A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.


Introduction
BCI is a direct communication channel established between the brain and external devices [1]. BCI provides people another way to communicate with the outside world. People can communicate without using language and action but can express their thoughts and control equipment directly through EEG signals. This also provides a more flexible way of information exchange for the development of intelligent robots in the future [2]. As a complex platform connecting bio-intelligence systems and artificial intelligence systems, BCI provides great help in the study of EEG signals. In recent years, along with the human's deeper understanding of the brain, significant advances have been made in the study of artificial intelligence techniques to simulate and extend brain function, such as deep learning, neural chips, and large-scale brain-like calculations. Especially because of the advancement of neuro-brain interface technology, human-machine hybrid intelligence with a fusion of machine intelligence and bio-intelligence is considered as the ultimate goal of the future evolution of artificial intelligence. [3,4]. The human brain is an extremely complex system. It is an important research topic in the field of neurology to study the thinking mechanism of human beings to realize the exchange of information between the nervous system and the surrounding environment. The human brain electrical signal comprehensively reflects the thinking activities of the brain in the nervous system and is the main basis for analyzing the brain conditions and neural activities. EEG is a good indicator of The three experimental levels designed in this experiment are low load, moderate load, and overload. Different load levels are set by the occurrence probability of different subtasks. In order to balance the practice and fatigue effect, the experiment uses the Latin square design to set the experimental processing sequence, balance the experimental conditions, and cancel out the sequence error caused by the influence of the sequence of experimental processing. The experimental task design is shown in Table 1. The three experimental levels designed in this experiment are low load, moderate load, and overload. Different load levels are set by the occurrence probability of different subtasks. In order to balance the practice and fatigue effect, the experiment uses the Latin square design to set the experimental processing sequence, balance the experimental conditions, and cancel out the sequence error caused by the influence of the sequence of experimental processing. The experimental task design is shown in Table 1.

Low Load Moderate Load High Load
System monitoring task Monitor the status of the upper left system monitoring task, and click the corresponding position with the mouse to respond.
1 12 24 Tracking monitoring tasks Monitor the upper and middle tracking monitoring task status and position information, press the button to respond, control the joystick.
1 12 24 Communication monitoring task Monitor the right communication monitoring taskbar and upcoming communication tasks, and press the button to respond.
1 12 24 Resource management task Monitor the middle and lower resource management tanks A, B, C, D oil status and oil pump failure information. 1 12 24

Experimental Procedure
The resting task and three formal experiments with different loads are carried out in the order of the Latin square design. The experimental procedure is shown in Table 2.

Data Acquisition
The experimental data were collected in three aspects: subjective evaluation scale, operational performance measurement system, and physiological collection system.

Subjective Rating Scale
The subjective evaluation scale used in this experiment is the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) scale. In order to measure the value of the brain load from the actual feelings and viewpoints of the operators themselves, the subjects are evaluated in the six dimensions of mental power demand, physical strength demand, time demand, effort level, performance level, and frustration degree. The weights of each dimension are determined by a two-two comparison method, and the final mental load value is calculated by a weighted average method.
The statistical results show that the NASA-TLX score gradually increases with the increase of mental load in the experimental design. Analysis of variance by SPSS repeated measurements shows that the main effect of NASA-TLX scores is significant for brain load factors (F (2, 44) = 51.651, p < 0.001, η 2 P = 0.701). Post-hoc least significance difference (LSD) analysis shows that the low-load NASA-TLX score is significantly lower than the medium load (p < 0.001) and high load (p < 0.001), while the medium-load NASA-TLX score is significantly lower than the high load (p < 0.001). Considering the effectiveness of the NASA-TLX scale for assessing mental workload, it is shown that the workload task conditions in the experiment are well set-up.

The Operational Performance Measurement System
The flight performance data is automatically recorded by the MATB-II platform in the background. The recorded contents are the correctness rate and response time of each task which are used to evaluate the subject's understanding of the experiment requirements or the serious attitude to the experiment. The final analysis of the data is based on the operational performance.

Physiological Acquisition System
This experiment used the Neuroscan Neuamps system (Synamps2, Scan4.3, EI Paso, TX, USA) to acquire a 32-lead EEG signal at a sampling rate of 1000 Hz and recorded a 4-lead vertical and horizontal EOG as well. The layout of the 32-lead electrode is shown in Figure 2.

Data Acquisition
The experimental data were collected in three aspects: subjective evaluation scale, operational performance measurement system, and physiological collection system.

Subjective Rating Scale
The subjective evaluation scale used in this experiment is the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) scale. In order to measure the value of the brain load from the actual feelings and viewpoints of the operators themselves, the subjects are evaluated in the six dimensions of mental power demand, physical strength demand, time demand, effort level, performance level, and frustration degree. The weights of each dimension are determined by a twotwo comparison method, and the final mental load value is calculated by a weighted average method.
The statistical results show that the NASA-TLX score gradually increases with the increase of mental load in the experimental design. Analysis of variance by SPSS repeated measurements shows that the main effect of NASA-TLX scores is significant for brain load factors (F (2, 44) = 51.651, p < 0.001, η 2 P = 0.701). Post-hoc least significance difference (LSD) analysis shows that the low-load NASA-TLX score is significantly lower than the medium load (p < 0.001) and high load (p < 0.001), while the medium-load NASA-TLX score is significantly lower than the high load (p < 0.001). Considering the effectiveness of the NASA-TLX scale for assessing mental workload, it is shown that the workload task conditions in the experiment are well set-up.

The Operational Performance Measurement System
The flight performance data is automatically recorded by the MATB-II platform in the background. The recorded contents are the correctness rate and response time of each task which are used to evaluate the subject's understanding of the experiment requirements or the serious attitude to the experiment. The final analysis of the data is based on the operational performance.

Physiological Acquisition System
This experiment used the Neuroscan Neuamps system (Synamps2, Scan4.3, EI Paso, TX, USA) to acquire a 32-lead EEG signal at a sampling rate of 1000 Hz and recorded a 4-lead vertical and horizontal EOG as well. The layout of the 32-lead electrode is shown in Figure 2. Taking the subject 5 under low load as an example, part of the EEG raw data is shown in Figure  3, and it can be seen that the EEG signal is a non-stationary random signal. Taking the subject 5 under low load as an example, part of the EEG raw data is shown in Figure 3, and it can be seen that the EEG signal is a non-stationary random signal.

Data Analysis Method
In this section, the proposed EEG data processing and classification analysis methods are presented. First, the EEG data is preprocessed according to the two criteria of "high performance" and "fewer artifacts" for subsequent analysis; then feature extraction is performed by calculating the power spectral density and energy. After preprocessing and feature extraction of EEG signals, the importance rating of EEG is evaluated by calculating the Gini index, and the most sensitive band under different mental loads is selected. Finally, the EEG signals are classified by the SVM classifier according to the most sensitive band and full band data. A detailed introduction is described in the following sections.

Independent Component Analysis
(1) Principle The noise-free model of ICA used for eliminating artifacts is represented by Figure 4, and the relationship can be expressed by Equation (1):  (2) Description of the ICA issue Due to the statistically independent conditions of the source signal, it can be separated from the

Data Analysis Method
In this section, the proposed EEG data processing and classification analysis methods are presented. First, the EEG data is preprocessed according to the two criteria of "high performance" and "fewer artifacts" for subsequent analysis; then feature extraction is performed by calculating the power spectral density and energy. After preprocessing and feature extraction of EEG signals, the importance rating of EEG is evaluated by calculating the Gini index, and the most sensitive band under different mental loads is selected. Finally, the EEG signals are classified by the SVM classifier according to the most sensitive band and full band data. A detailed introduction is described in the following sections.

Independent Component Analysis
(1) Principle The noise-free model of ICA used for eliminating artifacts is represented by Figure 4, and the relationship can be expressed by Equation (1): Independent component analysis is to design a matrix W to find y = Wx and solve the independent components y. We remove y' by one or a part of the component of y, which is represented by y = py. Then x = Ay is restored, where x' is the useful signal left after we eliminate the interference. It can be seen from the model that ICA estimates the source component S i from the mixed signal x = (x 1 , x 2 , ..., x n ) and the mixing matrix A as well. It is based on statistically independent signals from different sources.

Data Analysis Method
In this section, the proposed EEG data processing and classification analysis methods are presented. First, the EEG data is preprocessed according to the two criteria of "high performance" and "fewer artifacts" for subsequent analysis; then feature extraction is performed by calculating the power spectral density and energy. After preprocessing and feature extraction of EEG signals, the importance rating of EEG is evaluated by calculating the Gini index, and the most sensitive band under different mental loads is selected. Finally, the EEG signals are classified by the SVM classifier according to the most sensitive band and full band data. A detailed introduction is described in the following sections.

Independent Component Analysis
(1) Principle The noise-free model of ICA used for eliminating artifacts is represented by Figure 4, and the relationship can be expressed by Equation (1):  (2) Description of the ICA issue Due to the statistically independent conditions of the source signal, it can be separated from the mixed signal, and the separated y(t) components are also independent of each other.  Due to the statistically independent conditions of the source signal, it can be separated from the mixed signal, and the separated y(t) components are also independent of each other.
Researchers in this field have proposed different criterias from different application perspectives. Currently, the criterias based on the minimization of mutual information (MMI) and the maximization of entropy are the most widely used. The basic theory is derived from the probability density function of independence. In actual work, the probability density is generally unknown and difficult to estimate. The commonly used method is to expand the probability density as a series and convert it into high-order statistics. The estimation of the high-order statistics, or the introduction of nonlinear links at the output to establish optimization criteria, also implies the pursuit of higher-order statistics. The principle of the criterion is explained by the minimum mutual information criterion.
The minimum mutual information criterion is defined as: Let y be an M-dimensional random variable, p(y) be its probability density, p(y i ) be the edge density of component I in y, then the mutual information entropy of y is defined as: p(y i ), I(p y ) = 0 when it is assumed that the signal source is independent, that is, the components are independent of each other. Therefore, the mutual information is extremely small as a criterion for independent components.
In practical applications, the Kullback-Leibler divergence between p(y) and used as a quantitative measure of the degree of independence: Obviously, I(y) ≥ 0, when each component is independent, I(y) = 0. Therefore, the direct form of the minimum mutual information criterion is: looking for B under y = bx, which is the minimum of I(y) of (3).
In order to be practically usable, the probability density in I(y) needs to be expanded into a series. Since the entropy of the Gaussian distribution is the largest in the probability density distribution with equal covariance, the Gaussian distribution of covariance is commonly used as a reference standard in the expansion. For example, when Gram-Charlier is expanded, there is: where P G (y i ) is a Gaussian distribution with the same variance (δ 2 = 1) and mean (µ = 0) as P(y i ), k 3 y i and k 4 y i are third-order and fourth-order statistics of y i , and h n (y i ) is n-order Hermit Polynomial.
(3) ICA algorithm solution Generally, the observed signal is whitened, and the similar principal component analysis method is used to obtain: In mode: In Equation (6)  To estimate n independent components, we need to run the algorithm n times. In order to guarantee the different independent components of each separation, it is only necessary to add a simple orthogonal projection operation to the algorithm-the orthogonalization of the mixed matrix columns.

Feature Extraction Method
Feature extraction is performed by calculating the power spectral density and energy of the EEG data. The corresponding power spectral density (PSD) can be calculated according to Equation (7).
where F*(n) is the conjugate of F(n) and N is the signal length. Therefore, according to the frequency band distribution of the EEG signal, four energy characteristics corresponding to the four bands are calculated by Equation (8).
The data is normalized using Equation (9).
where P freq refers to the power spectral density value at a certain frequency value.

Feature Selection Method
Gini Impurity Gini index: The probability that a randomly selected sample will be misclassified in a subset [28,29] where m is the number of classes, and f i indicates the probability of the samples belonging to the ith class. Usually, there are hundreds of features in a data set. Selecting several features that have the greatest impact on the results can reduce the number of features when building the model. Here, random forests are used to filter the features. The feature selection is based on the Gini index, and the greater the change in the Gini index before and after the feature selection means that the feature has a greater influence.
The Gini index is expressed by GI and can be calculated according to Equation (10). The variable importance measures are expressed by VIM, and the Gini coefficient score VIM j (Gini) of each feature Xj can be calculated according to Equations (11) and (12).
Algorithms 2019, 12, 145 where GI m , GI l , and GI r respectively represent the Gini index before branching and the Gini index of the two new nodes after branching in random forests.

Description
SVM is a classification model whose basic model is a linear classifier that defines the largest interval in the feature space. By incorporating a kernel function, the SVM can be a substantially nonlinear classifier. The learning strategy of SVM is to maximize the interval and can be formalized as a problem of solving convex quadratic programming. For the nonlinear SVM, the classification decision function is learned from the nonlinear classification training set, through the kernel function and soft interval maximization, or convex quadratic programming.
The f (x) obtained by Equation (14) is called a nonlinear support vector, and K(x, z) is a positive definite kernel function.

Kernel Function
The choice of SVM kernel functions is crucial for the classification performance, especially for linearly inseparable data. The kernel function chosen here is the Gaussian kernel function.
The corresponding support vector machine is a Gaussian radial basis function classifier. In this case, the classification decision function becomes

Data Preprocessing
In order to ensure the validity of the experimental data, only the experimental data of 10 subjects is left for subsequent analysis according to the two criteria of "high performance" and "fewer artifacts" [30]. The "high performance" criterion means that the operational performance accuracy rate should be greater than 0.90. "Fewer artifacts" means that the artifacts in the original EEG data cannot exceed 5 min. EEG signals are very weak, and EMG, EOG, and ECG can interfere with EEG signals, so samples with artifacts greater than 5 min are removed. After that, the ICA is used to remove the EOG artifacts from the valid 10 subjects, and then filter and reconstruct the reference.

ICA-Based EEG Signal EOG Elimination
The applicable conditions of ICA are: (1) There is no time delay for the linear combination of signals; (2) the time course of the source is independent; and (3) the number of sources is smaller than or equal to the number of sampling points. EEG acquisition can be regarded as linear and immediate, by satisfying condition (1). Condition (2) is also reasonable, because it can be considered that EEG, ECG, EEG, etc., have different sources. Condition (3) is a bit fuzzy because we do not know the number of sources that are statistically independent of scalp potential, but a large number of ICA algorithm simulations can effectively separate a large number of time-dependent sources of EEG or brain topography.
We input the EEG signal (x) as the data sampled from different electrodes and x = As. By the fast fixed-point ICA algorithm we can find A, s. Then the artifacts can be eliminated to get s'.
Through (16), the EEG signal x' can be obtained after removing the eye electricity. Figure 5 shows the EEG topographic map of the subject 5 under low load, moderate load, and overload before the artifact is removed.
Algorithms 2019, 12, x FOR PEER REVIEW 10 of 18 algorithm simulations can effectively separate a large number of time-dependent sources of EEG or brain topography. We input the EEG signal (x) as the data sampled from different electrodes and x = As. By the fast fixed-point ICA algorithm we can find A, s. Then the artifacts can be eliminated to get s'. 1 ' ' x W s   (16) Through (16), the EEG signal x' can be obtained after removing the eye electricity. Figure 5 shows the EEG topographic map of the subject 5 under low load, moderate load, and overload before the artifact is removed.     Figure 7 shows the EEG topographical map of the subject 12 under low load, moderate load, and overload before using ICA to remove artifacts.  algorithm simulations can effectively separate a large number of time-dependent sources of EEG or brain topography. We input the EEG signal (x) as the data sampled from different electrodes and x = As. By the fast fixed-point ICA algorithm we can find A, s. Then the artifacts can be eliminated to get s'. 1 ' ' x W s   (16) Through (16), the EEG signal x' can be obtained after removing the eye electricity. Figure 5 shows the EEG topographic map of the subject 5 under low load, moderate load, and overload before the artifact is removed.     Figure 7 shows the EEG topographical map of the subject 12 under low load, moderate load, and overload before using ICA to remove artifacts.     It can be seen from Figures 6 and 8 that the individual brain activities are distinct under different load conditions.

Four Bands of EEG Data
Numerous studies have shown that human brain states are related to the α, β, θ, and δ bands. α band: The electromagnetic wave frequency is between 8~13 Hz, and the amplitude is between 30~50 μV. This periodic wave is produced in the parietal lobe and occipital region of the brain in the state of consciousness, quietness, or rest.
β band: The electromagnetic wave frequency is between 14~30 Hz, and the amplitude is between 5~20 μV. This activity occurs in the frontal area when people are awake and alert.
θ band: An electromagnetic wave with a frequency between 4 and 7 Hz with an amplitude of less than 30 μV. This activity occurs mainly in the parietal and temporal regions of the brain.
δ band: The electromagnetic wave frequency is between 0.5~3 Hz, and the amplitude is between 100~200 μV. This activity occurs during deep sleep, unconsciousness, anesthesia, or hypoxia.

Feature Extraction Based on Power Spectral Density and Energy
To minimize the time effects, EEG data of the middle 5 min is selected. Segmentation processing, fast Fourier transformation, power spectrum estimation, and energy calculation are performed in sequence. When segmentation is performed, the length of each segment is 1 s. Based on the idea of     It can be seen from Figures 6 and 8 that the individual brain activities are distinct under different load conditions.

Four Bands of EEG Data
Numerous studies have shown that human brain states are related to the α, β, θ, and δ bands. α band: The electromagnetic wave frequency is between 8~13 Hz, and the amplitude is between 30~50 μV. This periodic wave is produced in the parietal lobe and occipital region of the brain in the state of consciousness, quietness, or rest.
β band: The electromagnetic wave frequency is between 14~30 Hz, and the amplitude is between 5~20 μV. This activity occurs in the frontal area when people are awake and alert.
θ band: An electromagnetic wave with a frequency between 4 and 7 Hz with an amplitude of less than 30 μV. This activity occurs mainly in the parietal and temporal regions of the brain.
δ band: The electromagnetic wave frequency is between 0.5~3 Hz, and the amplitude is between 100~200 μV. This activity occurs during deep sleep, unconsciousness, anesthesia, or hypoxia.

Feature Extraction Based on Power Spectral Density and Energy
To minimize the time effects, EEG data of the middle 5 min is selected. Segmentation processing, fast Fourier transformation, power spectrum estimation, and energy calculation are performed in sequence. When segmentation is performed, the length of each segment is 1 s. Based on the idea of

Four Bands of EEG Data
Numerous studies have shown that human brain states are related to the α, β, θ, and δ bands. α band: The electromagnetic wave frequency is between 8~13 Hz, and the amplitude is between 30~50 µV. This periodic wave is produced in the parietal lobe and occipital region of the brain in the state of consciousness, quietness, or rest.
β band: The electromagnetic wave frequency is between 14~30 Hz, and the amplitude is between 5~20 µV. This activity occurs in the frontal area when people are awake and alert.
θ band: An electromagnetic wave with a frequency between 4 and 7 Hz with an amplitude of less than 30 µV. This activity occurs mainly in the parietal and temporal regions of the brain.
δ band: The electromagnetic wave frequency is between 0.5~3 Hz, and the amplitude is between 100~200 µV. This activity occurs during deep sleep, unconsciousness, anesthesia, or hypoxia.

Feature Extraction Based on Power Spectral Density and Energy
To minimize the time effects, EEG data of the middle 5 min is selected. Segmentation processing, fast Fourier transformation, power spectrum estimation, and energy calculation are performed in sequence. When segmentation is performed, the length of each segment is 1 s. Based on the idea of the averaging period method, half of the adjacent segments data is overlapped making the EEG characteristic curve smoother. Then, the Fourier transformation is performed on each segment to obtain F(n) (n = 1, 2, ..., 1000), and the corresponding frequency and amplitude.

Feature Selection Based on Gini Impurity
The 120-dimensional characteristic distribution of EEG data is: 1-30 dimension is the α band of each electrode point, 31-60 dimension is the β band of each electrode point, 61-90 dimension is the θ band of each electrode point, 91-120 dimension is the δ band of each electrode point. Taking the subject 22 as an example, the importance distribution of the 120-dimensional features in the EEG data calculated by Gini's impurity is shown in Figure 9. the averaging period method, half of the adjacent segments data is overlapped making the EEG characteristic curve smoother. Then, the Fourier transformation is performed on each segment to obtain F(n) (n = 1, 2, ..., 1000), and the corresponding frequency and amplitude.

Feature Selection Based on Gini Impurity
The 120-dimensional characteristic distribution of EEG data is: 1-30 dimension is the α band of each electrode point, 31-60 dimension is the β band of each electrode point, 61-90 dimension is the θ band of each electrode point, 91-120 dimension is the δ band of each electrode point. Taking the subject 22 as an example, the importance distribution of the 120-dimensional features in the EEG data calculated by Gini's impurity is shown in Figure 9.
(a) Importance score (b) Ranking of importance It can be seen from Figure 9 that the features with higher importance are concentrated in the β band of each electrode point, and the β band plays a major role in the four bands. The results obtained by performing the same feature selection for the remaining subjects are shown in Figure 10. It can be It can be seen from Figure 9 that the features with higher importance are concentrated in the β band of each electrode point, and the β band plays a major role in the four bands. The results obtained by performing the same feature selection for the remaining subjects are shown in Figure 10. It can be found that the features with higher importance in the EEG are also concentrated in the β band, which means the β band is the most sensitive compared to other bands.
Algorithms 2019, 12, x FOR PEER REVIEW 13 of 18 found that the features with higher importance in the EEG are also concentrated in the β band, which means the β band is the most sensitive compared to other bands.
(a) Importance score (b) Ranking of importance

Application of SVM Classifier to EEG Signals
Considering the individual differences, a load classification model for each subject is established separately. The following is an example of subject 22 to illustrate the construction of the classification network. The classification data set information after feature extraction and feature selection are shown in Table 3. The data volume and data dimension of different loads, training set, cross-validation set, and test set are given in the table. The dataset of subject 22 obtained in Section 4.2 can be expressed as A 1 , A 2 , ..., A 1749 . Each dataset has 120 dimensions, where the low load tag is 0, the moderate load tag is 1, and the high load tag is 2.
Since the sample size of each load level is 583, the effective number of samples of this model is 1749.
The SVM model has two very important parameters C and γ. Where C is the penalty factor, which is the tolerance for the error. The higher the C value is, smaller error can be allowed, and the over-fitting occurs more easily. The smaller the C value is, the easier under-fitting occurs. If the C value is too large or too small, the generalization ability is poor. γ implicitly determines the distribution of data after mapping to a new feature space. The larger the γ is, the more vectors there are. The number of support vectors affects the speed of training and prediction. So the two critical parameters of the classification network are the C and γ value.
Here, the parameters are adjusted according to the accuracy of the cross-validation set, using k-fold cross-validation. We use 10-fold cross-validation since it is the most widely used. The average of 10 results is used as an indicator, and a single estimation is finally obtained. This ensures that each subsample participates in the training and test, reducing generalization errors and preventing overfitting.
Taking the subject 22 as an example, the accuracy of the cross-validation set under different parameters is shown in Figure 11. It can be seen from the figure that when C = 5000 and γ = 0.00003, the accuracy of the cross-validation set is the highest. Taking the subject 22 as an example, the accuracy of the cross-validation set under different parameters is shown in Figure 11. It can be seen from the figure that when C = 5000 and γ = 0.00003, the accuracy of the cross-validation set is the highest. The classification result confusion matrix is used to reflect the quality of the classification results. Taking the three classifications as an example, the layout of the confusion matrix is shown in Table 4. Mij (i = 0, 1, 2, j = 0, 1, 2) indicates the number of samples in which the i-th class is divided into the j-th class. Among them, if i = j, the classification is correct, and if i ≠ j, the classification is wrong. From this indicator, the difficulty degree of classification can be determined.

Classification Result of All Feature Data
The SVM is used to classify the mental load for the data in the four bands. Taking the subject 22 as an example, when C = 5000, γ = 0.00003, the classification accuracy is 94% on the training set and 87% on the test set. The classification confusion matrix is shown in Figure 12.
(a) Training set classification confusion matrix (b) Test set classification confusion matrix  The classification result confusion matrix is used to reflect the quality of the classification results. Taking the three classifications as an example, the layout of the confusion matrix is shown in Table 4.

Output Class
M ij (i = 0, 1, 2, j = 0, 1, 2) indicates the number of samples in which the i-th class is divided into the j-th class. Among them, if i = j, the classification is correct, and if i j, the classification is wrong. From this indicator, the difficulty degree of classification can be determined.

Classification Result of All Feature Data
The SVM is used to classify the mental load for the data in the four bands. Taking the subject 22 as an example, when C = 5000, γ = 0.00003, the classification accuracy is 94% on the training set and 87% on the test set. The classification confusion matrix is shown in Figure 12. Taking the subject 22 as an example, the accuracy of the cross-validation set under different parameters is shown in Figure 11. It can be seen from the figure that when C = 5000 and γ = 0.00003, the accuracy of the cross-validation set is the highest. The classification result confusion matrix is used to reflect the quality of the classification results. Taking the three classifications as an example, the layout of the confusion matrix is shown in Table 4.

Output Class
Mij (i = 0, 1, 2, j = 0, 1, 2) indicates the number of samples in which the i-th class is divided into the j-th class. Among them, if i = j, the classification is correct, and if i ≠ j, the classification is wrong. From this indicator, the difficulty degree of classification can be determined.

Classification Result of All Feature Data
The SVM is used to classify the mental load for the data in the four bands. Taking the subject 22 as an example, when C = 5000, γ = 0.00003, the classification accuracy is 94% on the training set and 87% on the test set. The classification confusion matrix is shown in Figure 12.
(a) Training set classification confusion matrix (b) Test set classification confusion matrix

Classification Result of β Band Feature Data
The SVM is also applied to classify the mental load for the β band characteristic data. Taking the subject 22 as an example, when C = 100, γ = 0.03, the classification accuracy is 93% on the training set and 89% on the test set. The classification confusion matrix is shown in Figure 13.

Classification Result of β Band Feature Data
The SVM is also applied to classify the mental load for the β band characteristic data. Taking the subject 22 as an example, when C = 100, γ = 0.03, the classification accuracy is 93% on the training set and 89% on the test set. The classification confusion matrix is shown in Figure 13.

Comparative Analysis of Classification Results
The full band and β band features of all the participants are sent to the SVM for classification of mental load respectively. The classification accuracy of the two methods on the test set is shown in Table 5, where Acc(f) indicates the accuracy of classification using full band features, and Acc(β) indicates the accuracy of classification using the β band feature. From the classification results, the effect of using the β band data for classification alone is better than the classification using the four bands data. It can be explained that the β band feature contains most of the useful information about the data. The rest of the band information interferes with the data. In the case of testing mental load, the β band data can be separately analyzed, which not only can reduce the dimension of the data, but can also improve the performance of the model, and save resources such as memory.

Conclusions
In this paper, the sensitive bands of EEG data under different mental workloads are studied. The signals are obtained through different flight load experiments. Feature extraction is performed by calculating the PSD and energy of the EEG signal. According to the feature selection of Gini impurity, the importance index of different bands of EEG data is analyzed using the Gini index, and the characteristics of the band with higher importance are obtained. It is found that the β band has the

Comparative Analysis of Classification Results
The full band and β band features of all the participants are sent to the SVM for classification of mental load respectively. The classification accuracy of the two methods on the test set is shown in Table 5, where Acc(f ) indicates the accuracy of classification using full band features, and Acc(β) indicates the accuracy of classification using the β band feature. Table 5. Classification accuracy of the four band features and the β band feature on the test set (unit: %).

Subject Number
Acc(f ) Acc(β) Acc(β) − Acc(f ) From the classification results, the effect of using the β band data for classification alone is better than the classification using the four bands data. It can be explained that the β band feature contains most of the useful information about the data. The rest of the band information interferes with the data. In the case of testing mental load, the β band data can be separately analyzed, which not only can reduce the dimension of the data, but can also improve the performance of the model, and save resources such as memory.

Conclusions
In this paper, the sensitive bands of EEG data under different mental workloads are studied. The signals are obtained through different flight load experiments. Feature extraction is performed by calculating the PSD and energy of the EEG signal. According to the feature selection of Gini impurity, the importance index of different bands of EEG data is analyzed using the Gini index, and the characteristics of the band with higher importance are obtained. It is found that the β band has the highest importance and sensitivity under different mental loads. The data of the four bands and the data of the separate β band are sent to the SVM classifier for verification. By observing the final classification accuracy, it is found that the data of the β band alone has higher final classification accuracy than the data of the four bands. This also shows that the β band data is more important and sensitive than other bands under different brain loads, and can represent the entire EEG data for subsequent data analysis.