Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network

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Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network Xianlun Tang , Wei Li , Xingchen Li , Weichang Ma , Xiaoyuan Dang PII: DOI: Reference:

S0957-4174(20)30110-X https://doi.org/10.1016/j.eswa.2020.113285 ESWA 113285

To appear in:

Expert Systems With Applications

Received date: Revised date: Accepted date:

19 August 2019 15 November 2019 5 February 2020

Please cite this article as: Xianlun Tang , Wei Li , Xingchen Li , Weichang Ma , Xiaoyuan Dang , Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network, Expert Systems With Applications (2020), doi: https://doi.org/10.1016/j.eswa.2020.113285

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Highlights The EMD algorithm is improved by using two conditions to select IMFs; The improved EMD (CEMD) algorithm is used to reduce the noise of EEG signals; An EEG signal combination method is proposed to encode the ERS/ERD information; A model called 1DMSCNN is built to classify EEG signals; An intelligent wheelchair system based on the proposed algorithm is designed.

1

Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network Xianlun Tanga, Wei Lia*, Xingchen Lia, Weichang Maa, Xiaoyuan Dangb a.

Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

b.

College of Mobile Telecommunications, Chongqing University of Posts and Telecom, Chongqing 401520, China

Email address: Xianlun Tang: [email protected] Wei Li: [email protected] (*Corresponding author) Xingchen Li: [email protected] Weichang Ma: [email protected] Xiaoyuan Dang: [email protected]

2

Abstract: Electroencephalogram (EEG) signals classification plays a crucial role in brain computer interfaces (BCIs) system. However, the inherent complex properties of EEG signals make it challenging to get them analyzed and modeled. In this paper, a novel method based on conditional empirical mode decomposition (CEMD) and one-dimensional multi-scale convolutional neural network (1DMSCNN) is proposed to recognize motor imagery (MI) EEG signals. In the CEMD algorithm, the correlation coefficient between the original EEG signal and each intrinsic modal component (IMF) is used as the first condition to select IMFs, and the relative energy occupancy rates between the IMFs are the second condition. The CEMD algorithm is applied to remove the noise of EEG signals. Then, an EEG signals combination method is proposed to encode event-related synchronization/de-synchronization (ERS/ERD) information between the channels. Finally, a model called 1DMSCNN is built to classify the processed EEG signals. The proposed method is applied to the dataset collected in our laboratory and BCI competition IV dataset 2b. The results indicate that the proposed method can achieve higher accuracy for EEG signals classification, compared with other state-of-the-art works. In addition, the proposed algorithm is applied to the online recognition of EEG signals, a BCI system that directly interacts with brain and wheelchair is designed and implemented. This system can directly command wheelchair to turn left and right through EEG signals. The online experimental results indicate that the designed intelligent wheelchair system is a feasible BCI application. It verifies the proposed algorithm can be used in expert and intelligent systems. Our method can provide a stimulus to the development of human-robot interaction. Keywords: empirical mode decomposition; convolutional neural network; motor imagery EEG; feature extraction; intelligent wheelchair.

1. Introduction Brain-computer interfaces (BCIs) convert brain activity recorded from human scalp into computer control commands to control external devices, thus helping disabled people recover some motor abilities (Sun, Feng, Lu, Wang & Zhang, 2019; Van Erp, Lotte & Tangermann, 2012; Wolpaw, Birbaumer, McFarland, Pfurtscheller & Vaughan, 2002). There have been studies on the use of electroencephalography (EEG) to control intelligent wheelchair (zhang, et al., 2015), robotic arm (Wang, Dong, Chen & Shi, 2015) and other external equipment (Liao, et al., 2012; LaFleur, et al., 2013). In a BCI system, the feature extraction and classification of EEG signals form important parts. The commonly used EEG signals include event-related P300 potentials, steady-state visually evoked potentials (SSVEPs) and motor imagery (MI) related mu/beta rhythms (Yu, et al., 2015). Compared with other types of signals, there are some distinct features in EEG signals. The collected EEG signals vary with the structure of the human brain and the subjects' mental states (Gui, Jin, Xu, Ruiz-Blondet & Laszlo, 2015). Therefore, each subject’s EEG signal is unique. EEG signals are nonlinear and non-stationary, which means the characteristics of EEG signals change over time. Moreover, as the collected EEG signals are usually mixed with noise, posing a challenge to the analysis of EEG signals. Therefore, effective measures should be taken to improve the signal-to-noise ratio (SNR) of EEG signals. Different MI tasks inspire different activation states in the sensorimotor cortex of the brain (Tabar & Halici, 2016), and MI can be collected in a convenient, non-invasion, cost-saving way. Therefore, many researchers have focused on the feature extraction and classification of MI EEG signals. The commonly used feature extraction algorithms include wavelet transform (WT) (Ma, Guo, Su & Liang, 2017; Pattnaik, Dash & Sabut, 2016), common spatial patterns (CSP) (Lotte & Guan, 2010; Wu, et al., 2013; Zhang, et al., 2018), Extreme energy difference (EED) (Sun, S, 2010), principal component analysis (PCA) (Kottaimalai, Rajasekaran, Selvam & Kannapiran, 2013), empirical mode decomposition (EMD) (Gaur, Pachori, Wang & Prasad, 2015; Kevric & Subasi, 2017; Park, Looney, ur Rehman, Ahrabian & Mandic, 2012) and so on. As the EMD algorithm can 3

decompose signals adaptively, it has been proved to be a very suitable candidate to analyze nonlinear and non-stationary EEG signals. For instance, in (Kevric & Subasi, 2017), the EMD algorithm is used to filter out the noise in EEG signals. However, the typical EMD algorithm generally select the intrinsic modal components (IMFs) by researchers’ experience, which will result in some EEG samples mixing redundant information or losing some useful information. In addition, the features extracted by these traditional methods are usually hand-designed, requiring a high degree of expertise. Therefore, it is of great significance to automatically extract effective features from EEG signals. Deep learning can powerfully deal with nonlinear and non-stationary data and automatically extract the effective feature representation from the original data. In recent years, some deep learning methods (Chu, et al., 2018; Dose, Møller, Iversen & Puthusserypady, 2018; Li, Struzik, Zhang & Cichocki, 2015; Sun, Lo & Lo, 2019; Schirrmeister, et al., 2017; Tabar & Halici, 2016) are employed for the classification of EEG signals. Convolutional neural network (CNN) (LeCun, Bottou, Bengio & Haffner, 1998) is one of the typical representatives. In a study by Dose, Møller, Iversen and Puthusserypady (2018), an end-to-end CNN model is proposed for the classification of EEG signals, and the model is proved to be effective in classifying small sample EEG data. To adapt to the nonlinear and non-stationary features of EEG signals, CNN and stacked autoencoders (SAE) are combined to classify the EEG signals, and the results show the recognition accuracy of EEG signals is improved (Tabar & Halici, 2016). There are also some methods combining traditional feature extraction methods with deep learning methods (Li, Zhang, Luo & Yang, 2016; Xu, et al., 2018; Yang, Sakhavi, Ang & Guan, 2015). For instance, in (Xu, et al., 2018), the wavelet transform (WT) is used to convert the one-dimensional (1D) EEG signals into the Two-dimensional (2D) time-frequency images, and a CNN is built as the classifier. In this paper, to overcome the weakness of traditional EMD algorithm in terms of selecting effective IMFs based on researcher’s experience, the conditional empirical mode decomposition (CEMD) algorithm is proposed. The correlation coefficient between the original EEG signal and each IMF is used as the first condition to select IMFs, and the relative energy occupancy rates between the IMFs are the second condition. The CEMD algorithm is used to reduce the noise of EEG signals. EEG signals are multivariate time series signals with strong anterior-posterior dependence, and the characteristics of EEG signals are mainly changed over time. To protect the structure of EEG time series from being destroyed, 1D convolution is performed to extract features along the time axis. However, previous studies have shown that there are event-related synchronization (ERS) and event-related de-synchronization (ERD) phenomena exist (Yu, et al., 2015) between the EEG signals channels, and performing 1D convolution along the time axis cannot capture this information. Therefore, an EEG signals combination method is proposed to encode this information between the channels. Finally, to extract effective features of EEG signals from multiple scales, a one-dimensional multi-scale convolution neural network (1DMSCNN) is built to classify the processed EEG signals. The dataset collected in our laboratory and BCI competition IV dataset 2b are carried out to validate the performance of the proposed method, and as it shows, compared with other state-of-the-art works, the proposed method can achieve superior performance. It proves that the proposed method can extract the features from complex EEG signals more effectively, and it can be used in a BCI system. Finally, a BCI system that directly interacts with brain and wheelchair is designed and implemented. In the system, the proposed algorithm is applied to classify left hand and right hand MI EEG signals, and then the system generates corresponding control commands for the wheelchair to turn left and right. The online experiments show that the designed intelligent wheelchair system can accurately judge subject's instructions. It demonstrates that the designed intelligent wheelchair system is a feasible BCI application, and that our approach provides an effective strategy to design expert and intelligent wheelchair system. The main highlights of this paper are list as follow: 4

(1) The EMD algorithm is improved by using two conditions to select IMFs, and the improved EMD (CEMD) algorithm is used to reduce the noise of EEG signals; (2) An EEG signals combination method is proposed to encode the ERS/ERD information between the channels; (3) A model called 1DMSCNN is built to classify EEG signals; (4) An expert and intelligent wheelchair system based on the proposed algorithm is designed and implemented, and the online experiments show that the proposed algorithm is feasible for BCI application. 2. Conditional empirical mode decomposition 2.1 Empirical mode decomposition The EMD (Li, Zhou, Yuan, Geng & Cai, 2013) is an adaptive and intuitive signal-dependent decomposition algorithm for the analysis of nonlinear and non-stationary signals. The purpose of EMD is to decompose a complex signal into a set of band-limited IMFs and a residual component. Each IMF should satisfy two basic conditions: (1) In a full data set, the number of extreme points and the number of zero crossings should be equal or at most differ by one; (2) For any given point, the mean value of the envelops defined by local minima and local maxima should be zero. Using the EMD method, the EEG signal x(t) can be reconstructed as: n

x(t)   imfl (t)  res

(1)

l 1

where imf1 (t), imf 2 (t),

imfl (t) are all the IMFs decomposed from the original EEG signal, and n stands for

the number of IMFs. The number of IMFs obtained by different EEG signals may be different, which fully demonstrates that the EMD algorithm can adaptively decompose the EEG signals. res is the residue signal. 2.2 The conditions of selecting IMFs To overcome the problem caused by empirically selecting IMFs, the correlation coefficient between the original EEG signal and each IMF is used as the first IMFs selection condition, and the relative energy occupancy rates between the IMFs are proposed as the second condition. (1) Condition 1: correlation coefficient The definition of correlation coefficient is as follows: N

r

 (imf i 1

N

 (imf i 1

_

i

_

i

_

 imf )( xi  x)

 imf )

N

2

 (x i 1

i

_

 x)

(2) 2

where r is the correlation coefficient between the original EEG signal and an IMF, and its range is [-1, 1]. _

imf 

_ 1 N 1 N imfi , x is the original EEG signal, x   xi ， N represents the sampling frequency of the  n i 1 n i 1

signal. The larger the correlation coefficient is, the more effective information the IMF contains. The correlation coefficient between the original EEG signal and each IMF is calculated by formula (2). And the condition to satisfy the correlation coefficient is defined as:

r  where  is the set threshold and 0    1 . 5

(3)

(2) Condition 2: relative energy occupancy rate Firstly, the energy of each IMF is defined as: N

El   imfl (i)2

(4)

i 1

where El is the energy of the imfl , N represents the sampling frequency of the signal. So the energy of each IMF can be expressed as: E1 , E2 ,

, En .

Then, the relative energy occupancy rate is defined as:

q

El E1  E2 

(5)

 En

where q is the relative energy occupancy rate of the imfl . Higher relative energy occupancy rate means more obvious fluctuations of the IMF, indicating that it encompasses more effective information. The relative energy occupancy rate of each IMF is calculated according to formula (5), and the selecting condition that satisfies the relative energy occupancy rate is defined as: q

(6)

where  is the set threshold and 0    1 . And then, selected IMFs are used to reconstruct the EEG signal by the following formula: m

y   imfi

(7)

i 1

where y is the reconstructed EEG signals, m is the number of effective IMFs after selecting. 2.3 Conditional empirical mode decomposition algorithm The steps of the conditional empirical mode decomposition algorithm are as follows: Step1: Perform an EMD process on the EEG signal of one channel, obtain the IMFs and residual signal; Step2: Remove the residual signal; Step3: Retain the first five IMFs for analysis. According to previous research, the information related to the left and right hand motor imagery is mainly scattered in the first five IMFs. Step4: Use the correlation coefficient condition to select the IMFs. Calculate the correlation coefficient between each IMF and the EEG signal before the EMD process according to formula (2), and then the IMFs are selected according to formula (3); Step5: Use the relative energy occupancy rate condition to select the IMFs. For the IMFs selected by the correlation coefficient condition, the energy of each IMF is calculated according to formula (4). And then the relative energy occupancy rates of each IMF in the channel is calculated according to formula (5). Finally, the IMFs are selected according to formula (6); Step6: Reconstruct the EEG signals using the selected IMFs. 3. EEG signals combination method 1DCNNs are often used to extract features of time series signals and have achieved good results (Li, Zhang, Zhang & Wei, 2017; Ullah, Hussain & Aboalsamh, 2018). EEG signals are multivariate time series signals, and their characteristics are mainly changed over time. So we perform 1D convolution to extract features along the time axis. Furthermore, to utilize the information (ERD and ERS phenomena) between the EEG signal channels, the following method is proposed. The EEG signal from a left sensorimotor channel can be represented as [ A1 , A2 , 6

, Ae ] , and the EEG signal

from a right sensorimotor channel is represented as [ B1 , B2 ,

, Be ] . So the subtraction between the EEG signal

channels is defined as follows:

Ci  Ai  Bi，i  1,2, , e

(8)

where e denotes the length of the EEG signal in a channel, and the obtained EEG signal is expressed as [C1 , C2 ,

, Ce ] .

The schematic diagram of the EEG signals combination method is depicted in Fig.1. And the steps of the EEG signals combination method are as follows: Step1: Determine the EEG signals of the corresponding channels (In the same sensorimotor position on left and right sides of the brain); Step2: For each set of corresponding channels, the new EEG signals can be obtained by subtracting the EEG signals of the right channel from those of the left channel; Step3: The new EEG signal and the original signal are combined in parallel way.

Left

Right

  

Corresponding channel







New

Original signal

New signal

Left-Right

Signal combination

Left

Right

New

Combined signal

Fig.1 The schematic diagram of the EEG signals combination method

4. 1D Multi-scale convolutional neural network 4.1 Convolutional neural network CNN (Cecotti & Graser, 2008) can directly input the original samples and automatically learn the feature representation of the samples, thus facilitating the classification of the samples. After years of development and improvement, CNNs have been widely used in classification, target detection and other fields. Compared with fully connected networks, shared weights, local receptive fields and sub-sampling are the three main distinct features of CNNs. A CNN model is generally composed of five building blocks: input layer, convolution layer, pooling layer, fully connected (FC) layer and output layer. Convolution layer, one of the most important parts of CNNs, plays a key role in extracting features from the input data. The convolution formula is as follows: s dj  f (  sid 1 * wijd  bid )

(9)

iM j

where s dj denotes the j th feature graph in the d th convolution layer, wijd is the connection weight between the j th feature of the d th layer and the i th feature of the d-1 layer. * indicates the convolution operator,

M j is a collection of input features, b is the bias term, f ( ) denotes the activation function, sigmoid ( f ( x) 

1 ), hyperbolic tangent ( f ( x)  tanh( x) ) and rectified linear units (ReLU, f ( x)  max(0, x) ) are 1  e x 7

widely employed as activation functions. Pooling layer is done by aggregating neighboring values in a feature map into one value by taking the average (average pooling) or maximum (max pooling). Its main functions are to reduce the network parameters and the dimension of feature maps while preserving useful information. In most of the related research, the max pooling is chosen in CNN models, for it can better preserve the main features of the previous layer. In a CNN model, the convolution and pooling layers are usually followed by one or more fully connected layer. The fully connected layer converts the feature maps of the previous layer into a 1D vector, and connects the Softmax layer to separate the various categories. 4.2 1D multi-scale convolution The features extracted by convolution kernels of different scales are different. Convolution with large kernel size can capture relatively holistic features, but it is not sensitive to capture detail features. While convolution with small kernel size can capture detail features more effectively (Szegedy, Ioffe, Vanhoucke & Alemi., 2017). To make the extracted features contain more complete information, a 1D multi-scale convolution block is proposed, and its structure is shown in Fig.2. Input

Conv1-1 (Size:small)

Conv1-2 (Size:Large)

Max pooling1-3

Max Pooling1-1

Max Pooling1-2

Conv1-3 (Size:1)

Concatenation

Fig.2 The structure of multi-scale convolution block

In this block, convolution1-1 with large kernel size can capture relatively holistic features from EEG signals, but it cannot capture detail features effectively. Convolution1-2 with small kernel size can capture detail features from EEG signals. The two parts are followed by max pooling to reduce the parameters. In addition, max pooling1-3 is used directly to preserve the main features of the previous layer, and a convolution operation with the kernel size of 1 is used to change the dimension of the feature maps (Szegedy, et al., 2015). Finally, the feature maps obtained from the three parts are combined by concatenation operation. The block can extract features from EEG signals on multiple scales, thus ensuring the validity of extracted features and improving the classification accuracy of EEG signals. 4.3 1D Multi-scale convolutional neural network

Input layer

Multi-scale Convolution layer

Convolution layer

Max Pooling layer

FC layer

Output layer

Fig.3 The structure of 1D multi-scale convolutional neural network

The proposed structure of 1D multi-scale convolutional neural network is illustrated in Fig.3. The network is

8

designed to accept EEG signals of N EEG  Nchan shape (where N EEG refers to the length of EEG signal and

N chan refers to the number of EEG channels used). 1D multi-scale convolution is used to extract multi-scale features from the input EEG signals. The large kernel size is referred as K L , and the small kernel size is referred as K S . The main parameters of 1D multi-scale convolution layer are listed in Table 1. Then, the 1D multi-scale convolution layer is followed by a convolution layer, whose kernel size is K . Max pooling operation can reduce the size of feature maps by using K MP filters with a stride K MP (where K MP is the size of filters). The extracted features are the input of an FC layer with 300 hidden units. The output of network is generated by a Softmax layer with the number of neurons determined by the types of EEG signals. In addition, ReLU is chosen as activation function, because it can speed up the optimization process of the network. The main parameters of 1D multi-scale convolutional neural network are listed in Table 2. In the network, the cross-entropy is chosen as the loss function, which is defined as:

loss   pi log qi

(10)

i

where p indicates the expected value, and q represents the predicted value. The training strategy for our model is stochastic gradient descent (SGD) algorithm which has proven to be effective. The process of training is to minimize the value of loss. However, when the learning rate is too large, ReLU will cause some neurons in the network to die (Goh, et al., 2018). So the learning rate we set is  =1×10-4. When training the 1D multi-scale convolutional neural network, it may over fit with a few train samples. So we add dropout in the fully connected layer to reduce over-fitting. In the experiment, we set the dropout rate at 0.5 based on the empirical value (Srivastava, Hinton, Krizhevsky, Sutskever & Salakhutdinov, 2014) to regulate the network. Table 1 Main parameters of 1D multi-scale convolution layer Layer

Output

Kernel

Numbers

name

Shape

size

of kernel

Convolution1-1

NEEG×16

KL

Max pooling1-1

( N EEG / KMP ) 16

Convolution1-2

NEEG×16

Stride

Padding

16

1

SAME

KMP

-

KMP

SAME

KS

16

1

SAME

Max pooling1-2

( N EEG / KMP ) 16

KMP

-

KMP

SAME

Max pooling1-3

( N EEG / KMP )  Nchan

KMP

-

KMP

SAME

Convolution1-3

( N EEG / KMP ) 16

1

16

1

SAME

Concat

( N EEG / KMP )  48

-

-

-

-

Table 2 Main parameters of 1D multi-scale convolutional neural network Layer

Output

Kernel

Numbers

name

Shape

size

of kernel

Multi-scale convolution

( N EEG / KMP )  48

-

-

Convolution

( N EEG / KMP )  96

K

Max pooling

2 ( N EEG / KMP )  96

KMP

FC

300

Output (Softmax)

2

5. Experiment on motor imagery EEG classification 5.1 EEG signal acquisition based on Emotiv EPOC+ 9

Stride

Padding

-

-

96

1

SAME

-

KMP

SAME

-

-

-

-

-

-

-

-

electrode cap

conductive liquid

EPOC+ neuroheadset

AF3

AF4

F7

F8 F3 F4 FC5

FC6

T7

T8

CMS

DRL

P7

P8 O1

electrode box

O2

Emotiv wireless USB receiver

Fig.4 Emotiv EEG signal acquisition instrument

Fig.5 The placement position of Emotiv electrodes

The Emotiv EEG acquisition instrument is used in this experiment, which is shown in Fig.4. Its system comprises an EPOC+ neuroheadset, a wireless USB receiver, an electrode box, 16 electrode caps and conductive liquid. The EPOC+ neuroheadset is equipped with 14 effective electrodes and two reference electrodes. The electrodes are placed in accordance with the international 10-20 electrode placement standard, as shown in Fig.5. CMS and DRL are reference electrodes, and other electrodes are effective electrodes. The sample rate is 128 Hz. The EEG signals are collected by the EPOC+ neuroheadset and recorded by the EmotivPRO software.

(a)A subject

(b) Interface of EmotivPRO record software

Fig.6 A subject is conducting a collection experiment

The EEG signal acquisition experiment is conducted in a relatively quiet environment. 5 healthy postgraduates (denotes S1, S2, …, S5) are selected to collect the left and right hand motor imagery EEG signals. As we can see in Fig.6, a subject is collecting the EEG data. The experimental process of one trial is shown in Fig.7. Prior to the experiment, the subject sits in a chair for 40s to relax, and then enters the acquisition process. One trial of collection experiment requires 10s, with the first six seconds seeing the subject calm down and ready for the collection. When t=6s, there is a reminder sound to prompt the subject to begin imagining left or right hand movement. When t=10s, there is also a reminder sound to prompt the subject to stop imagining. And the EEG data recorded at the 8s and 9s are taken as samples, so two EEG signal samples can be obtained at one trial. For each subject, each type of imagination task is repeated 90 times, so we get 180 samples of left hand motor imagery EEG signal and 180 samples of right hand motor imagery EEG signal. To reduce the computational complexity, only the data of six channels (F3, FC5, T7, F4, FC6, T8) from the sensorimotor are retained for analysis. And the dataset is divided into a training set and a testing set according to the ratio of 4:1.

10

Imagination of left hand movement

Imagination of right hand movement Begin beep

Cue Relax 0

1

2

Rest Cue Next trial

Used data Motor Imagery Period

3

4

5 One trial

6

7

8

9

10

Fig.7 The experimental process of one trial

5.2 Data preprocessing EEG signals usually contain a lot of background noise such as electro-oculogram (EOG), electromyogram (EMG) and power frequency clutter, etc. To reduce the background noise, three effective measures are taken to process EEG signals. Firstly, the abnormal samples are removed. Secondly, each sample is subtracted from the average amplitude to obtain a zero-mean signal. Thirdly, a band-pass filtering of 8-30Hz for the EEG signals is used. Studies suggest that the event related to synchronization/de-synchronization phenomenon is mainly manifested in the mu rhythm (8~13 Hz) and beta rhythm (14~30 Hz) (Yu, et al., 2015). 5.3 Experimental process The EEG signals of each channel are processed separately using CEMD algorithm. The threshold of correlation coefficient condition and relative energy occupancy rate condition are obtained from the following two experiments. The experiments are performed on the first subject's data. (1) Determination of threshold  In the experiment, only the correlation coefficient condition is used to select IMFs, and the threshold



is

uniformly taken at intervals of 0.05 in [0, 1]. Then the processed EEG data are input into 1DCNN to be classified. The experimental result shows that the recognition accuracy of EEG signals decrease rapidly when the value of



exceeds 0.2. Fig.8 shows the recognition accuracy of EEG signal when the threshold



is 0, 0.05, 0.1,

0.15, 0.2 and 0.25 respectively. As can be seen from Fig.8, when the threshold is at 0.1, the recognition accuracy of EEG signal is the highest, so the threshold



is set at 0.1.

86 85 84

Accuracy (%)

83 82 81 80 79 78 77 76 0.00

0.05

0.10

0.15

0.20

0.25

Correlation coefficient

Fig.8. The recognition accuracy of EEG signal when setting different threshold

(2) Determination of threshold 

11



In the experiment, the correlation coefficient condition and relative energy occupancy rates condition are taken into account when selecting IMFs. On the basis of the above experiments, the threshold  is set at 0.1, and the threshold  is evenly taken at intervals of 0.05 in [0, 1]. The processed EEG data are input into 1DCNN to be classified. The experimental result shows that the recognition accuracy of EEG signal is the highest when the threshold  is 0.1, so the threshold  is set at 0.1. Fig.9 shows the recognition accuracy of EEG signal when the threshold  is 0, 0.05, 0.1, 0.15, 0.2 and 0.25 respectively. 86 85 84

Accuracy (%)

83 82 81 80 79 78 77 76 0.00

0.05

0.10

0.15

0.20

0.25

Relative energy occupancy rate

Fig.9. The recognition accuracy of EEG signal when setting different threshold 

As can be seen in Fig.5, F3 and F4, FC5 and FC6, T7 and T8 are symmetrically distributed on the left and right sides of the brain, so they are mutually corresponding channels. Then, EEG signals are processed by the EEG signals combination method proposed in section 3. Finally, the processed EEG signals are input into 1DMSCNN to be classified, and in this part, we set the parameter KL  10, KS  3, KP  2, K  5 . 5.4 Experimental results and analysis In this section, detailed experiments are conducted to verify the effectiveness of the proposed method for MI EEG signals recognition. To demonstrate the validity of CEMD algorithm and the proposed EEG signals combination method, EMD (Take the first 3 IMFs), CEMD and EEG signal combination method are employed to process EEG signals, respectively, and the same structure of 1DCNN is adopted to extract features of EEG signals. For each subject, the experiment is executed for 10 times. The average classification accuracy and standard error (SE) of each subject are depicted in Fig.10 (ESCB represents the EEG signals combination method). As can be observed in Fig.10 (a), the CEMD method can achieve the highest accuracy among 5 subjects, thus validating the effective of the proposed conditions for selecting IMFs. In addition, it can be observed that EEG signals processed by EMD method achieve better results than EEG signals without any treatment, indicating that the EMD method is a feasible method for processing EEG signals. From Fig.10 (b), we learn the recognition rate of EEG signals is improved after being processed by EEG signals combination method. It shows that EEG signals combination method takes into account the effective information between channels, and thus improves the recognition accuracy of EEG signals.

12

100

100

1DCNN ESCB+1DCNN

1DCNN EMD+1DCNN CEMD+1DCNN

90

Accurary (%)

Accurary (%)

90

80

80

70

70

60 1

60 1

2

3

4

2

3

4

5

Subject

5

Subject

(b) EEG signals processed by ESCB

(a) EEG signals processed by EMD and CEMD Fig.10 Average recognition accuracy of five subjects obtained by different EEG signal processing methods

In the next experiment, the 1DCNN, 2DCNN and 1DMSCNN are used to classify the EEG signals. The input EEG signals are also processed by the CEMD algorithm and EEG signal combination method. The Receiver Operating Characteristic (ROC) Curve is a graphical representation used to evaluate the quality of the classification model. The effect of the model can be judged by analyzing the value of the area under the curve (AUC). As can be observed in Fig.11, in most instances, 1DCNN shows the higher AUC value than 2DCNN, which proves that 1D convolution is more effective than 2D convolution in analyzing EEG signals. 1DMSCNN can achieve the best performance among 5 subjects, indicating that the features of EEG signals extracted from

1.0

1.0

0.8

0.8

True positive rate

True positive rate

multiple scales contain more valid information, thus it can improve the recognition accuracy of EEG signals.

0.6

0.4

0.2

0.4

0.6

0.8

1DCNN,AUC=0.98 2DCNN,AUC=0.94 1DMSCNN,AUC=0.99

0.0

0.0 0.2

0.4

0.2

1DCNN,AUC=0.84 2DCNN,AUC=0.80 1DMSCNN,AUC=0.88 0.0

0.6

0.0

1.0

0.2

0.4

0.6

False positive rate

False positive rate

(a)S1

(b)S2

13

0.8

1.0

1.0

0.8

0.8

True positive rate

True positive rate

1.0

0.6

0.4

0.2

0.6

0.4

0.2

1DCNN,AUC=0.74 2DCNN,AUC=0.71 1DMSCNN,AUC=0.78

1DCNN,AUC=0.81 2DCNN,AUC=0.82 1DMSCNN,AUC=0.84

0.0

0.0 0.0

0.2

0.4

0.6

0.8

0.0

1.0

0.2

0.4

0.6

0.8

1.0

False positive rate

False positive rate

(c)S3

(d)S4 1.0

True positive rate

0.8

0.6

0.4

0.2

1DCNN,AUC=0.82 2DCNN,AUC=0.82 1DMSCNN,AUC=0.86

0.0 0.0

0.2

0.4

0.6

0.8

1.0

False positive rate

(e)S5 Fig.11 the ROC curves of 5 subjects

The performance of the proposed algorithm is compared with that of other feature extraction algorithms, including CSP (Wu, et al., 2013), DBN (Chu, et al., 2018), long short-term memory with Discrete Wavelet Transform (DWT-LSTM) (Li, Zhang, Luo & Yang, 2016), 1DCNN and 1DMSCNN. Based on the features extracted by CSP, a linear SVM is employed to classify the EEG signals. Table 3 lists the recognition accuracy of these algorithms for 5 subjects. It can be seen that the proposed algorithm performs better than other algorithms among 5 subjects. Table 3 the recognition accuracy (%) of different algorithms for 5 subjects Subject

CSP

DBN

DWT-LSTM

1DCNN

1DMSCNN

Proposed

S1

80.56

83.33

81.94

81.94

83.33

86.11

S2

95.83

93.06

94.44

95.83

95.83

97.22

S3

72.22

73.61

75.00

73.61

76.39

77.78

S4

76.39

77.78

80.56

79.17

80.56

83.33

S5

75.00

80.56

83.33

80.56

81.94

84.72

Average

80.00

81.67

83.05

82.22

83.61

85.83

To validate the statistical significance of the differences between the proposed algorithm and the other five algorithms, two-way analysis of variance (ANOVA2) and multiple comparisons tests are used to calculate the P-values between the proposed algorithm and these algorithms. The subject and the method are two independent variables of the test, and accuracy is dependent variable of the test. The least significant difference (LSD)

14

method is used for multiple comparisons. The P-values between the proposed algorithm and other five algorithms are listed in Table 4. It is generally considered that there is a significant difference between the two comparison algorithms when the P-value is less than 0.05. As can be seen from Table 4, the P-values between the proposed algorithm and CSP, DBN, DWT-LSTM, 1DCNN and 1DMSCNN are all less than 0.05. Therefore, the recognition accuracy of the proposed algorithm is considered to be significantly improved. Table 4 the P-values between the proposed method and other five algorithms Method

CSP

DBN

DWT-LSTM

1DCNN

1DMSCNN

P-values

<0.001

<0.001

0.005

0.001

0.020

5.5 Experiment on BCI IV dataset 2b BCI Competition dataset 2b is also used to evaluate the performance of the proposed algorithm. The Dataset 2b is provided by the Technical University of Graz (TUG), and it includes two classes MI EEG signals involving left hand and right hand. The dataset comprised of EEG signals are collected from 9 subjects, with each subject going through 5 sessions of MI experiments, the first two sessions without feedback and the other three sessions with feedback. During the collection process, three bipolar recordings are (C3, Cz and C4) used to record the EEG signals with a sampling frequency of 250 Hz, and the recorded EEG signals are band-pass filtered between 0.5 Hz and 100 Hz. Each of the first two sessions and the rest three sessions includes 120 trials and 160 trials, respectively. As for preprocessing, the operations are employed as the former dataset. And the EEG signals are processed by CEMD algorithm and EEG signal combination method. In this dataset, the corresponding channels are C3 and C4. Then, the EEG signals are input into 1DMSCNN to be classified, and we set the parameter K L  15, KS  5, K P  5, K  5 . To evaluate the performance of the proposed method on BCI competition IV dataset 2b, we compare the proposed method with other state-of-the-art approaches, including EED (Sun, S., 2010), CSP (Wu, et al., 2013), ASCP (Sun & Zhou, 2014), DBN (Chu, et al., 2018), CNN-SAE (Tabar & Halici, 2016) and 1DMSCNN, with the recognition accuracy of those algorithms listed in table 5. Table 5 the recognition accuracy (%) of different algorithms for 9 subjects Subject

EED

CSP

ACSP

DBN

CNN-SAE

1DMSCNN

Proposed

B1

56.56

66.56

67.50

66.56

76.00

76.39

80.56

B2

52.19

57.81

55.36

62.50

65.80

61.03

65.44

B3

58.13

61.25

62.19

60.00

75.30

60.41

65.97

B4

89.69

94.06

94.69

96.87

95.30

98.65

99.32

B5

64.69

80.63

76.88

82.02

83.00

85.14

89.19

B6

71.88

75.00

75.94

77.44

79.50

80.56

86.11

B7

56.25

72.50

71.25

76.56

74.50

78.47

81.25

B8

80.00

89.38

89.38

88.75

75.30

84.21

88.82

B9

70.63

85.63

81.25

86.06

73.30

81.94

86.81

Average

66.69

75.87

74.93

77.42

77.60

78.53

82.61

As can be seen from Table 5, the classification performance of proposed method is more effective than that of other comparison methods for most subjects, and the proposed method can get the best average recognition accuracy. Moreover, the deep learning methods DBN, CNN-SAE and 1DMSCNN can achieve higher recognition accuracy than the traditional feature extraction algorithms EED, CSP and ACSP. And it also can be seen that all of the algorithms have poor recognition accuracy on subject B2 and B3. There are three possible reasons that for this. Firstly, the subject is poorly concentrated. Secondly, the noise level in the environment may 15

be differ during the time of collection, resulting in the collected EEG signals containing different amount of useless information. Thirdly, each subject’s EEG signals are unique, with some subject's EEG signals easier to classify, while some difficult to classify. ANOVA2 and multiple comparisons tests are also used to validate the statistical significance of the differences between the proposed algorithm and the other eight algorithms. The P-values between the proposed algorithm and other eight algorithms are shown in Table 6. It can be seen that the P-values between the proposed algorithm and EED, CSP, ASCP, DBN, CNN-SAE and 1DMSCNN are all less than 0.05. Table 6 the P-values between the proposed method and other eight algorithms Method

EED

CSP

ACSP

DBN

CNN-SAE

1DMSCNN

P-value

<0.001

0.002

<0.001

0.013

0.015

0.047

6. Online experiment To further verify the practicability of the proposed algorithm, an online experiment is carried out on the self-designed intelligent wheelchair system. The intelligent wheelchair system mainly includes: an Emotiv EEG acquisition instrument, a laptop computer, wireless communication module, control system and a wheelchair. The structure of the intelligent wheelchair system is shown in Fig.12. Emotiv EEG acquisition instrument

Laptop computer Emotiv wireless USB receiver

Feature extraction Recognition

Recognition result feedback

Control commands

Intelligent wheelchair

Wireless communication module

Control system

Fig.12 The structure of the intelligent wheelchair system

Emotiv EEG acquisition instrument can record EMG and EEG signals simultaneously. Through previous observations, we know that gritting the teeth can make the F8 channel produce obvious voltage changes, so this signal is used to start and stop the online experiment. Blinking the eye can make the FC4 channel produce obvious voltage changes, and it is used to command wheelchair to go straight. Furthermore, the left hand and right hand MI EEG signals of F3, F4, FC5, FC6, T7, and T8 channels are collected, and the proposed method is employed to classify the EEG signals. The left hand and right hand MI EEG signals are used to command wheelchair to turn left and right, respectively. The first three subjects in the former dataset conduct the online experiments, they command the intelligent wheelchair to go straight, turn left and turn right. All kinds of experiments are carried out in a crossover way. Each type of experiment is performed 140 times, with a five-minute break after 20 experiments and an interval of 20 seconds between each experiment. The online recognition accuracy is shown in Table 7. Table 7 the online recognition accuracy (%) of three subjects. 1DCNN Subject

Straight

S1 S2

Proposed

Left

Right

Average

Left

Right

Average

94.29

77.86

75.71

76.78

82.86

82.14

82.50

96.43

91.43

92.14

91.78

93.57

94.29

93.93

16

S3

86.43

71.43

68.57

70.00

75.00

73.57

74.28

As can be seen from Table 7, the recognition accuracy of the proposed method is higher than the recognition accuracy of the 1DCNN model. And the online recognition accuracy of the EMG signal is higher than that of the EEG signal, because the EMG signals have more obvious characteristics than the EEG signals. In addition, after comparing the experimental results of Table 3 and those of Table 7, it can be found that the online recognition accuracy is lower than the offline recognition accuracy. This is due to more factors need to be considered when conducting an online experiment. For example, the subjects may be susceptible to the surrounding environment and prone to fatigue. 7. Conclusion In this paper, a method based on CEMD and 1DMSCNN is proposed to improve the performance of MI EEG signals classification. The classic EMD algorithm is optimized by the strategy of adding condition to select effective IMFs. The EEG signals combination method is proposed to encode the effective information between channels. To extract effective features of EEG signals from multiple scales, the structure of convolutional neural network is improved by multi-scale convolution feature extraction strategy. The dataset collected in our laboratory and BCI competition IV dataset 2b are used to validate the effectiveness of the proposed method. Compared with other existing feature extraction algorithms such as CSP, DBN, LSTM and CNN, the recognition accuracy of the proposed method is significantly improved. The experimental results show that the proposed algorithm can extract more effective features from EEG signals and can be used to classify EEG signals in a BCI system. With the proposed algorithm as the basis, an intelligent wheelchair system featuring live interactions between brain and wheelchair is designed and developed. The system consists of five parts: an Emotiv EEG acquisition instrument, a laptop computer, wireless communication module, control system and a wheelchair. In this system，the left hand MI EEG and right hand MI EEG are used to command the wheelchair to turn left and right, respectively. The online experimental results show that the intelligent wheelchair system can effectively identify the EEG signals and give precise control commands, which further validates the applicability of the proposed algorithm. The designed intelligent wheelchair system needs to be used in a relatively quiet environment. The subject is easily affected by the external factors, noise in particular, during the collection of EEG signals. So the recorded EEG signals are usually blend with interference information, and it will affect the recognition accuracy of EEG signals. To design a better expert and intelligent wheelchair system, further work will be carried out in the following aspects: (1) Collecting and classifying more complex EEG signals to further improve the functions of the wheelchair, such as using EEG signals to control the speed of the intelligent wheelchair. (2) Studying how to further improve the performance of convolutional neural networks, such as reducing network parameters and reducing network training time. (3) Integrating EEG signals with other biological signals to control wheelchair is an interesting method as it allows for more precise control of the wheelchair and improves the anti-jamming capability of the designed wheelchair system. Acknowledgements This work is supported by the National Nature Science Foundation of China under Project 61673079, 61703068 and the Natural Science Foundation of Chongqing under Project cstc2018jcyjAX0160. References Chu, Y., Zhao, X., Zou, Y., Xu, W., Han, J., & Zhao, Y. (2018). A decoding scheme for incomplete motor imagery EEG with deep belief

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network. Frontiers in neuroscience, 12, 680. Cecotti, H., & Graeser, A. (2008). Convolutional neural network with embedded Fourier transform for EEG classification. In 2008 19th International Conference on Pattern Recognition (pp. 1-4). Dose, H., Møller, J. S., Iversen, H. K., & Puthusserypady, S. (2018). An end-to-end deep learning approach to MI-EEG signal classification for BCIs. Expert Systems with Applications, 114, 532-542. Gui, Q., Jin, Z., Xu, W., Ruiz-Blondet, M. V., & Laszlo, S. (2015). Multichannel EEG-based biometric using improved RBF neural networks. In 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) (pp. 1-6). Gaur, P., Pachori, R. B., Wang, H., & Prasad, G. (2015). An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface. In 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). Goh, S. K., Abbass, H. A., Tan, K. C., Al-Mamun, A., Thakor, N., Bezerianos, A., & Li, J. (2018). Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(9), 1858-1867. Kottaimalai, R., Rajasekaran, M. P., Selvam, V., & Kannapiran, B. (2013). EEG signal classification using principal component analysis with neural network in brain computer interface applications. In 2013 IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICECCN) (pp. 227-231). Kevric, J., & Subasi, A. (2017). Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomedical Signal Processing and Control, 31, 398-406. Liao, L. D., Chen, C. Y., Wang, I. J., Chen, S. F., Li, S. Y., et al. (2012). Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. Journal of neuroengineering and rehabilitation, 9(1), 5. LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., & He, B. (2013). Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface. Journal of neural engineering, 10(4), 046003. Lotte, F., & Guan, C. (2010). Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Transactions on biomedical Engineering, 58(2), 355-362. Li, J., Struzik, Z., Zhang, L., & Cichocki, A. (2015). Feature learning from incomplete EEG with denoising autoencoder. Neurocomputing, 165, 23-31. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. Li, M., Zhang, M., Luo, X., & Yang, J. (2016). Combined long short-term memory based network employing wavelet coefficients for MI-EEG recognition. In 2016 IEEE International Conference on Mechatronics and Automation (pp. 1971-1976). Li, S., Zhou, W., Yuan, Q., Geng, S., & Cai, D. (2013). Feature extraction and recognition of ictal EEG using EMD and SVM. Computers in biology and medicine, 43(7), 807-816. Li, D., Zhang, J., Zhang, Q., & Wei, X. (2017). Classification of ECG signals based on 1D convolution neural network. In 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom) (pp. 1-6). Ma, M., Guo, L., Su, K., & Liang, D. (2017). Classification of motor imagery EEG signals based on wavelet transform and sample entropy. In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (pp. 905-910) Pattnaik, S., Dash, M., & Sabut, S. K. (2016). DWT-based feature extraction and classification for motor imaginary EEG signals. In 2016 International Conference on Systems in Medicine and Biology (ICSMB) (pp. 186-201). Park, C., Looney, D., ur Rehman, N., Ahrabian, A., & Mandic, D. P. (2012). Classification of motor imagery BCI using multivariate empirical mode decomposition. IEEE Transactions on neural systems and rehabilitation engineering, 21(1), 10-22. Sun, L., Feng, Z., Lu, N., Wang, B., & Zhang, W. (2019). An advanced bispectrum features for EEG-based motor imagery classification. Expert Systems with Applications, 131, 9-19. Sun, S. (2010). Extreme energy difference for feature extraction of EEG signals. Expert Systems with Applications, 37(6), 4350-4357. Sun, Y., Lo, F. P. W., & Lo, B. (2019). EEG-based user identification system using 1D-convolutional long short-term memory neural networks. Expert Systems with Applications, 125, 259-267.

18

Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., et al. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human brain mapping, 38(11), 5391-5420. Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-First AAAI Conference on Artificial Intelligence (pp. 4278-4284). Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9). Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958. Sun, S., & Zhou, J. (2014). A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces. In 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 1746-1753). Tabar, Y. R., & Halici, U. (2016). A novel deep learning approach for classification of EEG motor imagery signals. Journal of neural engineering, 14(1), 016003. Ullah, I., Hussain, M., & Aboalsamh, H. (2018). An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Systems with Applications, 107, 61-71. Van Erp, J., Lotte, F., & Tangermann, M. (2012). Brain-computer interfaces: beyond medical applications. Computer, 45(4), 26-34. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical neurophysiology, 113(6), 767-791. Wang, H., Dong, X., Chen, Z., & Shi, B. E. (2015). Hybrid gaze/EEG brain computer interface for robot arm control on a pick and place task. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1476-1479). Wu, S. L., Wu, C. W., Pal, N. R., Chen, C. Y., Chen, S. A., & Lin, C. T. (2013). Common spatial pattern and linear discriminant analysis for motor imagery classification. In 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) (pp. 146-151). Xu, B., Zhang, L., Song, A., Wu, C., Li, W., Zhang, D., et al. (2018). Wavelet Transform Time-Frequency Image and Convolutional Network-Based Motor Imagery EEG Classification. IEEE Access, 7, 6084-6093. Yu, T., Xiao, J., Wang, F., Zhang, R., Gu, Z., Cichocki, A., & Li, Y. (2015). Enhanced motor imagery training using a hybrid BCI with feedback. IEEE Transactions on Biomedical Engineering, 62(7), 1706-1717. Yang, H., Sakhavi, S., Ang, K. K., & Guan, C. (2015). On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2620-2623). Zhang, R., Li, Y., Yan, Y., Zhang, H., Wu, S., Yu, T., & Gu, Z. (2015). Control of a wheelchair in an indoor environment based on a brain–computer interface and automated navigation. IEEE transactions on neural systems and rehabilitation engineering, 24(1), 128-139. Zhang, Y., Wang, Y., Zhou, G., Jin, J., Wang, B., Wang, X., & Cichocki, A. (2018). Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Expert Systems with Applications, 96, 302-310.

Author Contributions Section Xianlun Tang: Conceptualization, Methodology, Resources, Project administration, Funding acquisition, Writing Review & Editing. Wei Li: Conceptualization, Methodology, Software, Investigation, Writing - Original Draft, Writing - Review & Editing. Xingchen Li: Validation, Investigation, Writing - Review & Editing. Weichang Ma: Investigation, Writing - Review & Editing. Xiaoyuan Dang: Validation, Supervision.

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Conflict of Interest Form Dear Editor, There are no conflicts of interest. Sincerely Yours, Wei Li Aug 19, 2019

Credit Author Statement Xianlun Tang: Conceptualization, Methodology, Resources, Project administration, Funding acquisition, Writing Review & Editing. Wei Li: Conceptualization, Methodology, Software, Investigation, Writing - Original Draft, Writing - Review & Editing. Xingchen Li: Validation, Investigation, Writing - Review & Editing. Weichang Ma: Investigation, Writing - Review & Editing. Xiaoyuan Dang: Validation, Supervision.

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