employing the following technical solution: A computer program product includes software code, wherein a program in the software code performs the steps of the method described in the first aspect of the present invention.

[0009] The above technical solution has the following advantages or beneficial effects: (1) This invention constructs a dual-task paradigm that combines motor imagery tasks and cognitive tasks to achieve coordinated assessment of motor function and cognitive function. Compared with traditional single-dimensional assessment methods, it can more comprehensively reflect the neurological function status of the subjects. (2) This invention introduces brainwave propagation features in feature extraction, which characterizes the synergistic relationship between brain regions from the perspective of spatial propagation of brain activity, making up for the shortcomings of traditional methods that only focus on the intensity of local brain region activity, and improving the precision of the assessment. (3) By integrating motor imagery features, cognitive task features and traveling wave propagation features, this invention constructs a multidimensional comprehensive assessment model, which can realize quantitative analysis of motor function, cognitive function and synergistic function, and improve the accuracy and stability of assessment. This invention can realize dynamic monitoring of neurological function status, providing a reliable basis for rehabilitation assessment and individualized intervention for patients with stroke, traumatic brain injury and neurodegenerative diseases, and has good clinical application prospects. Attached Figure Description

[0010] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0011] Figure 1 This is a flowchart of a method for assessing motor and cognitive coordination function based on electroencephalogram (EEG) signals in a specific embodiment of the present invention; Figure 2 This is a diagram of the visual stimulus materials used in the motion imagery task in a specific embodiment of the present invention; Figure 3 This is a flowchart illustrating the motion imagination paradigm in a specific embodiment of the present invention; Figure 4 This is a diagram of the visual stimulus materials used in the N-back task in a specific embodiment of the present invention; Figure 5 This is a flowchart illustrating the N-back paradigm in a specific embodiment of the present invention. Detailed Implementation

[0012] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0013] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the invention. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0014] In this embodiment of the invention, "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of this invention, "multiple" refers to two or more.

[0015] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0016] All data acquisition in this embodiment is carried out in accordance with laws and regulations and with user consent, and the data is used legally.

[0017] Example 1 like Figure 1As shown, this embodiment provides a method for assessing motor and cognitive coordination function based on electroencephalogram (EEG) signals, including: S1: Collect EEG signal data from subjects while performing motor imagery and cognitive assessment tasks; S2: Extract motor imagery features, cognitive task features, and brainwave propagation features from the EEG signal data; S3: Input the motor imagery features, the cognitive task features, and the EEG traveling wave propagation features into the assessment model to assess the subject's motor function status, cognitive function status, and coordination function status; the assessment model outputs motor function score, cognitive function score, traveling wave propagation score, and comprehensive assessment index.

[0018] The specific process of step S1 is as follows: S1.1: The subject faces the display screen and performs a motor imagery task according to the prompts.

[0019] In this embodiment, as Figure 2 As shown, the motor imagery task specifically includes motor imagery tasks involving the left hand, right hand, left foot, and right foot. Different visual stimuli are presented through random upper and lower limb movements to guide the subject in motor imagery. In this embodiment, four different upper and lower limb movements are used; those skilled in the art can set these according to their needs. A reminder instruction is given before the visual stimulus is presented to attract attention and remind the subject to begin the task. In this embodiment, the reminder instruction lasts for 1.5 seconds, and each visual stimulus lasts for 3 seconds; those skilled in the art can set other values ​​according to their needs.

[0020] like Figure 3 The diagram shown illustrates the entire flowchart of a motor imagery paradigm. A paradigm is a complete experimental framework used to guide subjects in performing specific psychological or behavioral activities. It includes the stimulus presentation method, temporal sequence, instructions, trial structure, and rules for switching between training and experimental modes. A task refers to the specific cognitive or motor operation performed by the subject in each trial or stage within the paradigm framework. A paradigm can contain one or more tasks; for example, a motor imagery paradigm can include motor imagery tasks involving different limbs such as the left hand, right hand, and lower limbs.

[0021] In this embodiment, the paradigm includes a training mode and an experimental mode. The training mode is used for rehabilitation training, and the experimental mode is used for simultaneous acquisition of EEG signals. The training mode and experimental mode have the same task paradigm, the difference being that the EEG signals acquired in the experimental mode have corresponding labels, while only EEG signals are acquired in the training mode. In some embodiments, each paradigm first performs the training mode, and then selects whether to enter the experimental mode.

[0022] S1.2: Subjects face the display screen and perform a cognitive assessment task according to the prompts.

[0023] In this embodiment, as Figure 4 As shown, images of the twelve Chinese zodiac animals are used as visual stimuli, specifically including images of the rat, ox, tiger, rabbit, dragon, snake, horse, sheep, monkey, rooster, dog, and pig—a total of 12 categories. The system randomly presents a sequence of zodiac images, and the subjects respond by pressing buttons based on comparisons.

[0024] In this embodiment, the 1-back task mode of the N-back task is specifically adopted, meaning the subject needs to compare the current visual stimulus with the previous stimulus. Those skilled in the art can also modify the task mode based on actual needs. Other cognitive assessment tasks, such as the Oddball task or the Stroop task, can also be used.

[0025] The entire paradigm process of the N_back task is as follows: Figure 5 As shown, at the start of the experiment, the system first presents instructions explaining the task rules and key presses: observe the animal images on the screen and determine if the current image is the same as the previous one. If they are different: press the number "1". If they are the same: press the number "3". Press the space bar to begin practice. A training phase is conducted before the formal experiment. After the training, participants can press the space bar to enter the formal experiment or press "2" to re-enter the training phase.

[0026] S1.3: Collect EEG signals in experimental mode.

[0027] In this embodiment, a 64-lead EEG acquisition device was used for data acquisition at a sampling rate of 1000Hz; the scalp electrode impedance was controlled to be below 20kΩ to ensure signal quality. During EEG signal acquisition, synchronous labeling was performed in experimental mode to ensure consistency between the paradigm stimulation and the timing of EEG data acquisition.

[0028] The specific process of step S2 is as follows: S2.1: Preprocess the acquired raw EEG signals. The raw EEG signals include EEG signals from the motor imagery task and EEG signals from the N-back task.

[0029] S2.1.1: Bandpass filtering is performed on the acquired raw EEG signals.

[0030] The raw EEG signal is processed and synchronized. After synchronization and marking, bandpass filtering is performed to filter out low-frequency drift and high-frequency noise. In this embodiment, the bandpass filtering range is set to 0.5Hz to 45Hz to retain the main physiological frequency components in the EEG signal, including delta waves, theta waves, alpha waves, and beta waves.

[0031] S2.1.2: To eliminate power supply interference, a notch filter is further used to remove AC interference frequencies in addition to bandpass filtering, thereby improving the signal-to-noise ratio. In this embodiment, the notch filter is set to 50Hz.

[0032] S2.1.3: After completing the filtering process, the original sampling frequency is downsampled.

[0033] In this embodiment, the original sampling frequency is downsampled to 200Hz to reduce the amount of data and improve subsequent computational efficiency. This achieves a balance between computational complexity and signal strength while maintaining time resolution.

[0034] S2.1.4: Wavelet denoising is used to further process the EEG signal to reduce high-frequency electromyography interference.

[0035] S2.1.5: Independent Component Analysis (ICA) was used to remove artifacts generated by non-EEG activities such as eye movements and ECG. The final results were the preprocessed EEG signals for the motor imagery task and the preprocessed EEG signals for the N-back task.

[0036] S2.2: Extracting motor imagery features from preprocessed EEG signals of the motor imagery task. Motor imagery features include motion-related rhythm features, event-related desynchronization features, and spatial features. Motor imagery features are primarily extracted from central EEG channels, including but not limited to C3, C4, and Cz channels.

[0037] S2.2.1: Extract motor-related rhythm features from the central lead signals of the preprocessed motor imagery task EEG signals. In this embodiment, μ-rhythm and β-rhythm features are extracted, where the μ-rhythm frequency band is 8Hz–13Hz and the β-rhythm frequency band is 13Hz–30Hz. The power distribution within the above frequency bands is calculated by power spectral density analysis to characterize the intensity of motor cortex activity.

[0038] In some embodiments, left-side limb motor imagery corresponds to feature extraction of the right motor cortex, and right-side limb motor imagery corresponds to feature extraction of the left motor cortex.

[0039] S2.2.2: By comparing the frequency band power changes during the baseline phase and the motion imagination phase, event-related desynchronization features (ERD) are extracted. The calculation formula is as follows: (1); Where ERD (%) represents the percentage of event-related desynchronization; This represents the average power within the target frequency band during the baseline phase; The ERD value represents the average power within the target frequency band during the motor imagery phase. A larger ERD value indicates a more significant decrease in power within the target frequency band during motor imagery, suggesting a higher level of activation in the subject's motor cortex and stronger participation in motor imagery.

[0040] S2.2.3: Spatial features are extracted using the common space pattern method, expressed as follows: (2); Where w represents the spatial filtering vector; Σ1 and Σ2 represent the covariance matrices corresponding to the two types of motion image signals, respectively; represents the transpose of vector w; argmax represents the parameter that maximizes the objective function.

[0041] Spatial features can enhance the separability between different categories of motion imagination.

[0042] S2.2.4: Construct a motion imagery feature vector F based on motion-related rhythmic features, event-related desynchronization features, and spatial features. MI .

[0043] S2.3: Extracting cognitive task features based on preprocessed N-back task EEG signals. Cognitive task features include event-related potential (P300) features, event-related potential waveform features, and time-domain statistical features.

[0044] S2.3.1: Extract the relevant potential P300 features.

[0045] Event-related potential (P300) features were extracted within a time window of approximately 250 ms to 500 ms after stimulus presentation. These features included P300 amplitude and P300 latency. The P300 amplitude reflects the subject's attentional resource allocation level, while the P300 latency reflects information processing speed and working memory efficiency.

[0046] S2.3.2: Extract event-related potential waveform features.

[0047] The event-related potential waveform characteristics are obtained by averaging the number of trials of the same type of stimulus over time, and their expression is as follows: (3); Where ERP(t) represents the average event-related potential value at time t; N represents the number of trials; x i (t) represents the EEG signal at time t in the i-th trial; Σ represents the summation over all trials.

[0048] S2.3.3: Extract time-domain statistical features.

[0049] Temporal statistical features, including mean, peak value, peak-to-peak value, and variance, were extracted from the preprocessed N-back task EEG signals to supplement the dynamic changes in the cognitive processing.

[0050] In this embodiment, wavelet transform is used to extract time-frequency features, and its expression is as follows: (4); Where W(a, b) represents the wavelet transform coefficients; x(t) represents the preprocessed N-back task EEG signal; ψ represents the mother wavelet function; a represents the scaling parameter; b represents the time shift parameter; and ∫ represents the integration operation. This time-frequency feature can be used to characterize the temporal evolution of rhythmic changes during motor imagery, as well as the dynamic changes in the EEG response after stimulation in the N-back task.

[0051] S2.3.4: Based on the characteristics of event-related potential P300, Based on relevant statistical features and time-domain statistical features, a cognitive task feature vector F is constructed. N-back .

[0052] S2.4: Based on the preprocessed N-back task EEG signals, extract the traveling wave propagation features. These features include: phase gradient, propagation direction, propagation speed, and propagation consistency index. In this embodiment, traveling wave analysis is used to extract the traveling wave feature F from the preprocessed multi-channel EEG signals. tw .

[0053] S2.4.1: The instantaneous phase φ(t) of the EEG signal in each channel is calculated using the Hilbert transform, and its expression is as follows: (5); Where H[.] represents the Hilbert transform, and x(t) is the electroencephalogram (EEG) signal.

[0054] S2.4.2: Calculate the phase gradient based on the instantaneous phase φ(t) of the EEG signal; determine the propagation direction of the EEG signal in the scalp space based on the phase gradient; calculate the propagation speed based on the phase change rate; calculate the propagation consistency index based on phase consistency.

[0055] The assessment model in step S3 includes a motor function assessment sub-model, a cognitive function assessment sub-model, and a traveling wave propagation assessment sub-model.

[0056] S3.1: Construct a sub-model for motor function assessment, input motor imagery features into the sub-model for motor function assessment, and obtain a motor function score.

[0057] In this embodiment, the motor function assessment sub-model uses linear discriminant analysis to score motor imagery features. In some embodiments, the motor function assessment sub-model may also use a support vector machine.

[0058] The optimization objective of linear discriminant analysis is as follows: (6); Where w represents the projection vector; Represents the inter-class scatter matrix; Represents the within-class scatter matrix; This represents the transpose of vector w.

[0059] By inputting the motor imagery features into the motor function assessment sub-model and performing discriminant analysis on the motor imagery features, a motor function score can be obtained, the expression of which is as follows: (7); in, f1 represents the motor function score; f1 represents the mapping function established based on motor imagery features; This represents the feature vector of motion imagination.

[0060] In this embodiment, the specific process of establishing the mapping function f1 based on motion imagery features is as follows: the extracted motion imagery feature vector F... MI As input features for the training set, corresponding clinical standard motor function scores (e.g., Fugl-Meyer exercise scale scores) are obtained as true labels; a specific machine learning algorithm model (e.g., support vector regression SVR) is constructed; by minimizing the loss function between the predicted score and the true label, the model weight parameters are iteratively updated using an optimization algorithm until the model converges or reaches the preset number of iterations, and the model parameters at this time are saved, that is, the mapping function f1 is established.

[0061] Motor function score It is used to characterize the subject's motor imagery ability and the level of activation of the motor-related cortex.

[0062] S3.2: Construct a cognitive function assessment sub-model, input the cognitive task features into the cognitive function assessment sub-model, and obtain a cognitive function score.

[0063] In this embodiment, the cognitive function assessment sub-model adopts a classification model or a regression model, and its expression is as follows: (8); in, f1 represents the cognitive function score; f2 represents the mapping function from cognitive features to the score results.

[0064] Cognitive function score It is used to characterize subjects' attention allocation ability, working memory processing ability, and information processing efficiency in the N-back task.

[0065] In some embodiments, the classification model used by the cognitive function assessment sub-model can be any one of support vector machine (SVM), random forest (RF), or K-nearest neighbor algorithm (KNN); the regression model used is preferably any one of ridge regression, multiple linear regression (MLR), or backpropagation neural network (BPNN).

[0066] In some embodiments, the specific construction process of the mapping function f2 from cognitive features to scoring results is as follows: the cognitive task features of the obtained subject's N-back task are used as training input, and the standard clinical cognitive function score (e.g., the Brief Mental State Examination (MMSE) score) is used as the true label. Supervised training is performed using a classification or regression model, and the model parameters are optimized to minimize the prediction error. After the model training is completed, the established mapping function f2 is obtained.

[0067] S3.3: Construct a traveling wave propagation assessment sub-model, input the EEG traveling wave propagation characteristics into the traveling wave propagation assessment sub-model, and obtain the traveling wave propagation score.

[0068] The traveling wave propagation assessment sub-model uses either a classification or regression model. EEG traveling wave propagation features are input into the traveling wave propagation assessment sub-model to obtain a traveling wave propagation score, expressed as follows: (9); Among them, TW score f represents the traveling wave propagation score; f3 represents the mapping function from the traveling wave propagation characteristics to the score result; F tw This represents the characteristic vector of traveling wave propagation.

[0069] Traveling wave propagation score TW score It is used to characterize the organization of brain electrical activity in the scalp space, the degree of cross-brain region coordination, and the ability to control the timing of neural oscillations, thereby reflecting the dynamic state of the brain network during the subject's motor and cognitive coordinated processing.

[0070] In some embodiments, the specific process of establishing the mapping function f3 from the traveling wave propagation features to the scoring results is as follows: using the extracted EEG traveling wave propagation feature vector F... tw As input to the model, the actual spatiotemporal coordination quantitative evaluation index of the subject's nervous system is used as the true label to train and optimize the parameters of the classification or regression model. After the loss function converges, the network weights are saved, thus establishing the mapping function f3.

[0071] In some embodiments, the classification model used by the traveling wave propagation evaluation sub-model can be any one of support vector machine (SVM), K-nearest neighbor algorithm (KNN) or decision tree; the regression model used can be any one of Gaussian process regression (GPR), support vector regression (SVR) or multiple linear regression (MLR).

[0072] S3.4: The motor function score, cognitive function score, and traveling wave propagation score are integrated to construct a comprehensive evaluation index, the expression of which is as follows: (10); Where CI represents the comprehensive evaluation index, and α, β and γ represent the weighting coefficients (α+β+γ=1).

[0073] In some embodiments, the weighting coefficients α, β, and γ can be set according to actual assessment needs. When it is necessary to focus on assessing motor function, the value of α should be appropriately increased; when it is necessary to focus on assessing cognitive function, the value of β should be appropriately increased; and when it is necessary to focus on assessing traveling wave propagation, the value of γ should be appropriately increased.

[0074] Using the above methods, motor function scores, cognitive function scores, traveling wave propagation scores, and comprehensive assessment indices can be output, and the neurological function status of the subjects can be graded or the rehabilitation effect analyzed accordingly.

[0075] The assessment model in this embodiment can achieve joint analysis of the subject's motor imagery state, cognitive task state, and spatial propagation state of EEG activity, providing a more comprehensive quantitative basis for neurorehabilitation assessment and individualized intervention.

[0076] Example 2 This embodiment provides a motor and cognitive coordination function assessment system based on electroencephalogram (EEG) signals, including: The EEG signal acquisition module is configured to acquire EEG signal data of subjects performing motor imagery tasks and cognitive assessment tasks. The feature extraction module is configured to extract motor imagery features, cognitive task features, and brainwave propagation features from EEG signal data. The scoring module is configured to input motor imagery features, cognitive task features, and brainwave propagation features into the assessment model to evaluate the subject's motor function status, cognitive function status, and coordination function status; the assessment model outputs motor function score, cognitive function score, brainwave propagation score, and comprehensive assessment index.

[0077] It should be noted that the aforementioned EEG signal acquisition module, feature extraction module, and scoring module correspond to steps S1 to S3 in Embodiment 1. The examples and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in Embodiment 1. It should also be noted that these modules, as part of the system, can be executed in a computer system, such as a set of computer-executable instructions.

[0078] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0079] The proposed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0080] Example 3 This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the method described in Embodiment 1.

[0081] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0082] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0083] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0084] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0085] Those skilled in the art will recognize that the units and algorithm steps described in connection with the various examples of this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0086] Example 4 Embodiment 4 of the present invention provides a computer-readable storage medium.

[0087] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of the method as described in Embodiment 1 of the present invention.

[0088] The detailed steps are the same as those provided in Example 1, and will not be repeated here.

[0089] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0090] A computer program product includes software code, wherein the program in the software code performs the steps described in Embodiment 1 of the present invention.

[0091] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing motor and cognitive coordination function based on electroencephalogram (EEG) signals, characterized in that, include: Collect electroencephalogram (EEG) data from subjects while performing motor imagery and cognitive assessment tasks; Motor imagery features, cognitive task features, and brainwave propagation features are extracted from the EEG signal data. The motor imagery features, cognitive task features, and brainwave propagation features are input into the assessment model to evaluate the subject's motor function, cognitive function, and coordination function. The assessment model outputs motor function scores, cognitive function scores, traveling wave propagation scores, and a comprehensive assessment index.

2. The method for assessing motor and cognitive coordination function based on electroencephalogram (EEG) signals as described in claim 1, characterized in that, The motor imagery task includes motor imagery tasks involving the left hand, right hand, left foot, and right foot. Different visual stimuli are presented through random upper and lower limb movements to guide the subject in motor imagery. The cognitive assessment task adopts the N-back task.

3. The method for assessing motor and cognitive coordination function based on electroencephalogram (EEG) signals as described in claim 1, characterized in that, The motion imagery features include motion-related rhythmic features, event-related desynchronization features, and spatial features; The cognitive task features include event-related potential (P300) features, event-related potential waveform features, and time-domain statistical features.

4. The method for assessing motor and cognitive coordination function based on electroencephalogram (EEG) signals as described in claim 1, characterized in that, The propagation characteristics of the brainwaves include: phase gradient, propagation direction, propagation speed, and propagation consistency index.

5. The method for assessing motor and cognitive coordination function based on electroencephalogram (EEG) signals as described in claim 1, characterized in that, The assessment model includes a motor function assessment sub-model, a cognitive function assessment sub-model, and a traveling wave propagation assessment sub-model. Motor imagery features are input into the motor function assessment sub-model to obtain a motor function score; cognitive task features are input into the cognitive function assessment sub-model to obtain a cognitive function score; and cognitive task features are input into the cognitive function assessment sub-model to obtain a cognitive function score.

6. The method for assessing motor and cognitive coordination function based on electroencephalogram (EEG) signals as described in claim 5, characterized in that, The motor function score, the cognitive function score, and the traveling wave propagation score are integrated to construct a comprehensive evaluation index; the specific formula for the comprehensive evaluation index is as follows: Where CI represents the comprehensive evaluation index, MI score Indicates motor function score; Cog score Indicates cognitive function score, TW score The score represents the traveling wave propagation score, and α, β, and γ represent the weighting coefficients.

7. A motor and cognitive coordination function assessment system based on electroencephalogram (EEG) signals, characterized in that, include: The EEG signal acquisition module is configured to acquire EEG signal data of subjects performing motor imagery tasks and cognitive assessment tasks. The feature extraction module is configured to extract motor imagery features, cognitive task features, and brainwave propagation features from the EEG signal data. The scoring module is configured to input the motor imagery features, the cognitive task features, and the EEG traveling wave propagation features into the assessment model to evaluate the subject's motor function status, cognitive function status, and coordination function status; the assessment model outputs motor function score, cognitive function score, traveling wave propagation score, and comprehensive assessment index.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method for assessing motor and cognitive coordination functions based on electroencephalogram signals as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the method for assessing motor and cognitive synergistic function based on electroencephalogram signals as described in any one of claims 1-6.

10. A computer program product, comprising software code, characterized in that, The program in the software code executes the steps in the method for assessing motor and cognitive synergistic function based on electroencephalogram signals as described in any one of claims 1-6.

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