Exoskeleton rehabilitation equipment control method and system based on electroencephalogram signals

Abstract

The invention discloses an exoskeleton rehabilitation equipment control method and system based on electroencephalogram signals, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting a plurality of signal features into an intention classification model for intention classification, receiving the plurality of signal features through an input layer, extracting the plurality of signal features through a feature extraction layer, a motion intention index calculation layer performs calculation on the basis of the characteristics required by calculation to obtain a motion intention index, and a decision layer determines the intention type of the patient on the basis of the motion intention index; generating a motion parameter adjustment instruction based on the patient intention type; and adjusting the motion speed and the motion amplitude of the leg support based on the motion parameter adjustment instruction. According to the technical scheme, the problems that in the prior art, close real-time feedback and linkage between exoskeleton rehabilitation equipment and the nerve state of a patient are lacked, and rehabilitation individuation, accuracy and real-time adaptability are all limited can be solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for controlling exoskeleton rehabilitation equipment based on electroencephalogram signals. Background Art

[0002] At present, exoskeleton rehabilitation equipment plays an important role in the field of neurological injury rehabilitation, especially for stroke, spinal cord injury and other diseases, exoskeleton rehabilitation equipment can assist patients to restore basic functions such as walking and standing. However, the current exoskeleton rehabilitation equipment control technology has many limitations.

[0003] On the one hand, a large number of exoskeleton rehabilitation devices use control systems based on fixed parameters. These preset fixed motion parameters, such as gait, speed and stride, are mostly based on the level of the general population or average rehabilitation patients. However, there are significant individual differences among patients with nerve damage. The degree of nerve damage and physical function of each patient are different. This lack of personalized considerations cannot meet the special needs of patients, which can easily lead to poor rehabilitation effects and even hinder the rehabilitation process.

[0004] In the process of realizing the present invention, the inventors found that the prior art lacks close real-time feedback and linkage between the exoskeleton rehabilitation equipment and the patient's neural status, and does not fully utilize EEG signals to guide exoskeleton rehabilitation, which limits the personalization, accuracy and real-time adaptability of exoskeleton rehabilitation. Summary of the invention

[0005] In view of this, the present invention proposes an exoskeleton rehabilitation device control method and system based on EEG signals to solve the problem in the prior art that there is a lack of close real-time feedback and linkage between the exoskeleton rehabilitation device and the patient's neural state, which limits the personalization, accuracy and real-time adaptability of exoskeleton rehabilitation.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for controlling an exoskeleton rehabilitation device based on electroencephalogram signals, comprising: Collect the patient's EEG signals in real time through an EEG signal acquisition device; Converting the EEG signal from the time domain to the frequency domain by short-time Fourier transform, calculating the power spectrum density corresponding to the EEG signal, calculating the coherence and weighted phase delay index based on the power spectrum density, performing multiple comparisons on the functional connectivity values ​​calculated for each frequency band by using the coherence and weighted phase delay index, and extracting a variety of signal features; Inputting a plurality of signal features into an intention classification model for intention classification, the intention classification model comprising an input layer, a feature extraction layer, a motion intention index calculation layer, a decision layer and an output layer, receiving a plurality of signal features through the input layer, extracting a plurality of signal features through the feature extraction layer to obtain a plurality of features required for calculation, performing calculation based on the required features for calculation through the motion intention index calculation layer to obtain a motion intention index, determining a patient's intention type based on the motion intention index through the decision layer, and outputting the patient's intention type through the output layer; generating a motion parameter adjustment instruction based on the patient's intention type; The movement speed and movement amplitude of the leg support are adjusted based on the movement parameter adjustment instruction.

[0007] On the basis of the above technical solution, the present invention can also be improved as follows: Optionally, the calculating the coherence and weighted phase delay index based on the power spectral density comprises: The correlation is calculated by formula (1); Formula (1); In the formula, is the cross power spectral density, For electrode The autopower spectral density of For electrode The autopower spectral density of and Indicates different electrodes; The weighted phase delay index is calculated by formula (2); Formula (2); In the formula, is a discrete time point, N is the number of data points, k is the frequency, is the symbolic function, is the phase difference. When the phase difference is greater than 0, , when the phase difference is less than 0, , when the phase difference is equal to 0, .

[0008] Optionally, the calculating by the movement intention index calculation layer based on the features required for the calculation to obtain the movement intention index includes: The patient's movement intention index was calculated by formula (3); Formula (3); In the formula, is the movement intention index, EEG The frequency band set, EEG The power spectral density of EEG The weight coefficient of is the set of electrode pairs, For electrode pair The coherence value of For electrode pair The coherence weight of is the set of electrode pairs, For electrode pair The weighted phase delay index of For electrode pair The weighted phase delay index weight, is the adjustment coefficient for balancing the power spectral density, is the coherent adjustment coefficient, is the adjustment coefficient of the weighted phase delay index.

[0009] Optionally, determining the patient intention type based on the movement intention index by the decision layer includes: Determining whether the movement intention index is greater than a first preset threshold, and if so, determining it as a first patient intention type; Determining whether the movement intention index is within a second preset threshold range, and if so, determining it as a second patient intention type; Determine whether the movement intention index is less than a third preset threshold, and if so, determine it as a third patient intention type.

[0010] Optionally, the exoskeleton rehabilitation device control method based on EEG signals further includes: Recording the EEG signals of a normal person in different motion states, and storing the EEG signals of the normal person in different motion states in a storage unit; Determine whether there is a difference between the patient's EEG signal and that of a normal person under the same motion state. If so, determine the difference data and adjust the electrical stimulation parameters based on the difference data.

[0011] Optionally, the determining whether there is a difference between the EEG signal of the patient and the EEG signal of a normal person under the same motion state, and if so, determining difference data, and adjusting the electrical stimulation parameters based on the difference data, includes: The difference data is calculated by formula (4); Formula (4); In the formula, For differential data, is the weight of the time domain difference data, is the time domain difference data, is the weight of the frequency domain difference data, is the frequency domain difference data; The adjusted electrical stimulation intensity is calculated by formula (5); Formula (5); In the formula, is the adjusted electrical stimulation intensity, is the initial electrical stimulation intensity, is the electrical stimulation intensity adjustment coefficient, For differential data; The adjusted electrical stimulation frequency is calculated by formula (6); Formula (6); In the formula, is the adjusted electrical stimulation frequency, is the initial electrical stimulation frequency, is the electrical stimulation frequency adjustment coefficient, For differential data; The adjusted stimulation duration is calculated by formula (7); Formula (7); In the formula, is the adjusted stimulation duration, is the initial stimulation duration, is the stimulus duration adjustment factor, The difference data.

[0012] An exoskeleton rehabilitation device control system based on electroencephalogram signals, comprising: An electroencephalogram signal acquisition device, used for acquiring the patient's electroencephalogram signal in real time; A multiple signal feature extraction unit, used for converting the EEG signal from the time domain to the frequency domain by short-time Fourier transform, calculating the power spectrum density corresponding to the EEG signal, calculating the coherence and weighted phase delay index based on the power spectrum density, performing multiple comparisons on the functional connectivity values ​​calculated for each frequency band by using the coherence and weighted phase delay index, and extracting multiple signal features; an intention type determination unit, configured to input a plurality of signal features into an intention classification model for intention classification, wherein the intention classification model comprises an input layer, a feature extraction layer, a motion intention index calculation layer, a decision layer and an output layer, wherein a plurality of signal features are received through the input layer, a plurality of signal features are extracted through the feature extraction layer to obtain a plurality of features required for calculation, a motion intention index is obtained by performing calculation based on the features required for calculation through the motion intention index calculation layer, a patient intention type is determined based on the motion intention index through the decision layer, and the patient intention type is output through the output layer; An adjustment instruction generating unit, configured to generate a motion parameter adjustment instruction based on the patient's intention type; The power unit is used to adjust the movement speed and movement amplitude of the leg support based on the movement parameter adjustment instruction.

[0013] Optionally, the exoskeleton rehabilitation device control system based on EEG signals further includes a storage unit and a control unit; The storage unit is used to store EEG signals of normal people in different motion states; The control unit is used to determine whether there is a difference between the patient's EEG signal and the EEG signal of a normal person under the same movement state. If so, determine the difference data and adjust the electrical stimulation parameters based on the difference data.

[0014] An electronic device comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method when executing the computer program.

[0015] A non-transitory computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0016] The present invention has the following advantages: The exoskeleton rehabilitation equipment control method based on EEG signals in the present invention can closely track the changes in the patient's neural activity, analyze the movement intention in real time and dynamically optimize the movement parameters of the leg support based on the coordinated operation of the EEG signal acquisition device and the intention classification model. It breaks the limitations of the traditional fixed mode, provides patients with a personalized rehabilitation path that fits their current state, greatly improves the accuracy and effectiveness of rehabilitation training, and significantly enhances the rehabilitation effect.

[0017] The exoskeleton rehabilitation equipment control method based on EEG signals in the present invention adopts non-invasive EEG signals as the control source, avoiding the risks and discomforts that may be caused by traditional invasive stimulation methods, providing patients with a safe and non-traumatic rehabilitation experience, and improving patients' acceptance of rehabilitation treatment.

[0018] The exoskeleton rehabilitation equipment control method based on EEG signals in the present invention can customize an exclusive rehabilitation plan for each patient according to the EEG signal characteristics of each patient, enhance the targetedness and effectiveness of treatment, and further improve the patient's compliance and satisfaction during the rehabilitation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] For the purpose of illustration and not limitation, the present invention is now described in conjunction with the embodiments of the present invention and the accompanying drawings, in which: Figure 1Schematic diagram of a first process of a method for controlling an exoskeleton rehabilitation device based on EEG signals in an embodiment of the present invention; Figure 2 is a schematic diagram of a second process of a method for controlling an exoskeleton rehabilitation device based on EEG signals in an embodiment of the present invention; Figure 3 It is a schematic diagram of the main components of the control system of the exoskeleton rehabilitation device based on EEG signals in an embodiment of the present invention; Figure 4 This is a schematic diagram of the physical structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution in the implementation of the present invention will be clearly and completely described below in conjunction with the drawings in the implementation of the present invention. Obviously, the described implementation is only a part of the implementation of the present invention, not all of the implementations. Based on the implementation of the present invention, all other implementations obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the specification of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Figure 1 FIG. 1 is a schematic diagram of a first process of a method for controlling an exoskeleton rehabilitation device based on an electroencephalogram signal in an embodiment of the present invention. Figure 1 As shown, the first exoskeleton rehabilitation device control method based on EEG signals provided in an embodiment of the present invention includes the following steps S101 to S105.

[0024] S101, collecting the patient's electroencephalogram (EEG) signals in real time through an EEG signal collection device.

[0025] The specific operation can be to closely attach the electrode array of the EEG signal acquisition device to the patient's scalp, so as to accurately monitor the patient's brain wave activity in real time. Regardless of whether the patient is in a resting state or in the process of exercise, his or her neural state information can be obtained comprehensively and carefully.

[0026] S102, extracting multiple signal features from the EEG signal.

[0027] Specifically, the EEG signal is preprocessed to obtain preprocessed data, wherein the data preprocessing includes filtering, data segmentation, bad segment removal, artifact removal and re-reference. The EEG signal is accurately segmented and deeply analyzed through mark marking, so that the control unit can accurately understand the patient's movement intention corresponding to the changes in the EEG signal at different task stages.

[0028] The pre-processed data is converted from the time domain to the frequency domain by short-time Fourier transform, and the power spectrum density corresponding to the pre-processed data is calculated.

[0029] A coherence and weighted phase delay index is calculated based on the power spectral density.

[0030] The correlation is calculated by formula (1); Formula (1); In the formula, is the cross power spectral density, For electrode The autopower spectral density of For electrode The autopower spectral density of and Indicates different electrodes; The weighted phase delay index is calculated by formula (2); Formula (2); In the formula, is a discrete time point, N is the number of data points, k is the frequency, is the symbolic function, is the phase difference. When the phase difference is greater than 0, , when the phase difference is less than 0, , when the phase difference is equal to 0, .

[0031] The functional connectivity values ​​calculated for each frequency band were subjected to multiple comparisons by the network solution toolbox using the coherence and weighted phase delay index. After correction for multiple comparisons, the connectivity of the brain functional network was visualized using BrainNet Viewer, so as to deeply explore the characteristics of the brain functional network, discover potential differences and associations in brain function, and extract a variety of signal features.

[0032] Multiple signal features are input into the intention classification model to classify the intention and obtain the patient's movement intention.

[0033] S103, inputting multiple signal features into an intent classification model to perform intent classification.

[0034] Specifically, the intention classification model includes an input layer, a feature extraction layer, a motion intention index calculation layer, a decision layer and an output layer. The input layer receives a variety of signal features, the feature extraction layer extracts a variety of signal features to obtain a variety of features required for calculation, the motion intention index calculation layer performs calculations based on the features required for calculation to obtain a motion intention index, the decision layer determines the patient's intention type based on the motion intention index, and the output layer outputs the patient's intention type.

[0035] The patient's movement intention index was calculated by formula (3); Formula (3); In the formula, is the movement intention index, EEG The frequency band set, EEG The power spectral density of EEG The weight coefficient of is the set of electrode pairs, For electrode pair The coherence value of For electrode pair The coherence weight of is the set of electrode pairs, For electrode pair The weighted phase delay index of For electrode pair The weighted phase delay index weight, is the adjustment coefficient for balancing the power spectral density, is the coherent adjustment coefficient, is the adjustment coefficient of the weighted phase delay index.

[0036] Determining whether the movement intention index is greater than a first preset threshold, and if so, determining it as a first patient intention type; Determining whether the movement intention index is within a second preset threshold range, and if so, determining it as a second patient intention type; Determine whether the movement intention index is less than a third preset threshold, and if so, determine it as a third patient intention type.

[0037] An embodiment is: After a large number of clinical experiments and analysis of a large number of patient data, the first preset threshold was set at 0.6, the second preset threshold range was 0.3 - 0.6 (including 0.3 and excluding 0.6), and the third preset threshold was 0.3.

[0038] When the movement intention index is greater than 0.6, it is determined to be the first patient intention type, defined as "strong active movement intention", which means that the patient has a high willingness and relatively sufficient physical strength to perform voluntary movements of relatively large amplitude and speed.

[0039] When the movement intention index is between 0.3 and 0.6, it is determined to be the second patient intention type, defined as “moderate movement intention”, indicating that the patient has a certain willingness to exercise but may need partial auxiliary support from the exoskeleton device.

[0040] When the movement intention index is less than 0.3, it is judged as the third patient intention type, which is defined as "weak movement intention or no obvious movement intention". At this time, the patient may be at rest or have only very weak movement intention.

[0041] Based on this judgment result, the control unit of the exoskeleton rehabilitation device generates corresponding motion parameter adjustment instructions. For "moderate motion intention", the exoskeleton rehabilitation device may appropriately assist the patient's movement, adjust the movement speed of the leg support to a medium speed, and set the movement amplitude to a medium amplitude to match the patient's current motion intention and help the patient conduct more effective rehabilitation training.

[0042] In this way, the exoskeleton rehabilitation device can accurately determine the patient's intention type based on the patient's real-time movement intention index and make corresponding adjustments to achieve personalized and precise rehabilitation training assistance.

[0043] S104, generating motion parameter adjustment instructions based on the patient's intention type.

[0044] S105, adjusting the movement speed and movement range of the leg support based on the movement parameter adjustment instruction.

[0045] Specifically, in terms of adjusting the movement speed, the control unit will adjust the speed parameter value in the instruction according to the movement parameter, and accurately control the speed of the drive motor. By sending a specific pulse signal or analog voltage signal to the motor driver, the motor speed can be finely adjusted. For example, if the instruction requires speeding up the movement of the leg support, the control unit will increase the pulse frequency sent to the motor driver or increase the analog voltage amplitude, thereby increasing the motor speed and driving the leg support to run at a faster speed to match the movement intention conveyed by the patient's brain, such as being able to respond in time when the patient expects to walk quickly or perform rapid leg movements.

[0046] As for the adjustment of the range of motion, the control unit will accurately control the range of motion of the leg support joints according to the amplitude information in the command. The advanced sensor feedback is combined with the precise mechanical structure design to ensure that the amplitude of the leg support is accurate during flexion, extension, rotation and other movements. For example, when the command requires an increase in the range of motion, the control unit will adjust the limit device or control signal of the relevant joint so that the leg support can move within a larger angle range, which may be manifested as an increase in the leg lifting amplitude, a wider stride, etc., so that patients can perform more natural and coordinated leg movement training according to their own exercise intentions during the rehabilitation training process, further improving the effectiveness and adaptability of rehabilitation training, and better promoting the recovery and reconstruction of patients' nerve and muscle functions.

[0047] In this EEG-based exoskeleton rehabilitation device control method, the movement speed and amplitude of the leg support are adjusted instantly according to the patient's real-time EEG signals. During rehabilitation training, the patient's physical state and neural response may change at any time. For example, the patient may feel tired at a certain moment, and his or her movement intention will change accordingly. By collecting and analyzing EEG signals in real time, the control unit can quickly capture this change and adjust the exoskeleton's movement parameters in time to ensure that rehabilitation training is always carried out in the best condition.

[0048] This exoskeleton rehabilitation device control method based on EEG signals enables patients to train according to parameters that are consistent with their own movement intentions, can better mobilize the coordination of their own nerves and muscles, and accelerate the rehabilitation process. For example, during the rehabilitation process of a stroke patient, the personalized movement parameters generated according to his EEG signals can allow the patient to perform leg movements within a comfortable and effective range, avoid overtraining or undertraining, and thus restore walking ability more efficiently. Real-time adaptability helps prevent patients from being injured due to inappropriate movement parameters during rehabilitation training. For example, if a patient suddenly experiences abnormal conditions such as muscle spasms, the EEG signal will change. After detecting it, the control unit will immediately adjust the movement amplitude and speed of the leg support to avoid secondary injuries to the patient due to the forced movement of the exoskeleton, thereby ensuring the safety of rehabilitation training.

[0049] This EEG-based exoskeleton rehabilitation device control method combines EEG signals with exoskeleton motion control. By adjusting the exoskeleton motion according to the motion intention analyzed by EEG signals, the patient's brain, nervous system and exoskeleton can form an organic whole, which can better promote the recovery of neurological function. For example, when the patient's brain generates the motion intention of walking, the exoskeleton can assist the patient to complete the corresponding action in a timely and accurate manner. This synergistic effect helps to strengthen the neural connection between the brain and limbs and accelerate neurological rehabilitation.

[0050] Figure 2FIG. 1 is a schematic diagram of a second process of an embodiment of an exoskeleton rehabilitation device control method based on EEG signals of the present invention. Figure 2 As shown, in the second process of the exoskeleton rehabilitation device control method based on EEG signals in the embodiment of the present invention, the first to fifth steps are the same as the first process above, except that the second process further includes, after the above step S105: S106, determining whether there is a difference between the patient's EEG signal and that of a normal person under the same motion state, and if so, determining difference data, and adjusting electrical stimulation parameters based on the difference data.

[0051] Specifically, the EEG signals of normal people in different motion states are recorded and stored in a storage unit; after receiving the patient's EEG signal, it will first be compared and analyzed comprehensively and deeply with the patient's EEG signal in the same motion state pre-stored in the storage unit. This process involves precise consideration of multiple characteristic dimensions of the EEG signal, including but not limited to the signal's frequency distribution, amplitude, phase characteristics, and energy ratios between different frequency bands (such as delta, theta, alpha, beta, and gamma bands).

[0052] Advanced signal processing algorithms and data analysis techniques, such as spectrum analysis, time-frequency analysis, and classification algorithms in machine learning, are used to extract and quantify these features. By calculating the mean, variance, standard deviation and other statistical indicators of various feature parameters and strictly comparing them with the corresponding indicator range of normal EEG signals, it is possible to accurately determine whether the patient's EEG signals are different from normal EEG signals.

[0053] Assume that for a certain motion state, the collected patient EEG signal sequence is ,in , is the number of EEG signal data points of the patient; the EEG signal sequence of a normal person is , and through data preprocessing (such as interpolation, truncation, etc.) (Ensure that the number of data points is the same for the subsequent calculation and comparison).

[0054] First, calculate the mean of the two, which are respectively and ; ; ; Then calculate the absolute difference of the means as the mean difference part ; ; Calculate the variance of the patient's EEG signal and the normal person's EEG signal respectively, denoted as and ; ; ; Variance Difference Defined as: ; Assign weights to mean differences and variance differences separately and , , the weights can be determined based on actual experience or experimental results to obtain comprehensive time domain difference data: ; First, use a suitable transformation method (such as fast Fourier transform) to transform the time domain EEG signal and Convert to the frequency domain and get the corresponding power spectral density function and ,in represents the frequency, assuming that the frequency range considered is .

[0055] Compute the absolute difference in power spectral density at each frequency point: ; Take a weighted average of the absolute differences at all frequency points (assuming the weight is , which can be set according to factors such as the importance of different frequencies), and obtain frequency domain difference data: ; Taking into account the above time domain difference data and frequency domain difference data , and then assign them weights and ( ), and obtain the final comprehensive difference data : The difference data is calculated by formula (4); Formula (4); In the formula, For differential data, is the weight of the time domain difference data, is the time domain difference data, is the weight of the frequency domain difference data, is the frequency domain difference data; Through this progressively constructed formula, the differences between the EEG signals of patients and normal people can be quantified from different dimensions (time domain, frequency domain and a combination of the two), which can more comprehensively and accurately reflect the differences between the two. Then, based on the difference data, the electrical stimulation parameters can be reasonably adjusted, and operations such as operation can be performed to help exoskeleton rehabilitation equipment better serve the patient's rehabilitation process.

[0056] The adjusted electrical stimulation intensity is calculated by formula (5); Formula (5); In the formula, is the adjusted electrical stimulation intensity, is the initial electrical stimulation intensity, is the electrical stimulation intensity adjustment coefficient, For differential data; The adjusted electrical stimulation frequency is calculated by formula (6); Formula (6); In the formula, is the adjusted electrical stimulation frequency, is the initial electrical stimulation frequency, is the electrical stimulation frequency adjustment coefficient, For differential data; The adjusted stimulation duration is calculated by formula (7); Formula (7); In the formula, is the adjusted stimulation duration, is the initial stimulation duration, is the stimulus duration adjustment factor, The difference data.

[0057] If it is determined that there is a difference, the difference information is further mined to determine the specific difference data. The deviation degree and change trend of the difference data in terms of frequency, amplitude, phase, etc. are analyzed in detail, such as determining which frequency bands have abnormally increased or decreased energy, whether the amplitude fluctuation range exceeds the normal threshold, and whether the phase synchronization is destroyed.

[0058] Based on these precisely determined differential data, the electrical stimulation parameters are then intelligently adjusted. If it is found that the energy of the patient's EEG signal in a certain frequency band is too low, it may indicate that the neural function corresponding to this frequency band is not active enough. In this case, the frequency of electrical stimulation can be appropriately increased to stimulate the neural pathways related to this frequency band in a targeted manner and enhance their activity. Or if the amplitude fluctuates too much, it may mean that the neural excitability is unstable. In this case, the intensity or pulse width of the electrical stimulation can be adjusted to stabilize the excitability of the nerves and promote the recovery and balance of neural function. Through this dynamic electrical stimulation parameter adjustment based on differential data, precise regulation of the patient's neural state and personalized rehabilitation treatment can be achieved.

[0059] Figure 3 FIG. 1 is a schematic diagram of the main components of the control system of the exoskeleton rehabilitation device based on EEG signals in an embodiment of the present invention. Figure 3 As shown, the exoskeleton rehabilitation equipment control system 1 based on EEG signals provided in an embodiment of the present invention includes an EEG signal acquisition device 10, a plurality of signal feature extraction units 20, an intention type determination unit 30, an adjustment instruction generation unit 40 and a power unit 50.

[0060] The electroencephalogram signal acquisition device 10 is used to acquire the electroencephalogram signal of the patient in real time.

[0061] The multiple signal feature extraction unit 20 is used to convert the EEG signal from the time domain to the frequency domain by short-time Fourier transform, calculate the power spectrum density corresponding to the EEG signal, calculate the coherence and weighted phase delay index based on the power spectrum density, perform multiple comparisons on the functional connectivity values ​​calculated for each frequency band by using the coherence and weighted phase delay index, and extract multiple signal features; The intention type determination unit 30 is used to input multiple signal features into an intention classification model for intention classification, wherein the intention classification model includes an input layer, a feature extraction layer, a motion intention index calculation layer, a decision layer and an output layer, wherein multiple signal features are received through the input layer, multiple signal features are extracted through the feature extraction layer to obtain multiple features required for calculation, the motion intention index is calculated based on the features required for calculation through the motion intention index calculation layer to obtain a motion intention index, the patient's intention type is determined based on the motion intention index through the decision layer, and the patient's intention type is output through the output layer; The adjustment instruction generating unit 40 is used to generate a motion parameter adjustment instruction based on the patient's intention type; The power unit 50 is used to adjust the movement speed and movement amplitude of the leg support based on the movement parameter adjustment instruction.

[0062] Figure 4 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 4As shown, the electronic device 60 includes: a processor 601 (processor), a memory 602 (memory) and a bus 603; The processor 601 and the memory 602 communicate with each other via the bus 603; The processor 601 is used to call the program instructions in the memory 602 to execute the methods provided by the above-mentioned method embodiments.

[0063] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions. The computer instructions enable a computer to execute the methods provided by the above method embodiments.

[0064] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various storage media that can store program codes.

[0065] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for controlling an exoskeleton rehabilitation device based on electroencephalogram signals, characterized in that: include: Collect the patient's EEG signals in real time through an EEG signal acquisition device; Converting the EEG signal from the time domain to the frequency domain by short-time Fourier transform, calculating the power spectrum density corresponding to the EEG signal, calculating the coherence and weighted phase delay index based on the power spectrum density, performing multiple comparisons on the functional connectivity values ​​calculated for each frequency band by using the coherence and weighted phase delay index, and extracting a variety of signal features; Inputting a plurality of signal features into an intention classification model for intention classification, the intention classification model comprising an input layer, a feature extraction layer, a motion intention index calculation layer, a decision layer and an output layer, receiving a plurality of signal features through the input layer, extracting a plurality of signal features through the feature extraction layer to obtain a plurality of features required for calculation, performing calculation based on the required features for calculation through the motion intention index calculation layer to obtain a motion intention index, determining a patient's intention type based on the motion intention index through the decision layer, and outputting the patient's intention type through the output layer; generating a motion parameter adjustment instruction based on the patient's intention type; The movement speed and movement amplitude of the leg support are adjusted based on the movement parameter adjustment instruction.

2. The exoskeleton rehabilitation device control method based on EEG signals according to claim 1, characterized in that: The calculating of the coherence and weighted phase delay index based on the power spectral density comprises: The correlation is calculated by formula (1); Formula (1): In the formula, is the cross power spectral density, For electrode The autopower spectral density of For electrode The autopower spectral density of and Indicates different electrodes; The weighted phase delay index is calculated by formula (2); Formula(2); In the formula, is a discrete time point, N is the number of data points, k is the frequency, is the symbolic function, is the phase difference. When the phase difference is greater than 0, , when the phase difference is less than 0, , when the phase difference is equal to 0, .

3. The exoskeleton rehabilitation device control method based on EEG signals according to claim 2, characterized in that: The step of calculating the motion intention index by the motion intention index calculation layer based on the features required for the calculation to obtain the motion intention index includes: The patient's movement intention index was calculated by formula (3); Formula (3): In the formula, is the movement intention index, EEG The frequency band set, EEG The power spectral density of EEG The weight coefficient of is the set of electrode pairs, For electrode pair The coherence value of For electrode pair The coherence weight of is the set of electrode pairs, For electrode pair The weighted phase delay index of For electrode pair The weighted phase delay index weight, is the adjustment coefficient for balancing the power spectral density, is the coherent adjustment coefficient, is the adjustment coefficient of the weighted phase delay index.

4. The exoskeleton rehabilitation device control method based on EEG signals according to claim 1, characterized in that: The determining the patient intention type based on the movement intention index by the decision layer includes: Determining whether the movement intention index is greater than a first preset threshold, and if so, determining it as a first patient intention type; Determining whether the movement intention index is within a second preset threshold range, and if so, determining it as a second patient intention type; Determine whether the movement intention index is less than a third preset threshold, and if so, determine it as a third patient intention type.

5. The exoskeleton rehabilitation device control method based on EEG signals according to claim 1, characterized in that: The exoskeleton rehabilitation device control method based on EEG signals also includes: Recording the EEG signals of a normal person in different motion states, and storing the EEG signals of the normal person in different motion states in a storage unit; Determine whether there is a difference between the patient's EEG signal and that of a normal person under the same motion state. If so, determine the difference data and adjust the electrical stimulation parameters based on the difference data.

6. The exoskeleton rehabilitation device control method based on EEG signals according to claim 5, characterized in that: The determining whether there is a difference between the EEG signal of the patient and the EEG signal of a normal person under the same motion state, and if so, determining difference data, and adjusting the electrical stimulation parameters based on the difference data, includes: The difference data is calculated by formula (4); Formula (4): In the formula, For differential data, is the weight of the time domain difference data, is the time domain difference data, is the weight of the frequency domain difference data, is the frequency domain difference data; The adjusted electrical stimulation intensity is calculated by formula (5); Formula (5): In the formula, is the adjusted electrical stimulation intensity, is the initial electrical stimulation intensity, is the electrical stimulation intensity adjustment coefficient, For differential data; The adjusted electrical stimulation frequency is calculated by formula (6); Formula (6): In the formula, is the adjusted electrical stimulation frequency, is the initial electrical stimulation frequency, is the electrical stimulation frequency adjustment coefficient, For differential data; The adjusted stimulation duration is calculated by formula (7); Formula (7): In the formula, is the adjusted stimulation duration, is the initial stimulation duration, is the stimulus duration adjustment factor, The difference data.

7. An exoskeleton rehabilitation equipment control system based on EEG signals, characterized in that: include: An electroencephalogram signal acquisition device, used for acquiring the patient's electroencephalogram signal in real time; A multiple signal feature extraction unit, used for converting the EEG signal from the time domain to the frequency domain by short-time Fourier transform, calculating the power spectrum density corresponding to the EEG signal, calculating the coherence and weighted phase delay index based on the power spectrum density, performing multiple comparisons on the functional connectivity values ​​calculated for each frequency band by using the coherence and weighted phase delay index, and extracting multiple signal features; an intention type determination unit, configured to input a plurality of signal features into an intention classification model for intention classification, wherein the intention classification model comprises an input layer, a feature extraction layer, a motion intention index calculation layer, a decision layer and an output layer, wherein a plurality of signal features are received through the input layer, a plurality of signal features are extracted through the feature extraction layer to obtain a plurality of features required for calculation, a motion intention index is obtained by performing calculation based on the features required for calculation through the motion intention index calculation layer, a patient intention type is determined based on the motion intention index through the decision layer, and the patient intention type is output through the output layer; An adjustment instruction generating unit, configured to generate a motion parameter adjustment instruction based on the patient's intention type; The power unit is used to adjust the movement speed and movement amplitude of the leg support based on the movement parameter adjustment instruction.

8. The exoskeleton rehabilitation equipment control system based on EEG signals according to claim 7, characterized in that: The exoskeleton rehabilitation device control system based on EEG signals also includes a storage unit and a control unit; The storage unit is used to store EEG signals of normal people in different motion states; The control unit is used to determine whether there is a difference between the patient's EEG signal and the EEG signal of a normal person under the same movement state. If so, determine the difference data and adjust the electrical stimulation parameters based on the difference data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A non-transitory computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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