A cognitive assessment training method and system based on a cortical network compensation mechanism

Abstract

The application discloses a kind of cognitive evaluation training method and system based on cortical network compensation mechanism.The method comprises the following steps: S1: the cognitive evaluation of patient is carried out, while detecting the brain electrical signal of patient;S2: by the first data processing module, the cognitive evaluation data and brain electrical signal of step S1 are received and carried out real-time processing and fusion calculation;S3: according to the comprehensive ability level value of each brain network and the overall cognitive function level value output by step S2, cognitive training scheme is given;S4: in the process of implementing cognitive training scheme, by transcranial direct current stimulation module, synchronous electric stimulation is carried out.The application realizes the targeted evaluation of whole brain network by combining cognitive evaluation and EEG monitoring, so as to accurately locate damaged brain network;Further, the method of cognitive training combined with EEG and transcranial direct current stimulation is used, the effective complement of cognitive training and transcranial direct current stimulation is realized, and the purpose of enhancing training effect is achieved.

Description

Technical Field

[0001] This invention relates to a cognitive assessment training method based on cortical network compensation mechanisms, and also to a corresponding cognitive assessment training system, belonging to the field of cognitive neuroscience technology. Background Technology

[0002] Cognitive training is a scientific assessment and systematic training program designed based on the theory of neuroplasticity to target human cognitive abilities. It typically employs standardized experimental paradigms used in cognitive neuroscience research to design assessment and training programs. This allows for the assessment and training of various cognitive abilities, including attention, perception, memory, thinking, emotional intelligence, and cognitive flexibility. Furthermore, it guides users through carefully designed training courses tailored to their individual needs.

[0003] However, current cognitive assessment technologies primarily rely on existing scales and task-based methods. Therefore, existing cognitive assessment scales lack granularity and precision in diagnosis. Furthermore, in terms of cognitive intervention, cognitive training alone is insufficient for some patients, and targeted intervention remains unclear. Additionally, both transcranial direct current stimulation (tDCS) alone and in combination with EEG (electroencephalography) as an adjunct monitoring and treatment method suffer from limitations such as high cost and poor portability, resulting in low adoption rates.

[0004] Chinese invention application No. 202211424278.2 discloses a method for personalized transcranial electrical stimulation (TCS) intervention in cognitive impairment. This method includes cognitive training of a user through selected task groups, acquiring the user's electroencephalogram (EEG) signals during cognitive training, analyzing the user's cognitive training behavior and EEG signals to generate personalized TCS parameters, controlling a non-invasive transcranial alternating current (TCCAC) stimulation device to provide personalized TCS intervention, selecting the next task group based on the cognitive training results of the current task group, and repeating the method based on the selected task groups until the user completes a preset number of task groups. This method combines TCS with cognitive training to achieve personalized intervention, more effectively promoting intervention to improve cognitive impairment. The stimulation is insensitive and unlikely to interfere with the normal functioning of other brain regions, providing a more comfortable user experience. Summary of the Invention

[0005] The primary technical problem to be solved by this invention is to provide a cognitive assessment training method based on the cortical network compensation mechanism.

[0006] Another technical problem to be solved by the present invention is to provide a cognitive assessment and training system based on the cortical network compensation mechanism.

[0007] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0008] According to a first aspect of the present invention, a cognitive assessment training method based on a cortical network compensation mechanism is provided, comprising the following steps:

[0009] S1: Perform cognitive assessment on the patient and simultaneously detect the EEG signals corresponding to each question during the cognitive assessment process;

[0010] S2: The first data processing module receives the cognitive assessment data and EEG signals from step S1 and performs real-time processing and fusion calculation;

[0011] S3: Based on the comprehensive ability level value of each brain network and the overall cognitive function level value output in step S2, a cognitive training plan is given.

[0012] S4: During the implementation of the cognitive training program, the patient receives synchronous electrical stimulation from the transcranial direct current stimulation module.

[0013] Preferably, step S1 includes the following sub-steps:

[0014] S10: Assess the patient's cognitive abilities through the cognitive assessment module;

[0015] S11: Simultaneously with step S10, the EEG signal of the patient when completing step S10 is acquired through the EEG module;

[0016] S12: Send the cognitive assessment data obtained in step S10 and the EEG signals monitored in step S11 to the first data processing module.

[0017] Preferably, step S2 includes the following sub-steps:

[0018] S20: The first data processing module amplifies the received EEG signals, extracts the EEG signals corresponding to the cognitive assessment questions, and analyzes them.

[0019] S21: In sync with step S20, the first data processing module analyzes the cognitive assessment results of the current question and displays a non-dynamic graph of the time-frequency curve of the entire cognitive assessment period, synchronized with the aforementioned brain network curve.

[0020] S22: The first data processing module fuses and calculates the cognitive assessment data with the EEG signals, and outputs the comprehensive ability level value of each brain network and the overall cognitive function level value.

[0021] Preferably, the comprehensive ability level value N of the brain network is N = Nc + Ne;

[0022] Wherein, Nc is the cognitive assessment data corresponding to the cognitive assessment questions and the brain network, and Ne is the EEG signal monitoring value corresponding to the brain network.

[0023] Preferably, the overall cognitive function level value is G, where G = ∑[∑(N1c+N1e) / n+∑(N2c+N2e) / n+……+∑(Nnc+Nne) / n], and n is the number of the brain network.

[0024] One preferred approach is to rank the comprehensive ability levels of different brain networks from high to low, and then prioritize training the brain networks with the lowest scores.

[0025] According to a second aspect of the present invention, a cognitive assessment and training system based on a cortical network compensation mechanism is provided, comprising: a cognitive assessment module, an EEG module, a first data processing module, a second data processing module, a cognitive training module, and a transcranial direct current stimulation module; wherein,

[0026] The cognitive assessment module is used to perform cognitive assessments and obtain cognitive assessment data.

[0027] The EEG module is used to detect the EEG signals corresponding to each question during the cognitive assessment process while performing cognitive assessment, and obtain the EEG signals.

[0028] The first data processing module receives cognitive assessment data and EEG signals for real-time processing and fusion calculation;

[0029] The second data processing module is used to provide a cognitive training plan based on the comprehensive ability level value of each brain network and the overall cognitive function level value output by the first data processing module.

[0030] The cognitive training module is used to perform cognitive training according to the cognitive training scheme; the transcranial direct current stimulation module performs electrical stimulation synchronously during the cognitive training.

[0031] Preferably, the first data processing module amplifies the received EEG signal, extracts the EEG signal corresponding to the cognitive assessment question, and analyzes it.

[0032] The first data processing module synchronously analyzes the cognitive assessment results of the current question and displays a non-dynamic graph of the time-frequency curve of the entire cognitive assessment period, synchronized with the aforementioned brain network curve.

[0033] The first data processing module fuses cognitive assessment data with EEG signals to calculate and output the comprehensive ability level value of each brain network and the overall cognitive function level value.

[0034] Preferably, the second data processing module sorts the comprehensive ability level values ​​from high to low, and selects the brain networks with the lowest scores for priority training.

[0035] Compared with existing technologies, this invention achieves targeted assessment of the whole brain network by combining cognitive assessment and EEG monitoring, thereby accurately locating the damaged brain network; furthermore, it adopts a method of combining cognitive training with EEG and transcranial direct current stimulation to provide dual intervention for different degrees of cognitive function impairment detected by EEG, so as to achieve effective complementarity between cognitive training and transcranial direct current stimulation and achieve the goal of enhancing the effect of cognitive training. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the structure of the cognitive assessment and training system based on the cortical network compensation mechanism in the first embodiment of the present invention.

[0037] Figure 2 This is a photograph of the EEG module monitoring the patient's electroencephalogram (EEG) signals, as shown in the first embodiment of the present invention. Detailed Implementation

[0038] The technical content of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0039] like Figure 1 As shown, the cognitive assessment and training system based on the cortical network compensation mechanism provided in this embodiment of the invention includes: a cognitive assessment module, an EEG (Electroencephalogram) module, a first data processing module, a second data processing module, a cognitive training module, and a transcranial direct current stimulation (tDCS) module.

[0040] In this embodiment of the invention, the cognitive assessment training method based on the cortical network compensation mechanism includes:

[0041] S1: Perform cognitive assessment on the patient and simultaneously detect the patient's electroencephalogram (EEG) signals.

[0042] Specifically, this includes steps S10 to S12.

[0043] S10: Assess the patient's cognitive abilities through the cognitive assessment module.

[0044] Specifically, cognitive assessments of patients are conducted using electronic task-based assessment tools such as the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). This cognitive assessment refers to the evaluation of the overall cognitive domain or individual cognitive domains. Cognitive domains include attention and concentration, executive function, memory, language, visual-structural skills, abstract thinking, and calculation and orientation.

[0045] The Montreal Cognitive Assessment Scale (MCAS) consists of 11 items with a total score of 30. The normal score is ≥26. The MCAS assesses various cognitive abilities, including: cube copying, number repetition, clapping while listening to target words, word memorization, and mental arithmetic with consecutive subtraction. Each item covers a corresponding cognitive domain. Each cognitive domain belongs to one or more types of brain networks. In one embodiment of this invention, the brain network segmentation method uses the Cole-Anticevic approach, which is based on Glasser et al.'s multimodal parcellation (MMP), a relatively new segmentation method. Specifically, this brain network segmentation method includes not only cortical regions but also subcortical regions. The brain network is divided into twelve networks, including: primary visual network, secondary visual network, somatic motor network, tegmental network, dorsal attention network, language network, frontoparietal network, auditory network, default mode network, posterior multimodal network, ventral multimodal network, and orbitofrontal affective network.

[0046] In one embodiment of the invention, each cognitive domain is stored in correspondence with one or more types of brain networks to which it belongs. For example, when a patient completes the task of "clapping after hearing a target word," they need to perceive the sound stimulus, further process and judge the heard information (auditory network), and then send a response signal to the sensorimotor nerves (somatic motor network) to complete the output response of hand movement. Therefore, the first cognitive domain (clapping after hearing a target word) is stored in correspondence with the auditory network and the somatic motor network to establish a correspondence.

[0047] S11: While conducting a cognitive assessment of the patient, the EEG module is used to collect the patient's electroencephalogram (EEG) signals when the patient completes step S10.

[0048] Specifically, such as Figure 2 As shown, when performing step S10, that is, when the patient completes the questions on the MoCA scale, the corresponding brain network is activated, and the EEG module is used to monitor the electroencephalogram (EEG) signals of the activated brain network simultaneously.

[0049] This EEG module carries EEG acquisition software, enabling the display, analysis, storage, and offline playback of EEG signals acquired by a wireless amplifier and synchronization signals acquired by a multi-parameter synchronizer. Furthermore, the software can perform offline impedance measurement and real-time impedance monitoring during signal acquisition. Time-frequency analysis of electrophysiological signals and a series of characteristic parameters can be observed and analyzed in real time through the software's trend graph analysis function. Relevant data can be transmitted in real time via a data interface for integration and analysis by other software.

[0050] S12: Send the cognitive assessment data and the synchronously monitored EEG signals during the cognitive assessment process to the first data processing module.

[0051] In addition, in this step S11, the following two situations are divided according to the different diagnostic status of the patients.

[0052] The first scenario: For undiagnosed patients, provided that there are enough cognitive assessment tasks, they can be flexibly combined to ensure that each assessment covers the whole brain network and forms a closed loop with whole brain EEG monitoring.

[0053] The second scenario: For diagnosed patients, based on existing medical research results, the specific damaged brain network caused by the disease is identified. The system automatically or manually selects a cognitive assessment task that matches the damaged brain network, and simultaneously collects EEG signals from the whole brain network or specific brain networks.

[0054] S2: The first data processing module receives the cognitive assessment data and EEG signals from step S1 and performs real-time processing and fusion calculation.

[0055] Specifically, this includes steps S20 to S22.

[0056] S20: The first data processing module amplifies the received EEG signals, extracts the EEG signals corresponding to the cognitive assessment questions, and analyzes them. For example, if the current cognitive assessment question is "listen to words and clap your hands," then the question corresponds to the auditory network and the somatic motor network in the brain network. The first data processing module will then fuse and analyze the EEG signals of these two brain networks and output real-time curves of these two brain networks.

[0057] S21: The first data processing module analyzes the cognitive assessment results of the current question, synchronizes with the curves of the two brain networks mentioned above, and displays the time-frequency curve non-dynamic graph of the entire cognitive assessment period.

[0058] S22: The first data processing module fuses and calculates the cognitive assessment data with the EEG signal, and outputs the comprehensive ability level value Nn (n is the number of the brain network) of each brain network and the overall cognitive function level value G (the average of the functional levels of n neural networks).

[0059] Specifically, assuming a certain brain network (also called an attention network) is numbered 1 in all brain networks, the overall ability level value of the attention network is N1 = N1c + N1e. Here, N1c is the cognitive assessment data corresponding to the cognitive assessment question for that attention network, and N1e is the EEG signal monitoring value corresponding to that attention network. If a certain brain network corresponds to multiple cognitive assessment questions, then N1c is the average of the cognitive assessment data corresponding to the multiple cognitive assessment questions, and N1e is the average of the EEG signal monitoring values ​​for the multiple cognitive assessment questions. In this case, the overall cognitive function level value is G = ∑[∑(N1c + N1e) / n + ∑(N2c + N2e) / n + ... + ∑(Nnc + Nne) / n].

[0060] In addition, the N1e extraction and analysis were performed using the following methods: wavelet transform was used to extract coupling features from the EEG signals of the same brain network monitored by the EEG module; the S-estimator was used to extract synchronization features from the EEG signals of the same brain network monitored by the EEG module; and the SVM classifier was used to classify the electrical signal spectrum and complexity feature values ​​of different brain networks under cognitive question states in sequence.

[0061] S3: Based on the comprehensive ability level value Nn of each brain network and the overall cognitive function level value G output in step S2, a cognitive training plan is given.

[0062] Specifically, assuming the aforementioned cognitive assessment covers n brain networks, the comprehensive ability level value of these n brain networks is output. Then, these comprehensive ability level values ​​are sorted from highest to lowest, and the five brain networks with the lowest scores are prioritized for training. Next, five tasks are completed daily, and after one week of training, steps S1–S3 are repeated. A new training plan is then generated based on the new comprehensive ability level values ​​Nn of each brain network and the overall cognitive function level value G.

[0063] S4: During the implementation of the cognitive training program, the patient receives synchronous electrical stimulation from the transcranial direct current stimulation module.

[0064] Specifically, patients need to wear a transcranial direct current stimulation module during the cognitive training program provided in step S3.

[0065] After each training task, the training data is uploaded to the second data processing module for analysis and calculation, serving as the brain network functional level during the training process. If a patient's score on a particular training task is lower than 1.5 SD (SD being the standard deviation) of the group mean for that task, it is determined that electrical stimulation by the transcranial direct current (TCD) stimulation module is required; otherwise, supplementary electrical stimulation by the TCD module is not initiated. The supplementary electrical stimulation by the TCD module is applied to the brain network corresponding to the current task, with a stimulation duration of 30 seconds and a stimulation intensity of 1 m. After the TCD stimulation module's electrical stimulation ends, the next training task will begin, and the intervention for the day will conclude after completing five tasks sequentially.

[0066] In summary, the cognitive assessment and training method and system based on cortical network supplementation mechanism provided by this invention combines cognitive assessment and EEG monitoring to achieve targeted assessment of the whole brain network, thereby accurately locating damaged brain networks. Furthermore, by employing a method that combines cognitive training with EEG and transcranial direct current stimulation, dual intervention is carried out on different degrees of cognitive function impairment detected by EEG, achieving effective complementarity between cognitive training and transcranial direct current stimulation, thereby enhancing the effect of cognitive training.

[0067] It should be noted that the various embodiments in this invention can be combined to form new embodiments, all of which are within the protection scope of this invention.

[0068] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0069] The cognitive assessment and training method and system based on cortical network compensation mechanism provided by this invention have been described in detail above. Any obvious modifications made by those skilled in the art without departing from the essence of this invention will constitute an infringement of the patent rights of this invention and will incur corresponding legal liability.

Claims

1. A cognitive assessment and training system based on cortical network compensation mechanisms, characterized in that... It includes a cognitive assessment module, an EEG module, a first data processing module, a second data processing module, a cognitive training module, and a transcranial direct current stimulation module. The cognitive assessment module is used to perform cognitive assessments and obtain cognitive assessment data. The EEG module is used to detect the EEG signals corresponding to each question during the cognitive assessment process while performing cognitive assessment, and obtain the EEG signals. The first data processing module receives cognitive assessment data and EEG signals for real-time processing and fusion calculation; The second data processing module is used to provide a cognitive training plan based on the comprehensive ability level value of each brain network and the overall cognitive function level value output by the first data processing module. The cognitive training module is used to perform cognitive training according to the cognitive training plan; the transcranial direct current stimulation module performs electrical stimulation synchronously during the cognitive training. Specifically, if a patient's score on a training task is lower than the group mean of 1.5 SD for that task, it is determined that electrical stimulation by the transcranial direct current stimulation module is required; otherwise, supplementary electrical stimulation by the transcranial direct current stimulation module is not initiated. Furthermore, the location for supplementary electrical stimulation by the transcranial direct current stimulation module is the brain network corresponding to the current task. After the electrical stimulation by the transcranial direct current stimulation module ends, the next training task is initiated until the current cognitive training is completed.

2. The cognitive assessment and training system based on cortical network compensation mechanism as described in claim 1, characterized in that: The cognitive assessment module is used to assess the patient's cognitive abilities. Simultaneously, the EEG module is used to collect the patient's electroencephalogram (EEG) signals during the cognitive assessment. The acquired cognitive assessment data and the monitored EEG signals are sent to the first data processing module.

3. The cognitive assessment and training system based on cortical network compensation mechanism as described in claim 2, characterized in that... The first data processing module processes data in the following ways: The first data processing module amplifies the received EEG signals, extracts the EEG signals corresponding to the cognitive assessment questions, and analyzes them. The first data processing module synchronously analyzes the cognitive assessment results of the current question and displays a non-dynamic graph of the time-frequency curve of the entire cognitive assessment period, synchronized with the aforementioned brain network curve. The first data processing module fuses cognitive assessment data with EEG signals to calculate and output the comprehensive ability level value of each brain network and the overall cognitive function level value.

4. The cognitive assessment and training system based on cortical network compensation mechanism as described in claim 3, characterized in that: The overall ability level value of the brain network is N = Nc + Ne; Wherein, Nc is the cognitive assessment data corresponding to the brain network and the cognitive assessment questions, and Ne is the EEG signal monitoring value corresponding to the brain network.

5. The cognitive assessment and training system based on cortical network compensation mechanism as described in claim 4, characterized in that: The overall cognitive function level is valued as G. G=(N1c+N1e) / n+(N2c+N2e) / n+…+(Nnc+Nne) / n; Where n represents the total number of brain networks, 1, 2...n represent the brain network numbers, N1 is the overall functional level value of the first brain network; N1=N1c+N1e, N1c is the cognitive assessment score of the first brain network, and N1e is the EEG monitoring value of the first brain network.

6. The cognitive assessment and training system based on cortical network compensation mechanism as described in claim 5, characterized in that: The second data processing module sorts the comprehensive ability level values ​​from high to low, and selects the brain networks with the lowest scores for priority training.

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