2.1. Subjects
Thirty young volunteers (21 ± 1 years old, male: female = 8: 7) from Soochow University participated in this walking experiment. All volunteers were right-handed, in good health, and had no history of mental illness or cerebrovascular disease. All of them participated in this walking experiment for the first time, and all volunteers signed informed consent before the experiment.
2.2. Experiment Procedure
The experiment mainly includes two types of gait adjustments, the first type is the step adjustment, and the other type is the speed adjustment. In order to avoid the influence of the sequence of experiments, 30 young volunteers were randomly divided into two groups. The first group performed the step adjustment experiment before performing the speed adjustment experiment. In the second group, the speed is adjusted first and then the step is adjusted. Among them, step adjustment includes two states: step increase and step reduction; speed adjustment includes two states: speed increase and speedreduction. That is, there are four gait adjustment modes. The walking experiment was performed in the corridor of a laboratory. The experimental environment is shown in Figure 1.a. The experimental walking distance was 21m. Each participant walked 10.5m in a natural state (step speed range: 0.8-1.2m / s, step size range: 50-65cm), adjusted the gait at 10.5m, and the remaining 10.5m finished in the adjusted state. This requires that the subject's adjustment range when the speed or step size changes are significantly different from the normal walking. The range of gait parameter changes is about half of the speed or step size in the natural state. All subjects performed 2-3 times of pre-walking experiments before the experiment. A marking line is set at 10.5m. When the participants start the walking task, walk to the middle marking line and end the walking task, the operator should use fNIRS software to mark a Mark respectively. Participants went from one end to the other to complete a state adjustment, which was defined as a task. After completing one walking task, participants were required to take a break and turn to prepare for the next walking task. The rest time after each turn was not less than 45s. The specific length of the rest period was controlled by the subjects themselves. Each participant has to perform four types of gait adjustment tasks: speed increase, speed reduction, step increase, and stepreduction. The experimental process is shown in Figure 1.c. When subjects adjusted the walking speed, the step size remained the same. Similarly, when the participants adjust the step size, the walking speed is unchanged, and each gait adjustment task is performed twice.
Different from previous experiments on treadmills, in order to achieve walking tasks in the natural environment, this experiment did not deliberately require specific walking speed and step size. The experiment operator does not issue any instructions except the two commands "experiment start" and "experiment end" at the beginning and end of the experiment. The start, end, and rest time of the experiment are completely controlled by the subjects. All participants will perform pre-walking experiments 2-3 times before the actual experiment begins to grasp the corresponding start-stop and rest periods. In addition, we pay more attention to the changes in blood oxygen activation of the subjects before and after adjusting the gait. Therefore, the subjects are required to maintain their most natural walking state in the first stage of walking and must have a clear gait adjustment in the second stage.
2.3. NIRS imaging
The near-infrared brain imaging device used in this paper is a portable near-infrared brain imaging device (LIGHTNIRS)[24] of Shimadzu Corporation, Japan, as shown in Figure 1.b. The test wavelength is 780nm, 805nm, and 830nm, and the sampling frequency is 13.33Hz. During the experiment, the participants only need to walk naturally with the equipment on their backs.
LightNIRS has 8 detectors and 8 emitters. Two 2 × 4 areas are measured before and after the experiment. The first area is used to measure the PFC, and the second area is used to measure the SMA and PMC. Each brain area requires 4 detectors and 4 emitters, and a complete head holder is used to hold the detector and transmitter. The experiment is based on the 10-20 system[25] to locate the brain area. The specific hood layout is shown in Figure 2. Cz in the figure represents the vertical intersection of the anterior-posterior sagittal line and the left-right sagittal line of the human brain. Among them, the anterior-posterior sagittal line is the line connecting the nasal root and the posterior occipital bone, while the left-right sagittal line is the line connecting the left earlobe to the right earlobe. Channels 1-10 in the first area measure the blood oxygen content of the PFC, channels 11, 14, and 18 in the second area measure the left PMC, channels 13, 17, and 20 measure the right PMC, and 12, 15, 16, and 19 channels measure SMA. According to the Brodman brain area distribution, the distance between Cz and 19 channels is 30mm, and the distance between adjacent emitters and receivers is also 30mm. During the actual wearing of the headgear, due to the difference in the size of the skull of each subject, the area of each functional area of the brain differed. Therefore, it is necessary to use corresponding mathematical methods to reduce this difference in the subsequent data processing.
2.4. Data analysis
Walking tasks often cause motion artifacts and physical noise to be included in brain signals. At the same time, the continuous data monitoring process may cause zero drift [25]. In order to reduce these effects, combined filtering is used in this paper to remove related noise. According to the literature[26], different physiological information contained in oxyhemoglobin is distributed in different frequency bands, such as heart rate effect (0.6 ~ 2.0Hz), respiration effect (0.145 ~ 0.6Hz), and myogenic effect (0.052 ~ 0.145Hz), neurological effect (0.021 ~ 0.052Hz), endothelial cell metabolism activity (0.0095 ~ 0.021Hz) and endothelial cell activity (0.005 ~ 0.0095Hz). The neural activity of the human brain dominates gait adjustment intention during walking,so it is hoped that the corresponding frequency band range that reflects nerve activity in the hemoglobin information of the brain is extracted, that is, the frequency band less than 0.145Hz[27, 28].AChebyshev low-pass filter[29] with a second-order and a maximum ripple gain of 1dB in the passband is used to filter out noise caused by breathing and heartbeat, and the cutoff frequency is set to 0.145Hz. Then, a mathematical morphological filter (MMF) is used to eliminate baseline drift while maintaining the main morphology of blood oxygen signals. MMF can be expressed by the following formula:
![](data:image/png;base64,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)
represent morphological open filtering and morphological closed filtering, which are composed of corrosion and expansion[30].
In order to reduce the impact of different individuals' skull differences, the regions of interest (ROI) divided in this study are shown in Table 1. In order to reduce the impact of noise such as body motion, and to find the highest commonality in walking tasks for the brain region, using the weight method to calculate the overall blood oxygen signal of the ROI brain region. The calculation formula is as follows:
![](data:image/png;base64,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)
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABMAAAAWCAYAAAAinad/AAAAr0lEQVQ4Ee2S7Q2EIAyGO1cH6jydpst0mPdSwGAQqXfHT02MEeXh/Shh40UbWXhh36eZZGYQIlDcYoVu0t5Z4cN5CQyACYgEFQXAFUwMHUnAkzZDXd8cyprIQdcjWIgjsDpcuTwvlLaQ24wfi9We21+wUEQnq7/DQpVYajEOSGwa5BiB0uKp1Ym8Bcyh3FsE6szdNblQ1of12Fxzi4G9V7dQNvGRLL2wJKDJ562ZfQAL1DXQXLT85AAAAABJRU5ErkJggg==)
represents the matrix of the original channel blood oxygen signal composed of M channels and N sampling points, and
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABMAAAATCAYAAAByUDbMAAAAnklEQVQ4Ee2R6w0EIQiEqYuCqIdqaMZiZoMbwEduc5vsn82diREd/RiE8OCgB1l4FaxBWWBj/SYgmU5GNeOlTAcRWFteOINP5/O1GeYOqFyZENJQU3BqBsm4gDuMFeFrgsEBjM10sZZu9uzxYCltcO1JaEgavNkZgKYMIuqT1fofxj5LdpffwCLL9fqHXf/Pqp5drmaUvnWzpPvRj8AOSsFNBlzirP4AAAAASUVORK5CYII=)
represents the weight of each channel calculated from the blood oxygen concentration information entropy of each channel[31].
Table 1
The divide of the ROI brain
The number of ROI
|
The name of ROI
|
Channel number included
|
1
|
L-PFC
|
1, 4, 8
|
2
|
M-PFC
|
2, 5, 6, 9
|
3
|
R-PFC
|
3, 7, 10
|
4
|
L-PMC
|
11, 14, 18
|
5
|
SMA
|
12, 15, 16, 19
|
6 |
R-PMC
|
13, 17, 20
|
The letters L, M, and R represent Left, Medial, and Right respectively. For example, L-PFC indicates the left part of the PFC. |
First, four different gait adjustment tasks are uniformly defined as gait adjustment tasks. In order to analyze the differences in the activation of brain areas when subjects perform gait adjustment tasks, it is necessary to predict in advance which specific gait adjustment intention. Previous studies have shown that[7, 32], lower limb motor intention can usually be detected 0.5-2s in advance.The blood oxygen data within 2 seconds before and after the Mark position marked during the experiment is selected for analysis. The sliding-window method is used to calculate the time-domain characteristics of the blood oxygen signal to find the difference in the distribution of blood oxygen activation of the ROI in the two states. Among them, the window length is set to 40 sampling points, and the step size is set to 1 sampling point.
After finding the turning point of gait adjustment, in order to further distinguish the four states of speedincrease, speed reduction,step increase, and step reduction, we calculated the original signal of blood oxygen concentration and the rate of change of blood oxygen concentration in the ROI. The concentration change rate is calculated as dxi = xi+1-xi, where xi represents the blood oxygen data at the i sampling point in the time window. Similarly, xi+1 represents the blood oxygen data at the i+1 sampling point in the time window. Then calculate the Pearson correlation coefficient as the feature score for each of the ROI of the original signal and the blood oxygen difference signal. A feature score corresponds to two brain regions, and it is added to the corresponding ROI. All feature scores follow this operation to get the cumulative feature score of each ROI. After normalization, observing the spatial distribution of the correlation characteristics of the brain area under the different state adjustment states.
All analyses and calculations in this article are run under Windows 10 system (I5-8250 CPU and 8G memory). The analysis software is Anaconda3-4.3.1 with python 3.6. In order to statistically compare the validity of the results of blood oxygen activation in the ROIbetween the normal walking task and the gait adjustment task, this paper uses the function 'ttest_ind' of the 'stats.scipy' in the python package to calculate the significance level pvalues.Each t-test contains 30 sample data. If p values less than 0.05 indicate rejection of the null hypothesis.