Exploring the mechanism of Xingnao Kaiqiao acupuncture method for ischemic stroke based on EEG dynamic changes: a study protocol of an exploratory,prospective,interventional trial

Gui-Ping Li,Lin-Na Wu,Kai Li,Fan Xu,Jia-Rong Wu,Li-Li Zhao,Hao-Long Guo,Ting-Wei Zhu

1First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China. 2National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion,Tianjin 300193,China. 3School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China.

Abstract Background: The Xingnao Kaiqiao acupuncture method, founded by Academician Shi Xuemin, has outstanding efficacy in the treatment of ischemic stroke and has been widely used at home and abroad. However, after half a century of animal experiments and clinical studies, clearly and intuitively revealing its therapeutic mechanism is still a great challenge for researchers. Therefore, this experiment is based on the combination of medicine and engineering to study the immediate effects of the acupuncture method in treating patients by recording the electroencephalography(EEG)activities of subjects during the acupuncture process and to further reveal the therapeutic mechanism at the brain level. Methods: This trial is an exploratory, prospective, single-arm interventional study involving a total of patients with ischemic stroke. Physicians will record EEG data from patients during acupuncture as the primary outcome indicator. After pre-processing the EEG data,researchers will use various methods to analyze the immediate effects of acupuncture to obtain brain effectiveness. Deep learning will then be used to identify acupoint stimuli and receive correspondence between the acupuncture effect and the brain’s internal state.National Institutes of Health Stroke Scale score before and after the acupuncture process will be used as the secondary outcome indicator. Conclusion: This is the first study protocol to apply dynamic changes in EEG to explore a range of mechanisms of action of acupuncture in the treatment of ischemic stroke. We propose a method to analyze EEG signals of acupuncture patients. The deep learning model will be applied for supervised training to obtain the compelling relationship between the acupuncture method and internal brain states, providing a new prospect for the digitization of traditional Chinese medicine.

Keywords:Xingnao Kaiqiao acupuncture method;ischemic stroke;electroencephalography;study protocol

Highlights

In this study protocol, we explore the relationship between the efficacy of the Xingnao Kaiqiao acupuncture method and EEG changes in ischemic stroke patients. The data are combined with machine learning and deep learning for model training to rapidly evaluate and identify acupuncture techniques. Moreover, EEG metrics of acupuncture treatment are obtained through multiple data analysis methods. At the same time, medical-industrial integration offers new prospects for the digitization of Traditional Chinese Medicine.

Medical history of objective

In the 1970s, academician Shi Xuemin of the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine proposed the “Xingnao Kaiqiao acupuncture method”for treating stroke,based on the classical books of Chinese medicine in China and the clinical experience of the hospital for many years. The acupuncture points are mainly in the Yin meridian and the Governor’s vein, and the main operation of the acupuncture technique is the “reducing method.”This method of acupuncture has changed the past practice of acupuncture, which was “only taking Yang meridians to treat paralysis.” It is innovative and standardized in the method and sequence of acupuncture, the direction and depth of acupuncture,and the technique and timing of acupuncture. This method emphasizes the quantitative specification of acupuncture techniques and has significantly improved the efficacy of acupuncture in the treatment of stroke disease and remarkably shortened the course of treatment.

Background

Stroke in China is characterized by high morbidity, mortality,disability, recurrence, and economic burden. Its incidence continues to increase and trend toward the younger generation, with a significant impact on patients, families, and society [1]. The guidelines list acupuncture as a level II recommendation for stroke treatment, with level B evidence[2].Ischemic stroke (IS)is one of the most common types of stroke.Academician Shi Xuemin discovered the theoretical and technical system of the Xingnao Kaiqiao (XNKQ)acupuncture method for IS and standardized the acupuncture prescriptions and quantitative studies of the technique [3]. The method has been listed as a nationally applicable technology project and is widely used at home and abroad [4, 5].

After half a century of animal experiments and clinical studies,researchers have revealed the mechanism of actions and clinical effects of XNKQ acupuncture for IS, including improving cerebral circulation,promoting neurological remodeling,reducing infarct area,regulating cerebral vascular diastolic function, and inhibiting inflammatory response [6, 7]. The above studies lacked real-time simulations of changes in brain activity or made it difficult to visualize the effects of acupuncture at the brain level.

Electroencephalography (EEG) can directly reflect the functional state of the brain by recording the EEG activities of nerve cells in the cerebral cortex and scalp [8]. As the EEG kinetic system is composed of multiple neurons, making them structurally and functionally interconnected, network theory has been naturally applied to study brain dynamics based on acupuncture-evoked EEG signals. The corresponding synchronized evolutionary dynamics of brain regions may lead to dynamical functional connectivity on a wide range of temporal and spatial scales [9, 10]. Therefore, the reconstruction of brain functional networks, based on the recorded EEG signals,provides a quantitative approach to characterize complex network dynamics and sheds light on the physiological mechanisms behind acupuncture treatments.

In recent years, deep neural networks have been found to be computationally powerful, and data mining inherits the information that can be directly applied to classification [11-13]. As a result, a growing number of researchers attempt to use deep learning algorithms to automatically analyze EEG data for classification tasks or feature extraction. Schirrmeister et al. presented a comparison between convolutional neural network (CNN) with different architectures [14]. The CNN algorithm architecture was also applied to differentiate pathological and healthy EEG signals and obtain the best reported results over the dataset with better accuracy (ACC). In addition, CNN is a powerful computing tool to extract the implicit features of data objects containing spatial information. These studies suggest that CNN is very promising for automatically discovering feature bands and extracting rhythm features, including temporal and spatial attributes of EEG, for acupuncture effect diagnosis.

Thus, we propose to integrate artificial intelligence technology,modern information processing technology, and medical data to explore the effect of acupuncture on IS patients’ brain activities through EEG, analyze EEG micro-states and the complex network of the brain, and construct a functional network model of the brain under the influence of acupuncture. Then, we apply a deep learning model for supervised training to identify acupoint stimuli and obtain the correspondence between the acupuncture method and the internal state of the brain,to provide new avenues for acupuncture mechanism research.

Study objectives

The objectives of this trial are as follows: first, to elucidate the immediate EEG effects of the XNKQ acupuncture method on patients with IS and to provide a scientific basis for clinical practice. Second,we apply micro-state analysis, complex network analysis and deep learning methods to explore the correlation between the XNKQ acupuncture method and EEG changes in patients with IS. We also discover a correlation between internal brain states and the acupuncture technique. Based on this, we want to provide a fresh approach to understanding the functioning of acupuncture.

Methods

Study design

The study is exploratory, prospective, and interventional, which will include 30 patients with IS who meet the study criteria as subjects and apply the XNKQ acupuncture method. EEG will be dynamically recorded in subjects during acupuncture, and the immediate clinical effects of acupuncture will be observed using the National Institutes of Health Stroke Scale(NIHSS)scale.The EEG analysis will be performed in collaboration with Tianjin University, which has a rich theoretical research base in EEG data analysis and feature-extraction, deep learning theory, equipped with an EEG signal acquisition system,providing the necessary foundation for the experiment. A flow diagram of the trial is shown in Figure 1[15].

Study recruitment

The population is obtained from inpatients in the Third Ward of the Clinical Department of Acupuncture and Moxibustion of the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine (TCM). Subjects will be enrolled by physicians based on the principles of voluntary, informed and consent. The trial has been conducted in accordance with the Declaration of Helsinki principles and was registered with the Chinese Clinical Trials Registry (ChiCTR 2100050098) and approved by the Ethics Committee of the First Teaching Hospital of Tianjin University of TCM, China (approval number: TYLL2021[K]040). Participants were required to sign an informed consent form prior to participation and will be allowed to withdraw from the trial at any time during the trial, with or without providing reasons. Any protocol modifications or other changes will require the hospital ethics committee’s approval and the co-investigators consent. The First Teaching Hospital of Tianjin University of TCM is also known as the First Affiliated Hospital of Tianjin University of TCM.

Figure 1 Flow diagram of the trial.XNKQ, Xingnao Kaiqiao; EEG, electroencephalography.

Recruitment of IS patients for this study began on August 25, 2021.Due to the impact of the epidemic, only 21 subjects completed this study by November 2022 after passing an ethical review. The recruitment deadline is subsequently extended from December 2021 to June 2023 due to damage to the EEG instrument. The trial leader has informed the members of this study group that the trial has been postponed.

Inclusion criteria

Aged 50 to 75 years. Meets the diagnostic criteria for ischemic stroke 16 and has the first episode of anterior circulation ischemic stroke confirmed by CT/MRI [16]. 10 to 30 days of stable, and conscious disease. NIHSS score of 4 to 15. Not receiving acupuncture or other electrical or magnetic stimulation within 24 hours. Patients and their families were informed of this study and signed an informed consent form.

Exclusion criteria

Diagnosis of transient ischemic attack, acute posterior circulation ischemic stroke,acute phase severe coma,severe brain herniation,and massive ischemic stroke. Presence of severe primary cardiac, hepatic,renal, and hematopoietic systems. Chronic obstructive pulmonary disease with severe cardiopulmonary dysfunction; abnormal blood pressure,systolic ≥ 180 mm Hg or ≤ 90 mm Hg,diastolic ≥ 110 mm Hg or ≤ 60 mm Hg. Persons with psychotic mania, cognitive impairment, and inability to cooperate with acupuncture treatment.Those who have taken psychotropic, sedative, or anti-epileptic drugs in the last 1 week.

The studied device

Raw EEG data for this experiment is collected by Neuracle 64-lead device. EEG amplifiers contain subjects’ brain activity from 64 Ag-Ag-C1 scalp electrodes with an EEG device sampling rate of 1000 Hz and a hardware filter with a sampling rate of 0.5-70 Hz.

Intervention

Operating conditions

We need to ensure the test environment is clean, comfortable, and quiet with a room temperature of 22 ± 2°C and will prohibit other radio waves (e.g., TV, mobile phones.) during the test. To minimize unnecessary human interference, the patients need to try to breathe evenly, refrain from talking and remain awake during the trial. The patient’s blood pressure, heart rate, respiration, and temperature will be recorded prior to the test, and if they are within normal limits, we will proceed with the trial. The researcher prepares appropriately in advance to avoid unnecessary errors: (1) Inform the patient of the reason and process of this examination to prevent nervousness; (2)Wipe the patient’s head with an alcoholic cotton ball for degreasing to reduce the resistance during the study; (3) Pay attention to the accuracy of the placement of electrodes and the correct position of the corresponding electrodes when placing them; (4) Avoid communication with others and minimize limb movements to reduce the occurrence of motion artifacts.

Intervention method

The XNKQ acupuncture method is used in this study. The practitioner will select the bilateral Neiguan (PC6), the affected side Renzhong(GV26), Sanyinjiao (SP6), Jiquan (HT1), Chize (LU5), and Weizhong(BL40) acupoints. They will determine the above points according to the names and locations of the acupoints specified in the National Standard of the People’s Republic of China(GB/T 12 346-2006) [17].

Acupuncture operations

These procedures will be performed by clinicians who are proficient in XNKQ acupuncture methods.The needles will be Huatuo brand sterile disposable acupuncture needles (size: 0.25×40mm, Suzhou Medical Supplies Factory Ltd., Suzhou, China). The patients will be prone and will have the acupuncture site routinely disinfected.

First,the bilateral PC6 will be stabbed,straight for 0.5-1 cun(about 13-25 mm), using the twisting lifting-inserting reducing method for 60s. Needle sensation is transmitted to the upper arm. GV26 is then stabbed, and 0.3 to 0.5 cun (about 8-13 mm) is stabbed obliquely toward the nasal septum, using the bird-pecking reducing method to the extent that the patient tears or the eyes are moistened. After that,SP6 is stabbed, 1 to 1.5 cun (about 25-40mm) along the medial edge of the tibia at 45° to the skin, using the lifting-inserting reinforcing method for 60s, causing the lower limbs to twitch three times. HT1 is performed by moving the original point 1 cun (about 25mm) down along the meridian, avoiding the axillary hairs, and stabbing direct 1-1.5 cun (about 25-40mm), using the lifting-inserting reducing straight, using the lifting-inserting reducing method, causing the patient’s forearm and fingers to twitch three times.For BL40,the point is taken supine with the straight leg elevated and stabbed 0.5 to 1 cun(about 13-25mm) straight, using the lifting-inserting reducing method, causing the lower limb on the affected side to twitch three times.

All acupuncture points are subjected to standardized quantitative acupuncture such as lifting,pricking,twisting,and spinning to achieve“deqi” (sensations of soreness, numbness, swelling, and heaviness).Needles will be held for 30 minutes per treatment. The specific descriptions of the acupuncture techniques and related parameters are shown in Table 1.

The acupuncture process is divided into four stages (Figure 2): (i)pre-acupuncture stage (T0): the subject rests and remains awake for 10 minutes;(ii) “deqi”stage of needle manipulation(T1):a disposable needle is inserted into the selected acupuncture point at a certain depth to allow the subject to obtain a “deqi” feeling, and the corresponding acupuncture technique and stimulation time are applied; (iii) retain the needle stage (T2): retain needles for 30 minutes after performing acupuncture techniques; (iv)post-acupuncture stage (T3): subjects remains supine and rests after the start of the acupuncture for 10 minutes.method for the 60s, with three pumps on the affected upper limb. For LU5, the elbow is bent at 120°and 1 cun (about 25mm) is stabbed

Outcome measurements and data collection

Physicians will collect baseline data on the age, gender, occupation,existing medical condition, imaging data, and medication use of the patients.

The primary outcome indicators are EEG data obtained from the patient’s T0, T1, T2, and T3 phases by a physician with specialized training. Patients rest supine with a Borealis 64-lead EEG worn on the head to record the patient’s EEG signals at all stages continuously.

The secondary outcome indicator is the patient’s NIHSS score at T0 and T3, which will be independently evaluated and recorded by a physician working in neurology. Outcome measurement and data collection process for this trial (Table 2).

Statistical analysis

Sample size estimation

In a previous exploratory study, academic Julius et al. analyzed and determined that 12 participants per group were an appropriate sample size for a feasibility study [18]. Meanwhile, another study calculated that 15 participants per group might be sufficient to produce an equivalent effect [19].

Figure 2 Illustration of the acupuncture process

Table 1 Traditional descriptions and mathematical language representations of acupuncture techniques

Table 2 Outcome measurement and data collection process for this trial

Based on the good clinical efficacy of the XNKQ acupuncture method for IS, this study explored the correlation between the effects of the acupuncture method and EEG changes in patients with IS to reveal the therapeutic mechanism of acupuncture from the level of central nervous systems. We considered the minimum clinical sample size and the number of subjects required for previous computer modeling, and therefore the trial selected 30 participants for the study[20-22].

EEG signal data analysis

The preprocessing of EEG signals will be performed via MATLAB,especially EEGLAB. Besides, the characteristics of EEG signals will be investigated via Python. The specific processing steps are as follows:

The researchers will select the acquired real-time EEG signal.

EEG signal pre-processing. A zero-phase shift filter will be used to limit the spectral range to 1 to 45 Hz to filter out the 50 Hz IDF interference. Artifacts in blinking and muscle movements will be removed using the fastICA method based on the negative entropy approximation.The most significant noise interference signals that are difficult to filter out by the above two methods are removed by manual visual observation. Meanwhile, we will apply generative adversarial network to enhance the EEG data during data pre-processing.

Micro-state analysis. First, the EEG signal will be band-pass filtered from 2 to 20 Hz based on pre-processing, and the filtered EEG data of 2 seconds will be extracted. Second, the global field power of the EEG signal for 2 seconds length will be calculated. The raw EEG geopotential maps of the corresponding moment points at the global field power peak points will be extracted.These raw EEG geopotential maps will be clustered using the K-means clustering algorithm to obtain microstate templates. Finally, the states of each moment of the complete EEG sequence will be correlated with each microstate template to obtain the microstate sequence. The characteristic parameters of each microstate class of the brain will be calculated,including the average duration, frequency per second, microstate occupancy ratio, and state transition probability.

Graph theory metric analysis of brain neural network. Based on the brain matrix, the topology of neural network can be characterized by graph theory parameters.The clustering coefficient Ci of node i will be defined as the number of edges between the direct neighbors of node i divided by the number of possible edges, which the researchers define as:

WhereEiis the actual number of edges between neighboring nodes of node i, and N is the number of nodes within network. Global efficiency can be defined as:

Local efficiency will be calculated to measure the segregation property of network. Local efficiency of network will be the average of local efficiency of all nodes, which can be calculated as:

Small world network index can be defined as:

The final small world network index will be calculated by an average of 1000 random network implementations, which will be more conducive to information transmission among nodes.

Complex network analysis. A dynamic brain functional connectivity matrix will be constructed by calculating the dynamic coherence between leads from EEG data: a sliding time window of 3s and a step value of 1s are used to extract the brain functional connectivity matrix, and the corresponding network parameters such as clustering coefficient, characteristic path length, small world degree, global efficiency, and local efficiency will be calculated.

Deep learning method. The diagnosis of acupuncture effect on the brain has been a thorny clinical problem due to the lack of reliable biomarkers. In this section, we will provide spatial distributions to microstates as inputs to a CNN. A visualization technique called gradient-weighted class activation mapping will be used to extract the state of acupuncture.

CNN architecture and training strategy: Due to the limited number of input samples, the architecture of the CNN model has been dramatically simplified to avoid over-fitting in classical applications.CNN models are mostly three-layer convolutional layer structures[23-25]. Therefore, this work will design three convolutional layers for the hidden layer of CNN models.During the continuous debugging of the architecture of the CNN model, some widely used layers, such as the pooling layer, dropout layer, or batch normalization layer, will be omitted because they will generate excessive model parameters,leading to over-fitting of the model and reduced model generalization[26-28].In addition, to improve the convergence speed of the model,this work will add layer normalization between convolutional layers.In general, the architecture of the CNN model will be significantly simplified and components will be retained in order to fit the number of data samples in this work. To achieve an intuitive representation of the data distribution, we will add two full-connect layers before the‘Softmax’ layer of the CNN model.

We will set the training data for the network training procedure as acupuncture and standard groups. Then, the training data will be divided into the training set and the test set in the ratio of 8:2. In the training process, seven subsamples will be used as training data in each calculation, and the remaining one will be reserved as validation data to test the model. This process will be repeated eight times so that all eight subsamples will be involved in the training and testing phases. We will evaluate the performance of the models by randomly dividing all the samples in the training set into eight equal sub-sample sets to form eight-fold cross-validation. Nadam optimizer with a learning rate of 0.001 will be used [29]. To evaluate the overall performance of the CNN models, ACC and area under the curve and the ACC and area under the curve will be obtained on the held-out fold.

The relationship between the effect and the internal state will be explored through the obtained acupuncture effect, which will be used as feature input and the internal state as output,using the constructed deep learning model for supervised training (Figure 3). Statistical analysis will be carried out by IBM SPSS 25.0 software and MATLAB software for characteristic parameters.

NIHSS score analysis

NIHSS score data will be expressed as mean±standard deviation,and for normally distributed numerical variables, paired t-tests will be used when comparing NIHSS scores before and after acupuncture. For non-normally distributed numerical variables, the Wilcoxon signed-rank sum test will be performed as a non-parametric test. Data will be tested using a two-sided test, andp-value ＜0.05 will be considered statistically significant. The above data analysis will be performed using IBM SPSS Statistics V.25 software for statistical purposes.

Moreover, a general linear model will be used to explore the correlation between micro-state parameters within the clinical scale and multiple patient attributes. The schedule of this study protocol is shown in Figure 4.

Figure 3 EEG effect analysis scheme for IS treated with XNKQ acupuncture method. XNKQ, Xingnao Kaiqiao; IS, ischemic stroke; EEG,electroencephalography.

Figure 4 The schedule of this study protocol.EEG, electroencephalography.

Safety assessment

Adverse acupuncture events (SAE) include dizziness, thick needles,bent needles, broken needles, abnormal needling sensations,subcutaneous hematoma, and bleeding. Documentation of SAE should include the name, severity, start time, end time, measures taken, and referral information to assess the occurrence of adverse acupuncture reactions. In case of serious SAE (e.g., requiring hospitalization,resulting in disability or impaired workability.), the investigator should immediately report to the Medical Ethics Committee of The First Teaching Hospital of Tianjin University of TCM. The clinical trial should then be stopped until further instructions are provided.

Data registration and Quality control

Prior to the trial, uniform training will be provided to all relevant personnel to understand the purpose and content of the trial (e.g.,diagnosis of ischemic stroke, inclusion and exclusion criteria,intervention procedures,and outcome measures) to ensure the quality of the trial. A single physician will perform all needling. Under the supervision of the field investigator participating in the study, the practice or clinical personnel will fill in the data into the Case Report Form, which will truthfully record patient withdrawal, reasons for withdrawal and establish the corresponding trial database.Throughout the trial, data quality is strictly monitored at three levels.

Confidentiality

All information concerning the patient will be kept strictly confidential to the extent permitted by law. The investigator, the supervisor, the ethics committee, and the higher administration will be granted access to the patient’s medical records related to this study to confirm the authenticity and accuracy of the information collected in this study, but not the patient details. The patient names will not appear in public materials or reports related to this study.

Patient and public involvement

Patients or the public were not involved in our research design,conduct, reporting, or dissemination plans.

Discussion

Acupuncture is the treasure of Chinese traditional medicine and is effective in treating pain, inflammatory reactions and strokes [30].XNKQ, a unique acupuncture method for ischemic stroke rehabilitation, is outstanding and effective in improving neurological function and restoring the ability to perform daily [31-33].

EEG can objectively respond to the brain activities of patients with IS and contribute to the early diagnosis and prognosis of IS [34, 35].We use EEG to dynamically depict the effect of XNKQ acupuncture method on the electroencephalographic activity of IS patients, which may reveal the mechanism of acupuncture from the neuroimaging.

Current EEG studies have primarily focused on observing frequency,rhythm, amplitude, and waveform [36]. Visual simulation studies of functional changes in the brain of IS patients after acupuncture are lacking. Therefore, this study proposes to use microstate analysis,complex network analysis, and deep learning methods to process EEG data to explore the differences in the effects of various acupoints of the XNKQ acupuncture method on other individual brains producing acupuncture effects[37-39].

Stratification using microstate analysis allows for the study of transient steady-state characteristics of brain activity on small time scales [40, 41]. The analysis yields parameters that characterize each microstate class of the brain. These include the average duration,frequency per second, state occupancy ratio, and the probability of interconversion between microstates. It is commonly used to characterize transient brain activity with spatiotemporal properties.Complex network analysis is a powerful method for exploring functional and structural properties of the brain from both dynamic and static aspects, respectively [42, 43]. Based on the functional connectivity matrix, the brain network functions of patients with IS treated with different acupuncture stimulation will be reconstructed to obtain network parameters such as clustering coefficient,characteristic path length, small-world index, global efficiency, and local efficiency. The characteristics of acupuncture treatment for IS under multiple frequency bands are further analyzed [44, 45]. The mechanisms of brain function changes at different acupuncture stages of the effect will be analyzed according to the network parameters of other groups.Finally,we will use the features of obtained acupuncture effect as input to our pre-trained deep learning model for supervised training, to produce the results of relationship between the acupuncture effect and its internal state as an outcome of this training.[14, 46-48].

We also assess the immediate clinical effects of acupuncture using the NIHSS scale. We study the immediate effects and mechanisms of acupuncture in treating IS by the XNKQ acupuncture method. This protocol describes the first prospective trial using EEG to observe changes in brain function in patients with IS treated with XNKQ acupuncture. We anticipate that this study will elucidate the mechanism of the effectiveness in treating IS.

This study uses various advanced techniques to reduce the interference factors that affect EEG [49]. Although scalp EEG is easy to acquire, it has a low signal-to-noise ratio and is susceptible to external environmental agitation interference [50]. Improvements in experimental methods include checking equipment in advance,reducing other noise and light interference, maintaining a constant room temperature, and applying conductive paste to reduce impedance. These methods can remove non-physiological artifacts generated by the external environment and the equipment itself.Electrodes record EOG and EMG signals, and the interference of these signals will be subtracted in the subsequent data pre-processing part to remove physiological artifacts.

The pre-processing includes filtering, fastICA analysis, and manual visual observation.The filtering process can remove noise interference and improve the signal-to-noise ratio by limiting the range of the band signal [51]. The fastICA analysis method will also sort out the individual signal sources from the mixed EEG and further process the filtered information[52].Manual visual observation removes the most significant noise interference signals that are difficult to filter out by the two methods above.

In the conclusion, the limitations of this study protocol need to be acknowledged. This study is an exploratory trial, which only observed the dynamic changes of EEG activity in subjects with IS by the XNKQ acupuncture to reveal its mechanism of action at the brain level and lacked controls over the quantification of different stimuli.Furthermore,the findings of this study are only for IS patients aged 50 to 75 years with NIHSS scores of 4 to 15. Researchers in subsequent trials may expand the study by adjusting the inclusion and exclusion criteria to draw more generalizable conclusions.