Head smart wearable neural modulation and sleep monitoring treatment device and method

Abstract

This invention provides a head-mounted intelligent wearable neuromodulation and sleep state regulation device and method based on neural coupling tuning and state transfer induction, including a flexible contact array, a state recognition module, a state transfer induction module, a neural coupling modulation module, a stimulation output module, and a feedback optimization module. The system acquires brain state feature vectors and, based on sleep stages and state regulation goals, divides the head into multiple neural functional node regions. Based on the brain state feature vectors, it dynamically generates node combination relationships, node activation sequences, and node collaborative paths. The state transfer induction module controls multimodal stimulation signals to form rhythmic synchronization, phase coupling, and node collaborative regulation, and outputs the signals to the probes corresponding to the target nodes. Simultaneously, it collects feedback signals in real time and dynamically optimizes the topological relationships between nodes and stimulation parameters to achieve closed-loop transfer regulation of brain states between different sleep stages. This invention can improve the individual adaptability, regulation accuracy, and safety of sleep state regulation.

Description

Technical Field

[0001] This invention relates to the field of brain state regulation and digital health technology, specifically to a smart wearable head-mounted neuromodulation and sleep monitoring treatment device and method. Background Technology

[0002] With the fast pace of life, abnormal brain states such as insomnia, anxiety, and sleep apnea are becoming increasingly common. Existing monitoring and treatment equipment and technologies have significant shortcomings, making it difficult to achieve precise and dynamic guidance of brain state migration.

[0003] (1) Lack of semantic association in node localization: The head stimulation nodes of existing modulation devices are only set based on simple anatomical locations and are not associated with neural functions, psychological functions and sleep stage attributes. They cannot achieve accurate matching of "brain state characteristics - node functions" and have poor modulation targeting.

[0004] (2) Fixed intervention strategy: lack of dynamic point topology generation capability, fixed node combination, activation order and collaborative path, unable to dynamically adapt to the real-time brain state characteristics and sleep stage changes of the subject, making it difficult to guide the brain state to achieve effective transfer.

[0005] (3) Imperfect feedback mechanism: It relies on only a single EEG feedback and does not combine tissue coupling state (impedance, pressure, temperature) for multiple feedback. It cannot accurately assess the node coupling efficiency, resulting in a lack of comprehensiveness in feedback optimization and poor stability of brain state transfer.

[0006] Therefore, there is an urgent need for a monitoring and treatment device with semantic association of neural function, dynamic topology generation, multiple feedback optimization, and highly adaptable neural coupling capability to achieve a closed loop of "state assessment - topology generation - coupling regulation - feedback optimization", accurately guide the migration of brain state between different sleep stages, and improve the regulation effect and safety. Summary of the Invention

[0007] To address the problems of existing neuromodulation technologies lacking neural functional semantic mapping, dynamic node topology, and the difficulty in achieving dynamic brain state transfer, this invention provides a head-mounted intelligent wearable neuromodulation and sleep state regulation method and device based on neural functional semantic mapping and dynamic node topology.

[0008] The first embodiment of the present invention provides a head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method, comprising:

[0009] Acquire sleep and psychological state characteristics of subjects and generate brain state feature vectors;

[0010] Based on the brain state feature vector, select one or more head neural modulation nodes that need to be adapted, and dynamically generate a node topology relationship that includes node combination relationship, node activation order and node collaborative path, wherein each head neural modulation node corresponds to one or more dimensions of the brain state feature vector.

[0011] Based on the node topology relationship and node coupling strength, a state migration induction topology is dynamically constructed, and a control signal containing stimulation control parameters is output through the state migration induction module to the probe corresponding to the head neural modulation node that needs to be adapted, so that the probe outputs multimodal stimulation signals, forming a neural coupling tuning state between nodes and node collaborative regulation.

[0012] The probe collects feedback signals containing brainwave rhythm characteristics and tissue coupling characteristics, and dynamically optimizes the node topology, node coupling strength, and stimulation control parameters based on the feedback signals to guide the brain state to migrate between different sleep stages.

[0013] Further, the step of selecting one or more head neural modulation nodes that need to be adapted based on the brain state feature vector includes:

[0014] Based on the correspondence between the head neural regulation nodes and the brain state feature vector, the regulation value of each head neural regulation node is obtained.

[0015] Based on the preset abnormal ranges of each head neural modulation node and the corresponding modulation values ​​of the head neural modulation nodes, the head neural modulation nodes that need to be adapted are selected; or,

[0016] Based on the degree of deviation from the abnormal preset values ​​of each head neural modulation node, the head neural modulation nodes that need to be adapted are selected.

[0017] Furthermore, each head neural modulation node includes anatomical spatial attributes, neural functional attributes, psychological functional attributes, and sleep stage attributes. The dynamically generated node topology includes node combination relationships, node activation order, and node collaborative paths, including:

[0018] Based on one or more of the neural functional attributes, anatomical spatial attributes, and psychological functional attributes of each head neural modulation node, determine the node combination relationships of the head neural modulation nodes that need to be adapted, including:

[0019] Combine head neural modulation nodes that have the same or similar neural functions and require adaptation.

[0020] Combining head neural modulation nodes that are located in similar positions and require adaptation, or

[0021] Combine head neural modulation nodes that have the same or similar psychological functions and need to be adapted.

[0022] Based on the order of preset sleep stages corresponding to the sleep stage attributes of each head neural modulation node, the activation order of the head neural modulation nodes that need to be adapted is determined.

[0023] Based on the coordination interval where the head neural modulation node that needs to be adapted is located, the node coordination path is determined according to the coordination interval.

[0024] The node topology is constructed based on the node combination relationship, node activation order, and node collaboration path.

[0025] Furthermore, the node coupling strength includes one or more of the following: coupling weight parameter, phase coupling parameter, node association strength parameter, and node synchronization degree parameter, wherein:

[0026] The coupling weight parameter The weights used to describe the synergistic effect between two head neural regulatory nodes, when When, it indicates that head neural regulatory node i and head neural regulatory node j have no synergistic relationship. This indicates that head neural regulatory node i and head neural regulatory node j have the greatest synergistic relationship;

[0027] The phase coupling parameters This is used to describe the phase difference relationship between the output phase angles of two head neural modulation nodes, where, , This indicates that node i outputs the phase angle. The phase angle of node j is represented by the phase coupling parameter. The smaller the phase coupling parameter, the higher the degree of rhythm matching between the two head neural modulation nodes.

[0028] The node association strength parameter Used to describe the degree of functional association between two head neural regulatory nodes, where... , This represents the correlation coefficient between the state changes of two head neural regulatory nodes; the larger the value of the node association strength parameter, the stronger the association.

[0029] The node synchronization parameter This is used to describe the degree of consistency between the node's output rhythm and the target brain state rhythm, where... This indicates the time it takes for the output of the head's neural modulation nodes to match the current sleep stage. This represents the total working time; the larger the node synchronization parameter, the higher the synchronization level.

[0030] The stimulation control parameters include one or more of the following:

[0031] Output frequency parameters are used to determine the frequency range of the probe's output signal;

[0032] Output intensity parameter: used to determine the strength of the output energy;

[0033] Action time parameter: Used to determine the duration of node action;

[0034] Output timing parameters: used to determine the start-up time relationship between multiple nodes or multiple probes;

[0035] Multimodal output mode parameters: used to determine the combination of one or more output modes selected from electromagnetic field, mechanical micro-vibration, and optical modulation.

[0036] Furthermore, the step of outputting a control signal through the state transition induction module to the probe corresponding to the head neural modulation node that needs to be adapted includes:

[0037] Determine the current collaborative interval in the inter-node collaborative path, and select the target head neural modulation node contained in the current collaborative interval;

[0038] Based on the stimulation control parameters, the control signal is output to the probe corresponding to the target head nerve modulation node to control the probe to output a nerve coupling modulation signal, thereby achieving coordinated modulation between nodes;

[0039] While maintaining the continuous effect of the target head neural modulation node corresponding to the current collaborative interval, the collaborative path between the nodes is selected for the target head neural modulation node corresponding to the next collaborative interval, and the state transition induction module outputs the corresponding control signal.

[0040] Furthermore, the step of acquiring feedback signals containing EEG rhythm characteristics and tissue coupling characteristics through the probe, and dynamically optimizing the node topology, node coupling strength, and stimulation control parameters based on the feedback signals, includes:

[0041] The probe collects feedback signals containing brainwave rhythm characteristics and tissue coupling characteristics, and determines the current sleep stage based on the feedback signals.

[0042] Based on the current sleep stage and the sleep attributes of the head neural modulation nodes that need to be adapted, determine whether the sleep attributes of the head neural modulation nodes that need to be adapted include the current sleep stage. If so, reduce the coupling strength of the head neural modulation nodes that need to be adapted and include the current sleep stage.

[0043] Furthermore, when at least one head neural regulation node does not contain the current sleep stage in its sleep attributes, the head neural regulation node that does not contain the current sleep stage is turned off.

[0044] When the sleep attributes of a head neural modulation node do not include the current sleep stage, the coupling strength of the head neural modulation node that does not include the current sleep stage is reduced by a preset ratio, and a shutdown operation is performed when the coupling strength of the head neural modulation node is lower than the preset value.

[0045] Furthermore, the aforementioned head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method also includes:

[0046] The sleep attributes used to determine whether there are head neural regulation nodes that need to be adapted do not include deep sleep and / or REM stages.

[0047] When the sleep attributes of the head neural regulation nodes that need to be adapted do not include deep sleep and / or REM stages, the state transition induction module outputs the corresponding control to the head neural regulation nodes that include deep sleep or REM stages, so as to guide the brain state to migrate to the deep sleep or REM stages.

[0048] Furthermore, when the proportion of alpha waves in the collected EEG rhythm features is not less than 30%, it is determined to be the relaxation stage; when the proportion of delta waves in the collected EEG rhythm features is not less than 40%, it is determined to be the deep sleep stage.

[0049] This invention provides a head-mounted intelligent wearable neuromodulation and sleep monitoring therapy device, comprising a head-mounted wearing end and a host end connected to the head-mounted wearing end, for performing the aforementioned head-mounted intelligent wearable neuromodulation and sleep monitoring therapy method, including:

[0050] The head-mounted device includes a flexible lead and shielding layer that can be worn on the head. The flexible lead and shielding layer may include one or more flexible adaptive modules. Each flexible adaptive module includes one or more flexible contact electrode arrays, and each flexible contact electrode array includes one or more probes. The head-mounted device also includes a stimulation output interface for receiving control signals output from the host device and driving the corresponding probe to output neural coupling modulation signals to perform modulation operations. The stimulation output interface includes one or more of the following: an electromagnetic field output interface, a mechanical vibration output interface, a light modulation output interface, and a temperature regulation output interface.

[0051] The host terminal is used to acquire the sleep and psychological state characteristics of the subject, generate a brain state feature vector, and select one or more head neural modulation nodes that need to be adapted according to the brain state feature vector, dynamically generate a node topology relationship including node combination relationship, node activation order and node collaborative path, wherein each head neural modulation node corresponds to one or more dimensions of the brain state feature vector.

[0052] The host terminal also constructs a state transition induction topology based on the node topology relationship and node coupling strength, and outputs a control signal containing stimulation control parameters to the probe corresponding to the head neural modulation node that needs to be adapted through the state transition induction module, so that the probe outputs multimodal stimulation signals to form node collaborative modulation.

[0053] The host terminal also collects feedback signals containing EEG rhythm characteristics and tissue coupling characteristics through the probe, and dynamically optimizes the node topology, node coupling strength and stimulation control parameters based on the feedback signals to guide the brain state to migrate between different sleep stages.

[0054] Compared with existing technologies, the above-mentioned intelligent wearable neuromodulation and sleep monitoring treatment device and method have the following significant advantages:

[0055] ①Semantic mapping is accurate and highly targeted: Each node in the neural functional semantic node library is associated with multi-dimensional attributes, realizing accurate semantic matching between brain state features and node functions, which solves the problems of no functional association and poor targeting of existing technologies in node localization;

[0056] ② Dynamic topology adaptation and high migration efficiency: The dynamic point topology generation module can adjust the node combination, activation order and collaborative path in real time according to changes in brain state and sleep stage, so as to realize the dynamic adaptation of "brain state - topology structure" and greatly improve the efficiency of brain state migration.

[0057] ③ Strong state migration induction capability: Through the state migration induction module, a mechanism for rhythm matching, phase coordination and synergistic regulation between nodes is formed. The node participation relationship, node coupling strength and stimulation parameters are dynamically adjusted according to different sleep stages to achieve continuous migration guidance of brain state between different sleep stages.

[0058] ④ The closed-loop logic is complete and highly repeatable: It forms a complete closed loop of "state assessment - topology generation - coupling adjustment - feedback optimization". Each link has scientific basis and experimental support, and the adjustment effect is repeatable and verifiable, which is convenient for mass production and clinical application. Attached Figure Description

[0059] Figure 1 This is a flowchart of one embodiment of the head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method of the present invention.

[0060] Figure 2 This is a schematic diagram showing the distribution of 22 head neural regulatory nodes in different locations of the head.

[0061] Figure 3 This is a block diagram of one embodiment of the intelligent wearable neuromodulation and sleep monitoring treatment device for the head of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0063] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0064] Please see Figure 1 The diagram shows a flowchart of one embodiment of the intelligent wearable neuromodulation and sleep monitoring treatment method of the present invention.

[0065] Step S100: Obtain the sleep and psychological state characteristics of the subject and generate a brain state feature vector.

[0066] To provide subjects with targeted sleep monitoring and treatment plans, this implementation method will obtain data characterizing the subjects' sleep and psychological state through questionnaires, sensors, wearable devices, and smart terminals. For example, by initiating an adaptive dynamic question-generating program through a smart terminal, subjects can answer relevant questionnaires (including but not limited to multiple-choice and fill-in-the-blank questions related to sleep rhythm, mood fluctuations, cognitive activity, autonomic nervous system status, and fatigue recovery). Sensors and wearable devices can also acquire information such as sleep duration, sleep intensity, heart rate, and EEG, thereby determining the subjects' sleep and psychological state characteristics based on their answers, sleep duration, sleep intensity, heart rate, and EEG data.

[0067] In this embodiment, brain state feature vector This feature vector characterizes the current brain functional network state of the subject, providing a quantitative basis for subsequent dynamic site topology generation. The brain state feature vector includes sub-features such as sleep rhythm features, mood fluctuation features, cognitive activity features, autonomic nervous system state features, fatigue recovery features, and sleep stability features. Each sub-feature may include one or more dimensions. For example, sleep rhythm features may include dimensions such as sleep onset speed and nighttime rhythm stability; mood fluctuation features may include dimensions such as depressive tendency, rumination, and negative emotions; and sleep stability features may include dimensions such as the number of nighttime awakenings. If T is 22, it means that the dimension of the brain state feature vector can be 22. In other embodiments, the dimension of the brain state feature vector can be set to other values ​​as needed. In one embodiment, when generating the brain state feature vector based on the sleep and psychological state characteristics of the subject, the assessment method of application number [202610465022.8] can be used to assess the subject's state across 22 dimensions across four layers: circadian rhythm, sleep function, cognitive emotion, and physical regulation, and generate the brain state feature vector. In other embodiments, data related to sleep and psychological state characteristics can be aligned and standardized, and the values ​​of each dimension of the brain state feature vector can be obtained based on methods such as setting different weights, machine learning, and large models.

[0068] In other implementations, brain state feature vectors can also be obtained directly from a third party.

[0069] Step S200: Based on the brain state feature vector, select one or more head neural modulation nodes that need to be adapted, and dynamically generate a node topology relationship including node combination relationships, node activation order, and node collaborative paths. Each head neural modulation node includes anatomical spatial attributes, neural functional attributes, psychological functional attributes, and sleep stage attributes. In other embodiments, each head neural modulation node includes anatomical spatial attributes, neural functional attributes, and sleep stage attributes.

[0070] In this embodiment, each head neural modulation node includes anatomical spatial attributes, neural functional attributes, psychological functional attributes, and sleep stage attributes to achieve a multi-dimensional semantic association of "anatomy-neurology-psychology-sleep," wherein:

[0071] Anatomical spatial attributes: Based on the surface projection of neuroanatomy, the anatomical location of each head nerve modulation node on the head surface is clearly defined, ensuring accurate node positioning;

[0072] Neurological functional attributes: Corresponding to the corresponding brain state regulation targets, clarifying the neuromodulation function of the head neural regulation nodes; for example, the head neural regulation nodes in the prefrontal cognitive control area correspond to the brain state regulation targets of cognitive control and time rhythm regulation, the head neural regulation nodes in the temporal lobe cognitive emotion area correspond to the brain state regulation targets of emotion regulation, the head neural regulation nodes in the mid-parietal sleep integration area correspond to the brain state regulation targets of sleep stability, and the head neural regulation nodes in the occipital-cervical regulation area correspond to the brain state regulation targets of autonomic nervous regulation.

[0073] Psychological functional attributes: corresponding to the regulation goals of psychological states such as emotion, cognition, fatigue, and anxiety, to achieve semantic matching between brain state characteristics and node functions;

[0074] Sleep stage attributes: Adapt to different sleep stages and clarify the activation priority and coupling strength of nodes in different sleep stages.

[0075] Please refer to the following: Figure 2 The diagram shows 22 head neural modulation nodes distributed in different locations on the head.

[0076] In one embodiment of the present invention, the number of head neural modulation nodes is preferably set to 22, and the correspondence between each head neural modulation node and its functional semantics is shown in the table below:

[0077]

[0078] For example, node 15 in the prefrontal cognitive control area is associated with cognitive control and sleep rhythm functions, and is adapted to sleep stage attributes including wakefulness → relaxation → light sleep stages; node 9 in the temporal lobe cognitive emotion area is associated with emotion regulation functions, and is adapted to sleep stage attributes including wakefulness → relaxation stages.

[0079] In this embodiment, brain state feature vector This is used to characterize the current brain functional network state of a subject, including sub-features such as sleep rhythm characteristics, mood fluctuation characteristics, cognitive activity characteristics, autonomic nervous system state characteristics, fatigue recovery characteristics, and sleep stability characteristics. Each sub-feature may include one or more dimensions. Based on the neural functional attributes of head neural modulation nodes, these nodes are mapped to the dimensions of brain state feature vectors to achieve precise control of the head neural modulation nodes. Each head neural modulation node can be mapped to one or more dimensions in the brain state feature vector. For example, based on neural functional attributes, head neural modulation nodes can be mapped to dimensions with the same or similar brain state feature vectors, thus making the mapping of head neural modulation nodes and the dimensions of the corresponding brain state feature vectors functionally related. In one embodiment, the mapping relationship between head neural regulatory nodes and brain state feature vectors can be determined experimentally. For example, a preliminary clinical controlled trial involving 120 subjects with insomnia and mood disorders verified the correspondence between brain state features and head neural regulatory nodes. For instance, when mood fluctuation characteristics were abnormal, activation of the temporal lobe cognitive-emotional area node resulted in an 88.3% improvement rate in mood. This led to a mapping between the activation of the temporal lobe cognitive-emotional area node and the mood fluctuation feature dimension in the brain state feature vector. Similarly, when sleep rhythm characteristics were abnormal, activation of the prefrontal cortex cognitive control area and the mid-parietal sleep integration area node resulted in an 89.1% improvement rate in sleep rhythm, a statistically significant difference (p<0.01), confirming the scientific validity and reproducibility of the mapping relationship. This led to a mapping between the prefrontal cortex cognitive control area and the mid-parietal sleep integration area node and the sleep rhythm features in the brain state feature vector. In other embodiments, the mapping relationship between head neural regulatory nodes and brain state feature vectors can be pre-defined.

[0080] In one embodiment, the dimension of sleep onset switching speed under sleep rhythm features in the brain state feature vector can correspond to node 14 of the prefrontal cortex; the dimension of nocturnal rhythm stability under sleep rhythm features in the brain state feature vector can correspond to node 15 of the prefrontal cortex. Furthermore, one dimension of the brain state feature vector can map to one or more head neural regulation nodes. For example, the dimension of the number of nighttime awakenings under sleep stability features in the brain state feature vector can correspond to nodes 20 and 21 of the sleep integration area.

[0081] Step S202: Based on the correspondence between the head neural regulation nodes and the brain state feature vector, obtain the regulation value of each head neural regulation node.

[0082] Since there is a mapping relationship between head neural regulation nodes and brain state feature vectors, the values ​​of each dimension of the brain state feature vector can be determined as the regulation values ​​of the corresponding head neural regulation nodes. In this embodiment, the values ​​of each dimension in the brain state feature vector can be values ​​within 100. In other embodiments, the values ​​of each dimension in the brain state feature vector can also be standardized data within other ranges. When a head neural regulation node is mapped to one dimension of the brain state feature vector, the regulation value of the head neural regulation node is the value of the corresponding dimension. When a head neural regulation node is mapped to two or more dimensions of the brain state feature vector, the regulation value of the head neural regulation node is the weighted average, maximum, minimum, etc., value of the corresponding dimension.

[0083] Step S204: Based on the preset abnormal range of each head nerve modulation node, select the head nerve modulation nodes that need to be adapted.

[0084] In one embodiment, abnormal ranges for each head nerve modulation node can be preset. Head nerve modulation nodes requiring adaptation can be selected based on their modulation values ​​and corresponding abnormal ranges. When the modulation value of a head nerve modulation node falls within its corresponding abnormal range, that node is determined to be the node requiring adjustment. The abnormal range can be either greater than or less than a preset abnormal value. For example, if the abnormal range for a first-point-numbered head nerve modulation node is set as a first abnormal range (e.g., less than the first preset abnormal value), and the abnormal range for a second-point-numbered head nerve modulation node is set as a second abnormal range (e.g., greater than the second preset abnormal value), then if the modulation value of the first-point-numbered head nerve modulation node is within the first abnormal range (i.e., less than the first preset abnormal value), it is determined that the first-point-numbered head nerve modulation node is the node requiring adaptation. If the modulation value of the second-point-numbered head nerve modulation node is not within the second abnormal range (e.g., not greater than the second preset abnormal value), it is determined that the second-point-numbered head nerve modulation node does not require adaptation and can operate according to other control methods.

[0085] In other implementations, head neural modulation nodes that need to be adapted can be selected based on the degree of deviation from the abnormal preset value. For example, if the abnormal preset value of a head neural modulation node is set to 'a', and the adjustment value of the head neural modulation node is 'b', then the degree of deviation from the abnormal preset value can be expressed as |ab| / a. In this way, head neural modulation nodes with a large degree of abnormal deviation can be preferentially selected for adjustment.

[0086] In other embodiments, if the number of head neural modulation nodes that need to be adapted is too large, they can be sorted according to the degree of deviation of each head neural modulation node from the abnormal preset value, and the preset number of head neural modulation nodes at the top of the sort can be selected as the final head neural modulation nodes that need to be adapted.

[0087] Step S206: Based on one or more of the neural functional attributes, anatomical spatial attributes, and psychological functional attributes of each head neural modulation node, determine the node combination relationship of the head neural modulation nodes that need to be adapted.

[0088] In this embodiment, to achieve more targeted adjustments, the head nerve regulation nodes requiring adaptation can be combined based on neural function, anatomical space, and psychological function, allowing for unified adjustment of head nerve regulation nodes located within the same node combination relationship. For example, regarding neural function combination, the neural function attributes of the head nerve regulation nodes requiring adaptation can be obtained, and head nerve regulation nodes with the same or similar neural functions can be combined. Regarding anatomical space combination, the anatomical space attributes of the head nerve regulation nodes requiring adaptation can be obtained, and head nerve regulation nodes located in close proximity can be combined. For example, the head can be divided into multiple regions, and head nerve regulation nodes located within the same region can be grouped as a single node combination.

[0089] In this embodiment, sleep stages include wakefulness, relaxation, light sleep, deep sleep, and rapid eye movement (REM) sleep; brain state regulation goals include autonomic nervous system regulation, physical recovery, emotion regulation, cognitive emotion, cognitive control, circadian rhythm, sleep stability, and brain network integration, serving as a framework for brain functional networks. This embodiment divides the head into four regions based on different sleep stages and brain state regulation goals. The specific regions and their corresponding sleep stages and brain state regulation goals are as follows:

[0090] ①Occipital-Neck Adjustment Area: Associated with brain function networks related to autonomic nervous system regulation and physical recovery (corresponding to neurological function attributes), mainly adapted to the deep sleep stage (corresponding to sleep stage attributes), responsible for autonomic nervous system homeostasis regulation and fatigue recovery (corresponding to psychological function attributes);

[0091] ② Temporal lobe cognitive-emotion area: associated with brain functional networks related to emotion regulation and cognition (corresponding to neural functional attributes), mainly adapted to the wakefulness and relaxation stages (corresponding to sleep stage attributes), responsible for emotion calming and cognitive regulation (corresponding to psychological functional attributes);

[0092] ③ Prefrontal cortex: It is associated with cognitive control and circadian rhythm-related brain function networks (corresponding to neural function attributes), mainly adapting to the waking and light sleep stages (corresponding to sleep stage attributes), and is responsible for cognitive control and sleep rhythm guidance (corresponding to psychological function attributes).

[0093] ④ Mid-apical sleep integration area: associated with sleep stability and brain network integration related functional networks (corresponding to neurological functional attributes), mainly adapted to light sleep, deep sleep, and REM stages (corresponding to sleep stage attributes), responsible for sleep stability and brain network coordination (corresponding to psychological functional attributes).

[0094] The occipitocervical region contains head nerve regulation nodes 1-2, the temporal lobe cognitive-emotional region contains head nerve regulation nodes 3-12, the prefrontal cognitive control region contains head nerve regulation nodes 13-17, and the mid-parietal sleep integration region contains head nerve regulation nodes 18-22. Regarding the combination of psychological functions, the psychological function attributes of the head nerve regulation nodes requiring adaptation can be obtained, and head nerve regulation nodes with the same or similar psychological functions that require adaptation can be combined.

[0095] Step S208: Based on the sleep stage attributes of the head neural regulation nodes, determine the node activation order of the head neural regulation nodes that need to be adapted.

[0096] In this embodiment, the sleep stage attributes include wakefulness, relaxation, light sleep, deep sleep, and REM sleep. Therefore, the head neural modulation nodes that need to be adapted can be selected sequentially according to the preset sleep stage order. For example, the head neural modulation nodes that need to be adapted and whose sleep stage attributes include the wakefulness stage are activated first, followed by nodes whose sleep stage attributes include the relaxation stage, and finally nodes whose sleep stage attributes include the REM sleep stage. If there are two or more nodes in the same sleep stage, they can be activated according to the priority of the relevant nodes, that is, in the same sleep stage, nodes with higher priority are activated first.

[0097] In other implementations, activation can be performed sequentially based on the numbering of the head neural regulatory nodes that need to be adapted. For example, taking node 4 (temporal lobe cognitive cycle regulation node), node 10 (temporal lobe emotional homeostasis node), node 15 (frontoparietal network synchronization node), node 20 (mid-parietal sleep stabilization node), and node 21 (mid-parietal dream processing node) as examples, the node activation order can be represented as: 4→10→15→20→21, to indicate the order of node activation.

[0098] Step S210: Based on the synergistic interval where the head neural modulation node to be adapted is located, according to the node synergistic path determined by the synergistic interval, a node topology relationship including node combination relationship, node activation order and node synergistic path is dynamically generated. The synergistic interval includes fatigue recovery, mood improvement, rhythm guidance and sleep stabilization.

[0099] In this embodiment, the synergistic intervals may include four intervals: fatigue recovery, mood improvement, rhythm guidance, and sleep stability, thus forming a synergistic path of "fatigue recovery—mood improvement—rhythm guidance—sleep stability." Since each head neural regulation node contains neural function attributes, psychological function attributes, and sleep stage attributes, the synergistic intervals corresponding to the head neural regulation nodes to be adapted can be determined based on the neural function attributes, psychological function attributes, and sleep stage attributes contained in the adapted head neural regulation nodes, thereby achieving the purpose of constructing node topological relationships. In other embodiments, corresponding synergistic intervals can be set according to the regions divided in the head, such as the occipitocervical regulation region corresponding to the fatigue recovery synergistic interval, the temporal lobe cognitive-emotional region corresponding to the mood improvement synergistic interval, the prefrontal cognitive control region corresponding to the rhythm guidance synergistic interval, and the mid-parietal sleep integration region corresponding to the sleep stability synergistic interval.

[0100] In this embodiment, when there are multiple head neural modulation nodes that need to be adapted within the same coordination interval, the activation order is determined according to the priority of each head neural modulation node within the coordination interval. In other embodiments, the activation order can be determined according to the sequence number of each head neural modulation node within the coordination interval.

[0101] For example, by collecting data from 39-year-old female subjects, a multidimensional brain state feature vector was generated. After the above steps S202 and S204, it was determined that the sleep onset switching speed (25 points) and nighttime rhythm stability (28 points) dimensions under the sleep rhythm feature vector were within the abnormal range, corresponding to nodes 14 and 15 of the prefrontal cognitive control area, respectively; the depressive tendency (24 points) and rumination (26 points) dimensions under the emotion fluctuation feature were within the abnormal range, corresponding to nodes 4 and 10 of the temporal lobe cognitive emotion area, respectively; and the number of nighttime awakenings (23 points) dimension under the sleep stability feature was within the abnormal range, corresponding to nodes 20 and 21 of the mid-parietal sleep integration area.

[0102] According to step S206, from the perspective of anatomical space combination, No. 4 and No. 10 both belong to the temporal lobe cognitive emotion area and can be combined as (4, 10); No. 14 and No. 15 both belong to the prefrontal cognitive control area and can be combined as (14, 15); No. 20 and No. 21 both belong to the mid-parietal sleep integration area and can be combined as (20, 21).

[0103] According to step S208, if the sleep attributes of devices 4 and 10 include the wakefulness → relaxation → light sleep stage, the sleep attributes of devices 14 and 15 include the wakefulness → relaxation → light sleep → deep sleep stage, and the sleep attributes of devices 20 and 21 include the deep sleep → REM stage, then the node activation order can be 4 → 10 → 14 → 15 → 20 → 21.

[0104] Since nodes 4 and 10 are located in the temporal lobe cognitive-emotion area, corresponding to the synergistic interval for emotion improvement; nodes 14 and 15 are located in the prefrontal cognitive control area, corresponding to the synergistic interval for rhythm guidance; and nodes 20 and 21 are located in the mid-parietal sleep integration area, corresponding to the synergistic interval for sleep stability, their node synergistic path is "emotion improvement—rhythm guidance—sleep stability". Therefore, based on the node combination relationship, node activation order, and node synergistic path, the node topology relationship is dynamically generated as (4→10)→(14→15)→(20→21). In other implementations, nodes within the same combination relationship can be activated simultaneously as (4+10)→(14+15)→(20+21).

[0105] Step S300: Based on the node collaboration path and node coupling strength, a state migration induction topology is dynamically constructed. The state migration induction module outputs a control signal to the probe corresponding to the head neural modulation node that needs to be adapted, so that the probe outputs multimodal stimulation signals to form a neural coupling tuning state between nodes and node collaborative modulation.

[0106] Please refer to the following: Figure 3 The diagram shows a structural block diagram of a monitoring and treatment device according to one embodiment of the present invention. The monitoring and treatment device includes a head-mounted end and a main unit. The main unit is connected to the head-mounted end via a flexible cable interface (such as a magnetic or locking mechanism). Thus, the main unit inputs control signals through the flexible cable interface and can receive feedback signals transmitted from the head-mounted end. In other embodiments, the main unit can transmit data wirelessly to the head-mounted end.

[0107] The head-mounted device includes a flexible lead and shielding layer that can be worn on the head. The flexible lead and shielding layer may include one or more flexible adaptive modules, and each flexible adaptive module may include one or more flexible contact electrode arrays. In this embodiment, the flexible lead and shielding layer may include four flexible adaptive modules, corresponding to the frontal region, temporal lobe region, occipital-parietal region, and vertex region, respectively. Each flexible adaptive module can be independently bent and fitted, and the contact pressure (e.g., 5~15kPa) can be dynamically adjusted according to the tissue coupling state to adapt to different head shapes and wearing postures, ensuring good coupling between each probe and the scalp. The flexible contact electrode array may include 22 points, corresponding to 22 head neural modulation nodes. Each flexible contact electrode array includes one or more probes. For example, the mid-parietal dream processing node 21 may include four probes; the frontal sleep cognition node 13 may include two probes, located at relevant locations in the left and right hemispheres, respectively. In this embodiment, each probe is used to form a local neural coupling interface with the scalp. This neural coupling interface is constructed using a material with skin-like electrical properties, which can simulate the electrical properties of human skin, creating a low-intensity bioelectrical coupling environment with the scalp, improving coupling stability and comfort, and adapting to different scalp conditions. After receiving a control signal, the probe further includes at least one of the following neural modulation output modes to select single-modal or multi-modal collaborative output (i.e., multi-modal stimulation signals):

[0108] ① Weak electromagnetic field output: including low-frequency rhythm modulation signal and carrier control signal, wherein the low-frequency rhythm modulation frequency is 0.5–40Hz, preferably 4–13Hz; the carrier or drive control frequency is 100–500Hz; and the magnetic field strength is 0.1–30mT, used to form a non-invasive neural coupling regulation environment to guide the brain rhythm to migrate to the target sleep stage related state.

[0109] ② Optical modulation output: The signal is modulated using red or near-infrared light, with a preferred wavelength range of 650nm and a preferred output power of 150mW. This is used to regulate local neural activity and assist in brainwave rhythm synchronization and brain state migration.

[0110] ③ Mechanical micro-vibration output: The mechanical micro-vibration signal is rhythmically modulated, with a preferred modulation frequency of 0.5Hz and a preferred amplitude of 0.5mm, to form a non-invasive mechanical rhythm coupling environment to assist in rhythm synchronization between nodes, state transition induction, and neural coupling regulation.

[0111] ④ Thermoregulation output: By increasing or decreasing the contact temperature of the scalp, the dilation of subcutaneous blood vessels is altered, thereby inducing a relaxed state and thermoregulation to accelerate sleep onset and / or improve sleep quality.

[0112] Each probe also includes an electroencephalogram (EEG) feedback unit, a scalp contact impedance feedback unit, and a temperature feedback unit, which are used to transmit relevant feedback signals to the host computer.

[0113] The host unit includes a multimodal signal acquisition unit, a signal processing unit, a feature extraction and state recognition unit, a neural coupling modulation unit, a brain state resonance induction module (state transition induction module), and a stimulation output unit. In this embodiment, the multimodal signal acquisition unit receives feedback signals transmitted from the electroencephalogram (EEG) feedback unit, the scalp contact impedance feedback unit, and the temperature feedback unit. The signal processing unit amplifies, filters, and performs anti-artifact operations on the feedback signals. Subsequently, the feature extraction and state recognition unit extracts features to update the relevant dimension values ​​in the brain state feature vector, so that the state transition induction module can output a new control signal through the stimulation output unit based on the updated brain state feature vector.

[0114] Step S302: Construct a state transition induced topology based on the node cooperative path and node coupling strength.

[0115] In this embodiment, the node coupling strength is used to describe the degree of cooperation between nodes and reflects the magnitude of the interaction weight between nodes. It includes one or more of the following: coupling weight parameter, phase coupling parameter, node association strength parameter, and node synchronization degree parameter, wherein:

[0116] The coupling weight parameter The weights used to describe the synergistic effect between two head neural regulatory nodes, ranging from [0,1], when When, it indicates that nodes i and j have no cooperative relationship. This indicates that nodes i and j have the greatest synergy, which can be achieved by scoring each node and calculating its Pearson correlation coefficient. For example, nodes 4 and 10 (the synergy interval for mood improvement): =0.85, indicating that nodes 4 and 10 have a strong synergistic effect. In other embodiments, step S206 can determine the node combination relationship of the head neural modulation nodes that need to be adapted based on the coupling weight parameter. For example, when the coupling weight parameter of any two head neural modulation nodes exceeds a preset threshold, it is determined that there is a node combination relationship between the two.

[0117] The phase coupling parameters This describes the phase difference relationship between the output phase angles of two head neural modulation nodes. The phase angle is calculated by applying a small-amplitude AC detection signal to the tissue coupling interface formed by the probe and the scalp, and then calculating the phase difference between the voltage and current signals. Since the probe, scalp, subcutaneous tissue, and local biological tissue can be equivalently represented as a complex impedance network containing resistance (R) and capacitance (C) components, it can be calculated using a complex impedance model. The impedance can be expressed as: Z = R + jX, where R represents the resistance component and X represents the reactance component. The phase angle φ of the impedance can then be expressed as: φ = arctan(X / R). When the impedance phase angle is close to 0°, it indicates good impedance matching between the probe and the tissue, and a relatively stable coupling state. When the impedance phase angle deviates significantly from 0°, it may indicate abnormal probe contact, changes in the tissue interface, or a decrease in local coupling efficiency.

[0118] , This indicates that node i outputs the phase angle. This indicates the output phase angle of node j. The smaller the phase coupling parameter, the higher the rhythm matching degree between the two head neural modulation nodes. For example, node 4 outputs a phase of 0°, and node 10 outputs a phase of 3°. .

[0119] The node association strength parameter Used to describe the degree of functional association between two head neural regulatory nodes, ranging from [-1, 1], where... , This represents the correlation coefficient between the state changes of two head neural regulatory nodes; the larger the value of the node association strength parameter, the stronger the association; for example: node 15 (rhythm guidance) and node 20 (sleep stability): =0.82, indicating that the two nodes are highly functionally related.

[0120] The node synchronization parameter This is used to describe the degree of consistency between the node's output rhythm and the target brain state rhythm, where... This indicates the time it takes for the output of the head's neural modulation nodes to match the current sleep stage. This represents the total working time. A higher node synchronization level parameter indicates a higher degree of synchronization. For example, node 20 in deep sleep: =0.91 indicates that the node output rhythm is highly consistent with the rhythm of the deep sleep brain stage.

[0121] Step S304: Determine the current collaborative interval in the inter-node collaborative path, and select the target head neural modulation node corresponding to the current collaborative interval.

[0122] In this embodiment, the inter-node coordination path includes one or more coordination intervals. For example, in conjunction with the aforementioned embodiments, in the coordination path of "mood improvement - rhythm guidance - sleep stabilization," since an initial adjustment operation is required, the current coordination interval is "mood improvement," and the head neural regulation nodes included in the "mood improvement" coordination interval are nodes 4 and 10. Therefore, nodes 4 and 10 can be used as target head neural regulation nodes.

[0123] Step S306: Based on the state transition induced topology, a control signal containing stimulation control parameters is output to the probe corresponding to the head neural modulation node that needs to be adapted. The stimulation control parameters include at least one of the following: output frequency parameter, output intensity parameter, action time parameter, output timing parameter, and multimodal output mode parameter.

[0124] In this embodiment, the state transition induction module outputs a control signal containing stimulation control parameters to the probe corresponding to the head neural modulation node that needs to be adapted, based on the state transition induction topology. This enables the probe to output multimodal stimulation signals, forming node-based coordinated modulation. The stimulation control parameters include at least one of the following:

[0125] Output frequency parameter: used to determine the frequency range of the probe's output signal, such as 300Hz, 380Hz, etc.

[0126] Output strength parameters: used to determine the strength of the output energy, such as magnetic field strength, vibration amplitude, etc.

[0127] Action time parameter: used to determine the duration of action of the node, such as 5 min, 10 min, etc.;

[0128] Output timing parameters: used to determine the start-up time relationship between multiple nodes or multiple probes;

[0129] Multimodal output mode parameters: used to determine the combination of output methods such as electromagnetic field, mechanical micro-vibration, and optical modulation.

[0130] In this embodiment, when the subject wears the device, the neural coupling modulation control unit can automatically calibrate the contact pressure between the probe and the scalp, such as a contact pressure of 8 kPa (moderate intensity). When controlling the corresponding target head neural modulation node to perform the corresponding modulation operation, the state transition induction module can output a control signal containing stimulation control parameters. For example, the stimulation control parameters are as follows: No. 4 (380Hz, 0.065T, 2.9mW, 0.20mm), No. 10 (360Hz, 0.062T, 2.8mW, 0.20mm), No. 14 (320Hz, 0.055T, 2.9mW, 0.21mm), No. 15 (350Hz, 0.060T, 2.7mW, 0.19mm), No. 20 (400Hz, 0.068T, 2.8mW, 0.18mm), and No. 21 (390Hz, 0.066T, 2.8mW, 0.19mm).

[0131] The neural coupling modulation unit is used to perform dynamic weight allocation and timing control. The timing control is used to control the working duration of the target head neural modulation node and the time interval between different target head neural modulation nodes. The dynamic weight allocation is used to control the weight between the relevant stimulus control parameters when the target head neural modulation node is working, so as to increase or decrease the working intensity of the relevant target head neural modulation node. After determining the relevant adjustment parameters, the neural coupling modulation unit outputs the corresponding control signal by the state transition induction module.

[0132] During the synergistic range of "mood improvement," the state transition induction module sets the weak magnetic field parameters of node 4 to 380Hz, 0.065T, the light modulation parameter to 2.9mW, and the vibration parameter to 0.21mm. Simultaneously, it sets the relevant parameters for node 10 (weak magnetic field 360Hz, 0.062T, light modulation 2.8mW, vibration 0.20mm) to activate nodes 4 and 10. Other head neural modulation nodes (excluding those requiring adaptation, such as nodes 4, 10, 14, 15, 20, and 21) can remain in low-intensity standby mode (relevant parameters: weak magnetic field 120Hz, 0.01T). The neural coupling control unit will control nodes 14 and 15 to reactivate after 4 minutes. At this time, the state transition induction module receives these adjustment parameters and outputs corresponding control signals to nodes 14 and 15 after 4 minutes.

[0133] Step S308: Determine whether there is a next collaborative interval in the inter-node collaborative path. If there is a next collaborative interval, take the next collaborative interval as the current collaborative interval and return to step S302 for execution; if there is no next collaborative interval, execute the subsequent step S400.

[0134] In this embodiment, the state transition induction topology includes one or more head neural modulation nodes to be activated within a coordination interval and their activation time information. Therefore, the state transition induction module can sequentially activate head neural modulation nodes within different coordination intervals according to the activation time information. Simultaneously, while maintaining the continuous operation of the target head neural modulation node corresponding to the current coordination interval, the state transition induction module outputs control signals to the target head neural modulation node corresponding to the next coordination interval based on the coordination path between nodes.

[0135] For example, the activation sequence includes: first activating nodes 4 and 10 (corresponding to the mood improvement coordination interval), then activating nodes 14 and 15 (corresponding to the rhythm guidance coordination interval) after 5 minutes, and finally activating nodes 20 and 21 (corresponding to the sleep stability coordination interval) after 10 minutes. After the state transition induction module outputs the control signals corresponding to nodes 4 and 10, the neural coupling regulation unit outputs adjustment parameters to the neural coupling regulation unit, which is required to activate nodes 14 and 15 after 5 minutes. At this time, nodes 4 and 10 are still in the working state. Therefore, the rhythm guidance coordination interval corresponding to nodes 14 and 15 can be obtained, and the process returns to step S402 to continue execution. After nodes 14 and 15 are activated, the neural coupling regulation unit outputs adjustment parameters to the neural coupling regulation unit, which is required to activate nodes 20 and 21—the sleep stability coordination interval—after 10 minutes, and the process returns to step S402 to continue execution. Once nodes 20 and 21 are activated, nodes 4, 10, 14, 15, 20, and 21 are all in working condition. Since there is no next coordination interval, it means that the activation of the head neural regulation nodes in the activation sequence has been completed. In this way, subsequent feedback regulation operations can be performed. Nodes 4, 10, 14, 15, 20, and 21 output neural coupling regulation signals, forming a neural coupling tuning state between nodes, and achieving the purpose of node coordinated regulation.

[0136] Step S400: The probe collects feedback signals containing EEG rhythm characteristics and tissue coupling characteristics, and dynamically optimizes the node topology, node coupling strength and stimulation control parameters based on the feedback signals to guide the brain state to migrate between different sleep stages.

[0137] Step S402: The probe collects feedback signals containing brainwave rhythm characteristics and tissue coupling characteristics, and determines the current sleep stage based on the feedback signals.

[0138] In this embodiment, the host device collects feedback signals containing EEG rhythm characteristics and tissue coupling characteristics via probes. The EEG rhythm characteristics are obtained by acquiring the proportions of α, β, θ, and δ waves through EEG to monitor brain state. For example, in a waking state, β wave activity is dominant (i.e., the largest proportion) in the EEG, indicating active sympathetic nerves; in the relaxation stage, α wave activity increases, β waves decrease, indicating relaxation and reduced alertness; in the light sleep stage, θ waves appear, α waves dominate, sensation is weakened, and it is easy to enter a sleep preparation state; in the deep sleep stage, δ waves are dominant, and growth hormone secretion increases; in the REM stage, θ waves increase, low-frequency fast waves are mixed, and dream activity is active. Therefore, the host device can determine the subject's current sleep stage by analyzing the characteristics of α, β, θ, and δ waves in different sleep stages within the EEG rhythm characteristics. For example, an α wave proportion ≥30% indicates a relaxation stage, and a δ wave proportion ≥40% indicates a deep sleep stage.

[0139] The multimodal signal acquisition unit on the host side acquires at least one of the impedance, pressure, or temperature characteristics of the contact area through the probe. The signal processing unit, feature extraction and state recognition unit, and state transition induction module evaluate the coupling effect between the probe and the scalp (e.g., a good coupling is determined when the impedance phase angle is close to 0°), providing a basis for topology optimization. In other embodiments, if the absolute value of the rate of change of the impedance phase angle is detected to be greater than a threshold (e.g., 5 degrees per second) within a short period (e.g., within 1 second), it can be determined that the probe may have experienced momentary physical detachment or severe displacement. Accordingly, the system triggers a contact anomaly handling procedure, adjusting the contact pressure, pausing the corresponding probe output, or prompting for re-wearing to re-establish a stable tissue coupling state.

[0140] Step S404: Based on the current sleep stage and the sleep attributes of the target head neural modulation node to be adapted, determine whether the sleep attributes of the target head neural modulation node include the current sleep stage. If yes, reduce the coupling strength of the target head neural modulation node that includes the current sleep stage; otherwise, close the target head neural modulation node that does not include the current sleep stage.

[0141] For example, the sleep attributes of No. 4 include the stages of wakefulness → relaxation → light sleep; the sleep attributes of No. 14 include the stages of wakefulness → relaxation → light sleep → deep sleep; and the sleep attributes of No. 20 include the stages of deep sleep → REM sleep.

[0142] At the 18th minute of intervention, the proportion of alpha waves in the brain was 31%, indicating that the brain was in a relaxation phase. Since both No. 4 and No. 14 included the relaxation phase, the coupling strength of No. 4 and No. 14 could be reduced, such as by reducing the proportion of stimulation control parameters to 35%.

[0143] At the 33rd minute of intervention, the proportion of delta waves in the brain was 42%, indicating that the patient was in a deep sleep stage. Since No. 4 does not include the deep sleep stage, No. 4 can be turned off. No. 14 and No. 20 include the deep sleep stage, so the coupling strength of No. 14 and No. 20 can be reduced. For example, the coupling strength of No. 20 can be reduced by 55% (i.e., the value of the relevant stimulus control parameter is adjusted), and the coupling strength of No. 14 can be reduced by 20%.

[0144] Two hours after the intervention, the REM phase begins. At this point, number 14 is turned off, while number 20 is maintained to reduce dream interference.

[0145] In other implementations, when the sleep attributes of the target head neural modulation node (or the head neural modulation node to be adapted) do not include the current sleep stage, the coupling strength of the corresponding head neural modulation node can be reduced by a preset ratio, and a shutdown operation can be performed when the coupling strength of the head neural modulation node is lower than the preset coupling strength. For example, if the current sleep stage is deep sleep, the sleep attributes of node 4 do not include deep sleep, so the coupling strength of node 4 can be reduced by 50% during deep sleep; if the current sleep stage is REM sleep, the sleep attributes of node 4 do not include REM sleep, so the coupling strength of node 4 can be further reduced by 80% during REM sleep. After continuously reducing by 50% and 80%, the coupling strength of node 4 may be lower than the preset coupling strength. At this time, node 4 can be shut down during REM sleep to achieve the purpose of dynamically optimizing the node topology relationship.

[0146] In other embodiments, it can also be determined whether at least one target head neural modulation node has a sleep attribute that includes deep sleep and / or REM sleep stages. When the sleep attributes of all target head neural modulation nodes do not include deep sleep and / or REM sleep stages, the operation of the head neural modulation nodes that do include deep sleep and / or REM sleep stages is automatically controlled. Since one of the purposes of the system is to improve the sleep quality of the subject and to induce deep sleep and / or REM sleep stages as much as possible, the operation of the probes corresponding to the relevant head neural modulation nodes can still be controlled even if the sleep attributes of the target head neural modulation nodes do not include deep sleep or REM sleep stages. For example, in the above case, the target head neural modulation nodes only include node 4 and do not include nodes 14 and 20. When the subject enters the light sleep stage, nodes 14 and 20 can still be controlled to operate so that the subject can enter the deep sleep and / or REM sleep stages, realizing the transfer of brain state between different sleep stages.

[0147] In other implementations, based on the brain state migration trend (e.g., from wakefulness → relaxation → light sleep → deep sleep → REM sleep stage), the following parameters are dynamically adjusted to achieve closed-loop optimization: node activation order, node combination relationship, node coupling strength, and node action time; for example, when a slow brain state migration speed is detected, the coupling strength of the target node is increased and the action time is extended; when poor tissue coupling is detected, the contact pressure of the corresponding node is adjusted and the node combination relationship is optimized.

[0148] Compared with the prior art, the present invention has the following significant advantages:

[0149] ①Semantic mapping is accurate and highly targeted: Each node in the neural functional semantic node library is associated with multi-dimensional attributes, realizing accurate semantic matching between brain state features and node functions, which solves the problems of no functional association and poor targeting of existing technologies in node localization;

[0150] ② Dynamic topology adaptation and high migration efficiency: The dynamic point topology generation module can adjust the node combination, activation order and collaborative path in real time according to changes in brain state and sleep stage, so as to realize the dynamic adaptation of "brain state - topology structure" and greatly improve the efficiency of brain state migration.

[0151] ③ The state transition induction mechanism is clearly defined: the state transition induction module forms a rhythm synchronization, phase coupling and collaborative regulation mechanism between nodes, enabling the system to continuously guide brain state transition according to changes in sleep stages;

[0152] ④ The closed-loop logic is complete and highly repeatable: It forms a complete closed loop of "state assessment - topology generation - coupling adjustment - feedback optimization". Each link has scientific basis and experimental support, and the adjustment effect is repeatable and verifiable, which is convenient for mass production and clinical application.

Claims

1. A head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method, characterized in that, The monitoring and treatment methods include: Acquire sleep and psychological state characteristics of subjects and generate brain state feature vectors; Based on the brain state feature vector, select one or more head neural modulation nodes that need to be adapted, and dynamically generate a node topology relationship that includes node combination relationship, node activation order and node collaborative path, wherein each head neural modulation node corresponds to one or more dimensions of the brain state feature vector. Based on the node topology relationship and node coupling strength, a state migration induction topology is dynamically constructed, and a control signal containing stimulation control parameters is output through the state migration induction module to the probe corresponding to the head neural modulation node that needs to be adapted, so that the probe outputs multimodal stimulation signals, forming a neural coupling tuning state between nodes and node collaborative regulation. The probe collects feedback signals containing brainwave rhythm characteristics and tissue coupling characteristics, and dynamically optimizes the node topology, node coupling strength, and stimulation control parameters based on the feedback signals to guide the brain state to migrate between different sleep stages.

2. The head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method as described in claim 1, characterized in that, The step of selecting one or more head neural modulation nodes to be adapted based on the brain state feature vector includes: Based on the correspondence between the head neural regulation nodes and the brain state feature vector, the regulation value of each head neural regulation node is obtained. Based on the preset abnormal ranges of each head neural modulation node and the corresponding modulation values ​​of the head neural modulation nodes, the head neural modulation nodes that need to be adapted are selected; or, Based on the degree of deviation from the abnormal preset values ​​of each head neural modulation node, the head neural modulation nodes that need to be adapted are selected.

3. The head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method as described in claim 1, characterized in that, Each head neural modulation node includes anatomical spatial attributes, neural functional attributes, psychological functional attributes, and sleep stage attributes. The dynamically generated node topology includes node combination relationships, node activation order, and node collaborative paths, including: Based on one or more of the neural functional attributes, anatomical spatial attributes, and psychological functional attributes of each head neural modulation node, determine the node combination relationships of the head neural modulation nodes that need to be adapted, including: Combine head neural modulation nodes that have the same or similar neural functions and require adaptation. Combining head neural modulation nodes that are located in similar positions and require adaptation, or Combine head neural modulation nodes that have the same or similar psychological functions and need to be adapted. Based on the order of preset sleep stages corresponding to the sleep stage attributes of each head neural modulation node, the activation order of the head neural modulation nodes that need to be adapted is determined. Based on the coordination interval where the head neural modulation node that needs to be adapted is located, the node coordination path is determined according to the coordination interval; The node topology is constructed based on the node combination relationship, node activation order, and node collaboration path.

4. The head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method as described in claim 1, characterized in that, The node coupling strength includes one or more of the following: coupling weight parameter, phase coupling parameter, node association strength parameter, and node synchronization degree parameter, wherein: The coupling weight parameter The weights used to describe the synergistic effect between two head neural regulatory nodes, when When, it indicates that head neural regulatory node i and head neural regulatory node j have no synergistic relationship. This indicates that head neural regulatory node i and head neural regulatory node j have the greatest synergistic relationship; The phase coupling parameters This is used to describe the phase difference relationship between the output phase angles of two head neural modulation nodes, where, , This indicates that node i outputs the phase angle. The phase angle of node j is represented by the phase coupling parameter. The smaller the phase coupling parameter, the higher the degree of rhythm matching between the two head neural modulation nodes. The node association strength parameter Used to describe the degree of functional association between two head neural regulatory nodes, where... , This represents the correlation coefficient between the state changes of two head neural regulatory nodes; the larger the value of the node association strength parameter, the stronger the association. The node synchronization parameter This is used to describe the degree of consistency between the node's output rhythm and the target brain state rhythm, where... This indicates the time it takes for the output of the head's neural modulation nodes to match the current sleep stage. This represents the total working time; the larger the node synchronization parameter, the higher the synchronization level. The stimulation control parameters include one or more of the following: Output frequency parameters are used to determine the frequency range of the probe's output signal; Output intensity parameter: used to determine the strength of the output energy; Action time parameter: Used to determine the duration of node action; Output timing parameters: used to determine the start-up time relationship between multiple nodes or multiple probes; Multimodal output mode parameters: used to determine the combination of one or more output modes selected from electromagnetic field, mechanical micro-vibration, and optical modulation.

5. The head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method as described in claim 1, characterized in that, The step of outputting a control signal through the state transition induction module to the probe corresponding to the head neural modulation node that needs to be adapted includes: Determine the current collaborative interval in the inter-node collaborative path, and select the target head neural modulation node contained in the current collaborative interval; Based on the stimulation control parameters, the control signal is output to the probe corresponding to the target head nerve modulation node to control the probe to output a nerve coupling modulation signal, thereby achieving coordinated modulation between nodes; While maintaining the continuous effect of the target head neural modulation node corresponding to the current collaborative interval, the collaborative path between the nodes is selected for the target head neural modulation node corresponding to the next collaborative interval, and the state transition induction module outputs the corresponding control signal.

6. The head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method as described in claim 1, characterized in that, The process of acquiring feedback signals containing EEG rhythm characteristics and tissue coupling characteristics through the probe, and dynamically optimizing the node topology, node coupling strength, and stimulation control parameters based on the feedback signals, includes: The probe collects feedback signals containing brainwave rhythm characteristics and tissue coupling characteristics, and determines the current sleep stage based on the feedback signals. Based on the current sleep stage and the sleep attributes of the head neural modulation nodes that need to be adapted, determine whether the sleep attributes of the head neural modulation nodes that need to be adapted include the current sleep stage. If so, reduce the coupling strength of the head neural modulation nodes that need to be adapted and include the current sleep stage.

7. The head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method as described in claim 6, characterized in that, When at least one head neural regulation node does not contain the current sleep stage in its sleep attributes, the head neural regulation node that does not contain the current sleep stage is turned off. When the sleep attributes of a head neural modulation node do not include the current sleep stage, the coupling strength of the head neural modulation node that does not include the current sleep stage is reduced by a preset ratio, and a shutdown operation is performed when the coupling strength of the head neural modulation node is lower than the preset value.

8. The head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method as described in claim 6, characterized in that, Also includes: The sleep attributes used to determine whether there are head neural regulation nodes that need to be adapted do not include deep sleep and / or REM stages. When the sleep attributes of the head neural regulation nodes that need to be adapted do not include deep sleep and / or REM stages, the state transition induction module outputs the corresponding control to the head neural regulation nodes that include deep sleep or REM stages, so as to guide the brain state to migrate to the deep sleep or REM stages.

9. The head-mounted intelligent wearable neuromodulation and sleep monitoring treatment method as described in claim 6, characterized in that, When the proportion of alpha waves in the collected EEG rhythm features is not less than 30%, it is determined to be the relaxation stage; when the proportion of delta waves in the collected EEG rhythm features is not less than 40%, it is determined to be the deep sleep stage.

10. A head-mounted intelligent wearable neuromodulation and sleep monitoring therapy device, comprising a head-mounted wearing end and a host end connected to the head-mounted wearing end, for performing the head-mounted intelligent wearable neuromodulation and sleep monitoring therapy method as described in any one of claims 1-9, characterized in that: The head-mounted device includes a flexible lead and shielding layer that can be worn on the head. The flexible lead and shielding layer may include one or more flexible adaptive modules. Each flexible adaptive module includes one or more flexible contact electrode arrays, and each flexible contact electrode array includes one or more probes. The head-mounted device also includes a stimulation output interface for receiving control signals output from the host device and driving the corresponding probe to output neural coupling modulation signals to perform modulation operations. The stimulation output interface includes one or more of the following: an electromagnetic field output interface, a mechanical vibration output interface, a light modulation output interface, and a temperature regulation output interface. The host terminal is used to acquire the sleep and psychological state characteristics of the subject, generate a brain state feature vector, and select one or more head neural modulation nodes that need to be adapted according to the brain state feature vector, dynamically generate a node topology relationship including node combination relationship, node activation order and node collaborative path, wherein each head neural modulation node corresponds to one or more dimensions of the brain state feature vector. The host terminal also constructs a state transition induction topology based on the node topology relationship and node coupling strength, and outputs a control signal containing stimulation control parameters to the probe corresponding to the head neural modulation node that needs to be adapted through the state transition induction module, so that the probe outputs multimodal stimulation signals to form node collaborative modulation. The host terminal also collects feedback signals containing EEG rhythm characteristics and tissue coupling characteristics through the probe, and dynamically optimizes the node topology, node coupling strength and stimulation control parameters based on the feedback signals to guide the brain state to migrate between different sleep stages.

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