Method for processing biological signal generated during movement of lower extremity

Abstract

The invention provides a method for processing biological signals generated in lower limb movement, which comprises the following steps of: preprocessing collected electroencephalogram signals and electromyographic signals which are generated corresponding to ankle dorsal flexion actions; calculating the cross spectrum density between the electroencephalogram signal and the electromyographic signal according to the preprocessed electroencephalogram signal and the preprocessed electromyographic signal; the coupling strength of the electroencephalogram signals and the electromyographic signals at the specific frequency is calculated according to the cross spectrum density, and the coupling strength is used for evaluating the lower limb movement function of the sporter. According to the invention, a frequency band specific normalized cross-spectrum power (NCSP) analysis method is adopted to quantify the coupling strength of the electroencephalogram signal and the electromyographic signal in alpha, beta and gamma frequency bands, a neural mechanism of coupling weakening of the alpha and beta frequency bands after stroke is disclosed, a new biomarker is provided for motion control disorder, and the application prospect is wide. And personalized rehabilitation evaluation and dynamic adjustment can be realized based on the dynamic change of the NCSP value, and formulation and optimization of a precise rehabilitation scheme are supported.

Description

Technical Field

[0001] The present invention relates to the technical field of lower limb movement assessment, and more specifically, to a method, a terminal device, and a computer-readable storage medium for processing bio-signals generated during lower limb movement. Background Art

[0002] Stroke is a serious neurological disease, usually caused by insufficient blood perfusion in the brain or a bleeding event, resulting in irreversible brain tissue damage. Motor dysfunction is one of the most common sequelae of stroke, and hemiplegia is the main manifestation, seriously affecting the patient's daily activity ability and independence. With the development of neurophysiological techniques, especially the progress of electroencephalogram (EEG) and electromyogram (EMG) techniques, researchers are able to collect rich bioelectrical data, more accurately evaluate the degree of motor impairment in stroke patients, and monitor their rehabilitation progress. By synchronously recording EEG and EMG signals, the information exchange between the brain and muscles can be deeply analyzed, reflecting the control level of the cerebral cortex over muscle activity. Corticomuscular Coupling (CMC) refers to the process in which motor commands are sent from the cerebral cortex, conducted through the nervous system to the relevant muscles, inducing muscle responses, and providing feedback to the brain. By analyzing the synchrony of EEG and EMG signals, the functional integration pattern between the cerebral cortex and muscles can be revealed, thereby evaluating the integrity of the neural control system and its plasticity during the rehabilitation process.

[0003] Currently, some studies have attempted to explore the characteristics of upper limb corticomuscular coupling and evaluate the degree of motor control through the synchrony analysis of EEG and EMG signals. These studies have utilized spectral analysis techniques of EEG and EMG signals, such as power spectrum analysis and coherence analysis, to reveal the changes in corticomuscular coupling. However, research on lower limb corticomuscular coupling is still relatively scarce, mainly because the cortical regions involved in lower limb motor control are relatively complex, located in the medial cortical region of the brain, making it more difficult to conduct research in this field. Summary of the Invention

[0004] To solve the above technical problems existing in the prior art, the present invention provides a method, a terminal device, and a computer-readable storage medium for processing bio-signals generated during lower limb movement.

[0005] The method for processing bio-signals generated during lower limb movement provided by one aspect of the present invention includes: preprocessing the collected EEG signals and EMG signals; calculating the cross-spectral density between the preprocessed EEG signals and the preprocessed EMG signals; calculating the coupling strength between the EEG signals and the EMG signals according to the cross-spectral density, and the coupling strength is used to evaluate the lower limb motor function of the exerciser.

[0006] In an example of the method provided in the above aspect, the method for preprocessing the electroencephalogram signal includes: performing a first filtering process on the electroencephalogram signal by using a notch filter; performing a second filtering process on the electroencephalogram signal after the first filtering process by using a band-pass filter; performing a first artifact reduction process on the electroencephalogram signal after the second filtering process by using a common reference averaging method; and performing a second artifact reduction process on the electroencephalogram signal after the first artifact reduction process by using an independent component analysis algorithm.

[0007] In an example of the method provided in the above aspect, the method for preprocessing the electromyogram signal includes: performing a first filtering process on the electromyogram signal by using a band-pass filter; performing a second filtering process on the electromyogram signal after the second filtering process by using a notch filter; and resampling the electromyogram signal after the second filtering process so that the sampling frequency of the resampled electromyogram signal is the same as the sampling frequency of the electroencephalogram signal.

[0008] In an example of the method provided in the above aspect, according to the preprocessed electroencephalogram signal and the preprocessed electromyogram signal, the cross-spectral density between the electroencephalogram signal and the electromyogram signal is calculated by using the following formula

[0009]

[0010] where S xy (f) represents the cross-spectral density between the electroencephalogram signal and the electromyogram signal, X n (f) represents the Fourier transform of the electroencephalogram signal collected for the nth time, Y n (f) represents the Fourier transform of the electromyogram signal collected for the nth time, represents the complex conjugate of Y n (f), N is the total number of acquisitions, and f represents the frequency.

[0011] In an example of the method provided in the above aspect, the coupling strength between the electroencephalogram signal and the electromyogram signal at a specific frequency is the absolute value of the cross-spectral density.

[0012] In an example of the method provided in the above aspect, the method further includes: performing min-max normalization processing on the coupling strength.

[0013] In an example of the method provided in the above aspect, the method further includes: averaging the coupling strengths after min-max normalization processing for all acquisitions within any one of all frequency bands to obtain the average normalized coupling strength of the any one frequency band.

[0014] In an example of the method provided in the above aspect, all frequency bands include a first frequency band, a second frequency band, and a third frequency band. The first frequency band is 8 - 13 Hz, the second frequency band is 14 - 30 Hz, and the third frequency band is 31 - 80 Hz.

[0015] According to another aspect of the present invention, a terminal device is further provided, which includes a processor and a memory connected to the processor. Among them, the memory stores program instructions; the processor is configured to execute the program instructions stored in the memory to implement the method as described above.

[0016] According to still another aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores program instructions, and when the program instructions are executed, the method as described above is implemented.

[0017] Beneficial effects: Through the innovative dynamic ankle dorsiflexion task paradigm, the present invention fills the gap in the research of lower limb corticomuscular coupling (CMC) and expands the application scope of corticomuscular coupling research. Further, the present invention adopts the frequency band-specific normalized cross-spectrum power (or normalized coupling strength) (NCSP) analysis method to quantify the coupling strength between EEG and EMG in the α, β, and γ frequency bands, reveals the neural mechanism of the weakened coupling in the α and β frequency bands after stroke, provides a new biomarker for movement control disorders, and based on the dynamic changes of NCSP values, personalized rehabilitation evaluation and dynamic adjustment can be realized, supporting the formulation and optimization of precise rehabilitation programs. Description of the Drawings

[0018] Through the following description in combination with the drawings, the above and other aspects, features, and advantages of the embodiments of the present invention will become clearer. In the drawings:

[0019] Figure 1 is a schematic diagram of an experimental paradigm adopted when conducting experiments using the method for processing biological signals generated during lower limb movement according to an embodiment of the present invention;

[0020] Figure 2 is a flowchart of the method for processing biological signals generated during lower limb movement according to an embodiment of the present invention;

[0021] Figure 3 is an experimental control diagram of conducting experiments on subjects using the method for processing biological signals generated during lower limb movement according to an embodiment of the present invention;

[0022] Figure 4 is a schematic structural diagram of the terminal device according to an embodiment of the present invention;

[0023] Figure 5Schematic structural diagram of a computer storage medium according to an embodiment of the present invention. Detailed implementation manners

[0024] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0025] Those skilled in the art of the present technology can understand that unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "including" used in the description of the present invention means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when the present application states that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any unit and all combinations of one or more related listed items.

[0026] Those skilled in the art of the present technology can understand that unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined as here.

[0027] The present invention aims to study the cortical-muscular coupling characteristics in the dynamic ankle dorsiflexion movement task. By synchronously collecting electroencephalogram signals (or electroencephalogram, EEG) and electromyogram signals (or electromyogram, EMG), the coupling strength between the cortex and muscle activities is quantified, so as to reveal the neural regulation mechanism of lower limb movement control. Figure 1 Schematic diagram of an experimental paradigm adopted when conducting an experiment using a method for processing biological signals generated during lower limb movement according to an embodiment of the present invention. Refer to Figure 1 , the experimental paradigm includes the presentation of arrow cues, and these cues indicate different movement directions. Among them, the left arrow represents the left ankle dorsiflexion movement, the right arrow represents the right ankle dorsiflexion movement, the hollow arrow represents the movement imagination, and the solid arrow represents the actual movement.

[0028] In addition, in Figure 1 Relax represents rest and relaxation, Motor Imagery represents motor imagery, and Moving represents movement.

[0029] In each experiment, each action cue is continuously displayed for 5 seconds, followed by a random rest of 2 to 4 seconds, and then the next task is performed. Each experimental session contains 8 runs, and each run contains 2 trials for each action type. To avoid habituation effects, all trials are presented in a balanced random manner to ensure that each action is performed 64 times. Therefore, each run lasts approximately 1 minute, each experimental session lasts approximately 8 minutes, and a total of 256 trials (64 for each action type) are collected. During the experiment, participants can rest as needed between each session to ensure their comfort and improve data accuracy. To minimize the interference of external body movements on the EEG signal recording, participants are required to avoid unnecessary body movements during the experiment to ensure the synchronous recording of EEG signals and EMG signals.

[0030] Figure 2 is a flowchart of a method for processing biological signals generated during lower limb movement according to an embodiment of the present invention.

[0031] Referring to Figure 2 in step S210, the collected EEG signals and EMG signals are preprocessed.

[0032] Specifically, the EEG signals and EMG signals of the exerciser during ankle dorsiflexion movement are collected according to the experimental paradigm as Figure 1 shown.

[0033] In addition, the method for preprocessing the EEG signals includes: first, using a notch filter (such as a 50 Hz notch filter) to perform the first filtering process on the EEG signals to remove power frequency interference; second, using a bandpass filter to perform the second filtering process on the EEG signals after the first filtering process to limit the frequency of the EEG signals after the second filtering process between 3 - 80 Hz; then, using the common reference averaging method to perform the first artifact reduction process on the EEG signals after the second filtering process to reduce the artifacts related to the reference electrode; finally, using the independent component analysis (ICA) algorithm to perform the second artifact reduction process on the EEG signals after the first artifact reduction process to identify and remove the artifacts related to eye movement and muscle activity, thereby further purifying the EEG signals.

[0034] Furthermore, the method for preprocessing the EMG signals includes: First, the EMG signals are first filtered using a band-pass filter (such as a Chebyshev Type II band-pass filter), where the passband of the band-pass filter is 20 - 170 Hz, so as to remove low-frequency and high-frequency noises; Second, the notch filter is used to perform a second filtering process on the EMG signals after the second filtering process to further suppress power frequency interference; Finally, the EMG signals after the second filtering process are resampled (for example, resampled to 1000 Hz) so that the sampling frequency of the resampled EMG signals is the same as the sampling frequency of the EEG signals, thereby ensuring the time alignment of the two signal sources and enabling synchronous analysis. This series of preprocessing steps ensures the high quality of the EEG signals and EMG signals, providing a reliable signal basis for subsequent cortex-muscle coupling analysis and movement control evaluation.

[0035] In step S220, the cross-spectral density between the preprocessed EEG signals and the preprocessed EMG signals is calculated.

[0036] Specifically, in order to analyze the cortex-muscle coupling between the EEG signals and the EMG signals, the cross-spectral density (CSD) between the EEG signals and the EMG signals is calculated. The cross-spectral density is a frequency-domain metric that quantifies the degree of coupling between two signals and captures their resonant power and phase relationship at each frequency. The calculation formula for the cross-spectral density is: where, X n (f) and Y n (f) are the Fourier transforms of the EEG signals and the EMG signals in the nth experiment (or acquisition) respectively, denotes the complex conjugate of Y n (f), and N is the total number of experiments.

[0037] In step S230, the coupling strength between the EEG signals and the EMG signals is calculated based on the cross-spectral density. Among them, the coupling strength can be used to evaluate the lower limb motor function of the mover.

[0038] Specifically, in the calculated cross-spectral density, the amplitude |S xy (f)| reflects the coupling strength between the EEG signals and the EMG signals at frequency f, while the phase information reveals the phase difference between the EEG signals and the EMG signals.

[0039] In addition, in the method for processing the bio-signals generated during lower limb movement according to the embodiments of the present invention, in order to standardize this coupling strength, for |S xy(f) Min-max normalization was performed to obtain the normalized cross-spectral power (or normalized coupling strength) (NCSP). Subsequently, the normalized cross-spectral power was analyzed within three frequency bands (alpha wave: 8 - 13 Hz, beta wave: 14 - 30 Hz, gamma wave: 31 - 80 Hz). Within each frequency band, the average normalized cross-spectral power of that band was calculated by averaging the normalized cross-spectral power of all experiments, thus providing a robust metric for evaluating cortico-muscular coupling within different frequency ranges.

[0040] In addition, in the method for processing biosignals generated during lower limb movement according to an embodiment of the present invention, by synchronously recording electroencephalogram (EEG) and electromyogram (EMG) signals of a subject during a dynamic ankle dorsiflexion task, the cortico-muscular coupling strength is analyzed to achieve precise assessment of lower limb motor function and rehabilitation effect. Specifically, the system preferably acquires EEG signals in the Cz (central vertex) region (the main cortical region corresponding to lower limb movement) and EMG signals of the tibialis anterior (TA) muscle (because it has the largest activation amplitude during ankle dorsiflexion movement), and uses the Normalized Cross-Spectral Power (NCSP) method to calculate the coupling strength between EEG and EMG in the alpha, beta, and gamma frequency bands, thereby quantifying the information transfer efficiency between the brain and muscles. By comparing the NCSP values of the affected side, non-affected side, and healthy control group of stroke patients, the system can objectively evaluate the severity of motor dysfunction and identify the weakening of coupling in the beta and gamma frequency bands as potential neurophysiological biomarkers of impaired motor control after stroke.

[0041] Furthermore, the method for processing biosignals generated during lower limb movement according to an embodiment of the present invention can also be used for dynamic monitoring and evaluation of the rehabilitation effect of stroke patients after surgery. Based on the evaluation results, when rehabilitation progress (such as a significant increase in the NCSP value) or deterioration of dysfunction (such as a significant decrease in the NCSP value) is detected, the rehabilitation strategy and treatment plan can be dynamically adjusted, thereby achieving personalized and precise rehabilitation intervention.

[0042] Figure 3 is an experimental control chart of an evaluation experiment on a subject using the method for processing biosignals generated during lower limb movement according to an embodiment of the present invention. In Figure 3Among them, Figures A, D, and G show the α frequency band, Figures B, E, and H show the β frequency band, and Figures C, F, and I show the γ frequency band. Figures A, B, and C show the comparison between the left lower limb (Left on the abscissa) and the right lower limb (Right on the abscissa) of healthy participants; Figures D, E, and F show the comparison between the affected lower limb (Affected on the abscissa) and the unaffected lower limb (Unaffected on the abscissa) of stroke patients; Figures G, H, and I show the comparison between the lower limbs of healthy participants (Healthy on the abscissa) and the unaffected lower limb of stroke patients (Unaffected on the abscissa). * indicates p < 0.05, ** indicates p < 0.01, and p represents statistically significant differences.

[0043] Refer to Figure 3 , differences between the affected and unaffected lower limbs of stroke patients: The NCSP values of the affected lower limbs of stroke patients were significantly lower than those of the unaffected lower limbs in the α, β, and γ frequency bands (α frequency band: p < 0.001; β frequency band: p < 0.001; γ frequency band: p < 0.001), especially in the dynamic ankle dorsiflexion task, where the coupling in the β and γ frequency bands was more significantly weakened. This result reveals the disruption of the motor control pathway by stroke, especially the impact on fine motor coordination.

[0044] Comparison between the unaffected lower limb of stroke patients and the healthy control group: There were significant differences in the NCSP values of the unaffected lower limbs of stroke patients compared with the healthy control group in the α and β frequency bands (α frequency band: p = 0.014678; β frequency band: p = 0.01651), while there was no significant difference in the γ frequency band (p = 0.51471). This indicates that the cortico-muscular coupling of the unaffected lower limb is basically equivalent to that of healthy individuals, but there is a certain compensatory enhancement.

[0045] Therefore, the experimental results show that according to the embodiments of the present invention, through the dynamic ankle dorsiflexion task and the frequency band-specific NCSP analysis method, the cortico-muscular coupling differences between healthy individuals and stroke patients can be effectively quantified, verifying the feasibility of the method. According to the embodiments of the present invention, it is successfully revealed that the coupling in the α and β frequency bands of the affected lower limbs of stroke patients is weakened, providing a quantifiable biomarker for motor control disorders and supporting personalized rehabilitation assessment and dynamic adjustment. Specifically, when using the method according to the embodiments of the present invention to perform lower limb rehabilitation training on patients, by real-time feedback of the EEG-EMG cortico-muscular coupling quantification value (NCSP value) to the patients, the patients can be timely motivated to strengthen their active participation in lower limb rehabilitation exercise training. Further, taking the rehabilitation index (i.e., the NCSP value) of EEG-EMG fusion as the core feedback parameter, the progress and effect of motor rehabilitation can be quantified in real time; and the lower limb movement rehabilitation system can be continuously adjusted and optimized in real time to drive the intervention strategies such as electrical stimulation parameters and exoskeleton control, realizing the dynamic adaptation of intervention parameters and forming personalized rehabilitation assessment and dynamic adjustment.

[0046] In summary, according to the embodiments of the present invention, through the innovative dynamic ankle dorsiflexion task paradigm, the gap in the research of lower limb corticomuscular coupling (CMC) is filled, and the application scope of corticomuscular coupling research is expanded. Further, according to the embodiments of the present invention, the frequency band-specific NCSP analysis method is adopted to quantify the coupling strength between EEG and EMG in the α, β, and γ frequency bands, revealing the neural mechanism of the weakened coupling in the α and β frequency bands after stroke, providing a new biomarker for movement control disorders, and enabling personalized rehabilitation assessment and dynamic adjustment based on the dynamic changes of NCSP values, supporting the formulation and optimization of precise rehabilitation programs. Furthermore, according to the embodiments of the present invention, a segmented dynamic ankle dorsiflexion task (including a preparation period of 0.5 s, an execution period of 1 s, and a maintenance period of 1 s, with a total of two movement processes) is designed to synchronously trigger the segmented analysis of EEG-EMG signals.

[0047] In addition, to implement the method for processing biological signals generated during lower limb movement in the above embodiments, the present application also provides a terminal device 300. For details, please refer to Figure 4 The terminal device 300 according to the embodiments of the present application includes a processor 31, a memory 32, an input / output device 33, and a bus 34.

[0048] The processor 31, the memory 32, and the input / output device 33 are respectively connected to the bus 34. The memory 32 stores program data, and the processor 31 is configured to execute the program data to implement the method described in the above embodiments.

[0049] In the embodiments of the present application, the processor 31 may also be referred to as a CPU (Central Processing Unit). The processor 31 may be an integrated voltage control system chip with signal processing capabilities. The processor 31 may also be a general-purpose processor, a digital signal processor (DSP, Digital Signal Process), an application-specific integrated voltage control system (ASIC, Application Specific Integrated Circuit), a field programmable gate array (FPGA, FieldProgrammable GateArray), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor 31 may also be any conventional processor, etc.

[0050] The present application also provides a computer storage medium. Please continue to refer to Figure 5 , Figure 5It is a schematic structural diagram of an embodiment of a computer storage medium provided by the present application. Program data 41 is stored in the computer storage medium 40. When the program data 41 is executed by a processor, it is used to implement the method for processing biological signals generated during lower limb movement in the above embodiment.

[0051] When the embodiments of the present application are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0052] The above are only the embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for processing biological signals generated during lower limb movement, characterized in that, The method includes: Preprocessing the collected electroencephalogram (EEG) signals and electromyogram (EMG) signals, where the EEG signals and the EMG signals are generated corresponding to the ankle dorsiflexion movement; Calculating the cross-spectral density between the EEG signals and the EMG signals according to the preprocessed EEG signals and the preprocessed EMG signals; Calculating the coupling strength between the EEG signals and the EMG signals according to the cross-spectral density.

2. The method according to claim 1, wherein The method for preprocessing the EEG signals includes: Performing a first filtering process on the EEG signals by using a notch filter; Performing a second filtering process on the EEG signals after the first filtering process by using a band-pass filter; Performing a first artifact reduction process on the EEG signals after the second filtering process by using the common reference averaging method; Performing a second artifact reduction process on the EEG signals after the first artifact reduction process by using the independent component analysis algorithm.

3. The method according to claim 1, characterized in that The method for preprocessing the EMG signals includes: Performing a first filtering process on the EMG signals by using a band-pass filter; Performing a second filtering process on the EMG signals after the second filtering process by using a notch filter; Resampling the EMG signals after the second filtering process so that the sampling frequency of the resampled EMG signals is the same as the sampling frequency of the EEG signals.

4. The method according to claim 1, wherein According to the preprocessed EEG signals and the preprocessed EMG signals, and using the following formula to calculate the cross-spectral density between the EEG signals and the EMG signals, Among which S xy (f) represents the cross - spectral density between the electroencephalogram signal and the electromyogram signal, X n (f) represents the Fourier transform of the electroencephalogram signal collected for the nth time, Y n (f) represents the Fourier transform of the electromyogram signal collected for the nth time, represents the complex conjugate of Y n (f), N is the total number of acquisitions, and f represents the frequency.

5. The method according to claim 1 or 4, characterized in that, The coupling strength between the EEG signals and the EMG signals at a specific frequency is the absolute value of the cross-spectral density.

6. The method according to claim 1, wherein The method further includes: performing min-max normalization processing on the coupling strength.

7. The method according to claim 6, wherein The method further includes: averaging all the collected coupling strengths after min-max normalization processing within any one of all frequency bands to obtain the average normalized coupling strength of the any one frequency band.

8. The method according to claim 7, wherein All frequency bands include a first frequency band, a second frequency band, and a third frequency band. The first frequency band is 8 - 13 Hz, the second frequency band is 14 - 30 Hz, and the third frequency band is 31 - 80 Hz.

9. A terminal device, characterized in that, The terminal device includes a processor and a memory connected to the processor, where The memory stores program instructions; The processor is configured to execute the program instructions stored in the memory to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program instructions, where the program instructions, when executed, implement the method according to any one of claims 1 to 8.

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