# Group Feature Learning and Domain Adversarial Neural Network for aMCI
Diagnosis System Based on EEG

Chen-Chen Fan∗,1,2, Haiqun Xie∗,5, Liang Peng1, Hongjun Yang1, Zhen-Liang
Ni1,2, Guan’an Wang1,2,
Yan-Jie Zhou1,2, Sheng Chen1,2, Zhijie Fang1,2, Shuyun Huang5, Zeng-Guang
Hou1,2,3,4, Fellow, IEEE 1State Key Laboratory of Management and Control for
Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing
100190, China2University of Chinese Academy of Sciences, Beijing 100049,
China3CAS Center for Excellence in Brain Science and Intelligence Technology,
Beijing 100190, China4Joint Laboratory of Intelligence Science and Technology,
Institute of Systems Engineering, Macau University of Science and Technology,
China5Department of Neurology, First People’s Hospital of Foshan, Foshan,
Guangdong 528000, China. Corresponding author: Zeng-Guang Hou.
<EMAIL_ADDRESS>authors contributed
equally to this work

###### Abstract

Medical diagnostic robot systems have been paid more and more attention due to
its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI)
is considered an effective means to prevent Alzheimer’s disease (AD). Doctors
diagnose MCI based on various clinical examinations, which are expensive and
the diagnosis results rely on the knowledge of doctors. Therefore, it is
necessary to develop a robot diagnostic system to eliminate the influence of
human factors and obtain a higher accuracy rate. In this paper, we propose a
novel Group Feature Domain Adversarial Neural Network (GF-DANN) for amnestic
MCI (aMCI) diagnosis, which involves two important modules. A Group Feature
Extraction (GFE) module is proposed to reduce individual differences by
learning group-level features through adversarial learning. A Dual Branch
Domain Adaptation (DBDA) module is carefully designed to reduce the
distribution difference between the source and target domain in a domain
adaption way. On three types of data set, GF-DANN achieves the best accuracy
compared with classic machine learning and deep learning methods. On the DMS
data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity
and specificity are 90% and 89%. In addition, by comparing three EEG data
collection paradigms, our results demonstrate that the DMS paradigm has the
potential to build an aMCI diagnose robot system.

##  I INTRODUCTION

Machine learning method can help the construction of more effective robots in
the medical field [1, 2], and has been widely used in disease diagnosis
systems. The World Alzheimer Report 2015 [3] points out that there are around
46 million people with Alzheimer’s disease (AD) worldwide. Mild cognitive
impairment (MCI) is considered as an intermediate transition state between
normal aging and AD, i.e. the early stage of AD. MCI patients convert to AD at
a rate of 10% to 15% per year, which is ten times that of normal elderly
people [4]. Amnestic mild cognitive impairment (aMCI) is a subtype of MCI, and
memory impairment is the central and earliest symptom. Since there is no
widely used specific drug for AD, early prevention of AD is critical. People
with aMCI have a high risk of developing AD [5], so the diagnosis of aMCI
plays a significant role in the early recognition and early intervention of
AD. However, the diagnosis of aMCI requires a variety of expensive medical
examinations (e.g., MRI, PET) and the doctors of the high level. Due to the
uneven distribution of medical resources, some people living in remote areas
cannot timely screen for aMCI, resulting in a greatly increased risk of AD.
Therefore, the construction of a medical robot diagnostic system for aMCI
diagnosis will help to improve the medical condition in remote areas.

As a widely used electrophysiological examination method, EEG can record the
brain’s spontaneous biological potential and reflect the brain’s activity. Its
advantages are higher time resolution, relatively low cost, and portable
equipment. However, there are considerable differences in EEG signals among
different individuals, which brings significant challenges to the diagnosis of
aMCI. Disease diagnosis based on EEG signals faces several challenges. The
first one is robust feature extraction. In the same group, the data
distributions among individuals are very different. It might force the neural
network tend to remember individual features instead of general and key
information of aMCI.

The second one is domain bias. Besides the differences among individuals, the
data distribution of the same person is also affected by the temporal-spatial
factors. For example, morning (after having a good sleeping) and a quiet place
contribute to EEG of high quality, but night (easy to be tired) and a noisy
place affect EEG quality a lot. Thus there is no guarantee that the train and
test data have the same or similar data distribution. The different
distribution of train and test data makes the model performs worse on test
data.

Recently, some studies have tried to diagnose MCI or aMCI. Most of these
methods using various machine learning algorithms to build classifier [6]. The
regional spectral features of EEG and features based on complexity were
extracted and classified by SVM [7].

Neural fuzzy k-nearest neighbor classifier was proposed for EEG classification
[8]. Relative power (RP) was classified as a feature input neural network to
diagnose MCI [9]. A deep machine learning method based on convolutional neural
network and autoencoder MLP was proposed to learn features [10]. Implicit
compression time function (IFAST) compressed closed-eye resting EEG data into
spatial invariants of instantaneous voltage distribution, and employing MLP
for the classification task [11].

Most of these methods do not consider the difference in data distribution
among the train and test data. Specifically, traditional supervised learning
methods assume that the train and test data have the same or similar
distribution, thus the model trained by the train set can be applied to
predict the label of the test set. However, there are considerable differences
in EEG signals among different individuals, and the train and test set cannot
satisfy the assumption of similar distribution, thus limiting the performance
of these methods.

To extract robust features related to aMCI, we propose a group feature
extraction framework GFE (Figure 2). All train data is divided into two groups
according to labels (aMCI and HC: healthy control), and each group of data
contains multiple individuals (each individual has multiple samples). For the
samples in each group, a feature extractor is used to generate a feature
representation. The individual discriminator is used to determine which
individual the sample comes from. Through the adversarial learning, the
feature extractor generates a feature representation that confuses the
individual discriminator. These group-level features reduce the difference
among different individuals in the same group and emphasize the corresponding
group attributes. Thus group-level features have more potential to be truly
relevant to the disease.

To alleviate the domain bias, we propose a Dual Branch Domain Adaptation
architecture (DBDA) as in Figure 2, which enables a more general assumption
that train and test data may not maintain the same distribution. DBDA reduces
the difference in the marginal distribution among the train and test data
through adversarial learning on the premise of the unknown test data label.
Specifically, through the adversarial learning of feature extractor and domain
discriminator, the feature extractor transforms the original data into a new
feature space, where the marginal distribution of the train and test data is
more similar.

Based on the above analysis, a group feature domain adversarial neural network
(GF-DANN) framework (Figure 2) is proposed, which combines GFE and DBDA for
multi-task learning. GF-DANN extracts more robust group features and uses
adversarial learning to solve the domain adaptation problem.

The contributions of this work can be concluded as follows:

  * •

We design the GFE module to learn group features by adversarial learning.

  * •

We introduce DBDA module to reduce the distribution difference between the
source and target domains.

  * •

The proposed method has achieved significantly better results than classic
deep learning and machine learning algorithms in all three data sets.

##  II Overview of GF-DANN Architechture

The architechture of GF-DANN is shown in Figure 2. It contains three modules:
Group Feature Extraction, Dual Branch Domain Adaptation, and Classification,
which correspond to three learning tasks respectively. Through multi-task
training, GF-DANN extracts more robust group features and reduces the
distribution difference between the source domain and the target domain. The
detailed mathematical description of GF-DANN is given below.

Figure 1: The feature extractor of GF-DANN.

Figure 2: The framework of GF-DANN. It contains three modules: Group Feature
Extraction (GFE), Dual Branch Domain Adaptation (DBDA), and Classification
(C). The solid black line represents the source domain data, the solid red
line is the target domain data, and the black dashed line denotes a specific
category of train data.

###  II-A Group Feature Extraction Module

EEG data has individual differences, even in the same group. To extract robust
features related to aMCI, GFE module learns the common features in each group
through two pairs of individual discriminators and feature extractors to
eliminate the influence of individual differences. It includes two feature
extractors, two individual discriminators, and two gradient reverse layers.

Feature Extractor: The input data (shallow feature tensor) is defined as
$x_{(u,v)}^{c_{i}}\in R^{C\times K\times T}$, where
$c_{i}\in\left\\{c_{1},c_{2}\right\\}$ indicates that the sample is from the
aMCI group or HC group. The individual label set is defined as
$I_{1}=\\{1,\cdots,\mathrm{i},\cdots,m\\}$ and
$I_{2}=\\{1,\cdots,\mathrm{j},\cdots,n\\}$, where $m$ ($n$) is the total
number of aMCI (HC) group subjects. $u\in I_{1}$ (if $c_{i}=c_{1}$) or $u\in
I_{2}$ (if $c_{i}=c_{2}$) is the individual label in the corresponding group,
and $v$ indicates the individual sample number. Therefore, $x_{(u,v)}^{c_{i}}$
represents the $v$-th sample of the individual with the individual number $u$
in the group $c_{i}$. $C$ represents the dimension of the feature, $K$
represents the number of frequency bands, and $T$ represents the number of
time bands. The acquisition of input data is described in detail in section
IV-B.

The structure of the feature extractor is shown in Figure 1. As the input data
is not an image, pooling layer is not used in order not to lose the feature
information. To reduce the amount of parameters and computation cost, the
input data pass through three $3\times 3$ depthwise separable convolution [12]
with bn and relu activation layers to extract features. Then the $1\times 1$
convolution is used to reduce the number of channels and finally stretch the
extracted features to one dimension to obtain the group feature vector $F$.

Define $X_{R}$ to represent all samples. According to the samples from the
source domain and the target domain, $X_{R}=X_{S}\cup X_{T}$ can be obtained,
where $X_{S}$ represents the source domain data set and $X_{T}$ represents the
target domain data set. Define the set
$M=\left\\{p_{1},p_{2},\cdots,p_{i},\cdots,p_{m}\right\\}$ and
$N=\left\\{q_{1},q_{2},\cdots,q_{j},\cdots,q_{n}\right\\}$, where $m$ ($n$) is
the number of subjects in group $c_{1}$ ($c_{2}$) and $p_{i}$ ($q_{j}$)
represents the number of samples owned by subject with individual label $i$
($j$) from the group $c_{1}$ ($c_{2}$). According to the group label of the
train set, $X_{S}$ can be expressed as:

$X_{S}=X_{S}^{c_{1}}\cup X_{S}^{c_{2}}$ (1) $X_{S}^{c_{1}}=X_{1}^{c_{1}}\cup
X_{2}^{c_{1}}\cdots\cup X_{i}^{c_{1}}\cdots\cup X_{m}^{c_{1}}$ (2)
$X_{S}^{c_{2}}=X_{1}^{c_{2}}\cup X_{2}^{c_{2}}\cdots\cup
X_{j}^{c_{2}}\cdots\cup X_{n}^{c_{2}}$ (3)

where
$X_{i}^{c_{1}}=\left\\{x_{(i,1)}^{c_{1}},x_{(i,2)}^{c_{1}},\cdots,x_{\left(i,p_{i}\right)}^{c_{1}}\right\\}$,$1\leq
i\leq m$,
$X_{j}^{c_{2}}=\left\\{x_{(j,1)}^{c_{2}},x_{(j,2)}^{c_{2}},\cdots,x_{\left(j,q_{j}\right)}^{c_{2}}\right\\}$,
$1\leq j\leq n$.

The features extracted by the feature extractor can be formulated as:

$F_{S_{k}}^{c_{k}}=\left\\{\xi_{k}(x)|x\in X_{S}^{c_{k}}\right\\}$ (4)
$F_{S_{k}}=\left\\{\xi_{k}(x)|x\in X_{S}\right\\}$ (5)
$F_{T_{k}}=\left\\{\xi_{k}(x)|x\in X_{T}\right\\}$ (6)

where $k\in\left\\{1,2\right\\}$, $\xi_{k}:R^{C\times K\times T}\rightarrow
R^{d}$ denotes the function map of feature extractor-$k$, $d$ is the length of
the feature vector.

Individual Discriminator: Individual discriminators 1 and 2 are trained by
individual label sets $I_{1}$ and $I_{2}$, respectively. GRL is a module
proposed by [13], which keeps the input unchanged during forwarding
propagation, and multiplies the negative scalar during reverse propagation to
reverse the gradient, which can maximize the loss of the discriminator. The
individual discriminator strives to distinguish which subject the input data
come from, while the feature extractor updates the parameters by maximizing
the loss of individual discriminator and strives to generate a data
representation to confuse the individual discriminator. The loss function of
the individual discriminator is defined as follows:

$L_{d_{1}}\left(X_{S}^{c_{1}};\theta_{f_{1}},\theta_{d_{1}}\right)=\ell_{d_{1}}\left(\psi_{d_{1}}\left(\xi_{1}\left(X_{S}^{c_{1}};\theta_{f_{1}}\right);\theta_{d_{1}}\right),Y_{d_{1}}\right)$
(7)
$L_{d_{2}}\left(X_{S}^{c_{2}};\theta_{f_{2}},\theta_{d_{2}}\right)=\ell_{d_{2}}\left(\psi_{d_{2}}\left(\xi_{2}\left(X_{S}^{c_{2}};\theta_{f_{2}}\right);\theta_{d_{2}}\right),Y_{d_{2}}\right)$
(8)

where $\ell_{d_{1}},\ell_{d_{2}}$ denote loss functions,
$\psi_{d_{1}},\psi_{d_{2}}$ are individual label classifiers, $Y_{d_{1}}\in
I_{1},Y_{d_{2}}\in I_{2}$ represent individual labels,
$\theta_{f_{1}},\theta_{f_{2}},\theta_{d_{1}},\theta_{d_{2}}$ are the
parameters of the two feature extractors and two individual discriminators.

###  II-B Dual Branch Domain Adaptation Module

There are differences among the source domain and target domain data of EEG
signals. To reduce this difference, DBDA module extracts features through a
dual-branch feature extractor and conducts adversarial training with the
domain discriminator. DBDA includes two feature extractors (shared with GFE),
two individual discriminators, and a gradient reverse layer.

Domain Discriminator: The domain discriminator is trained by the domain label
set $D=\\{0,1\\}$, Where $0$ and $1$ represent data from the source domain and
target domain, respectively. Domain discriminator narrows the feature
distribution gap between the source and target domains. Specifically, the
domain discriminator determines which domain the input data come from. The two
feature extractors update their parameters by maximizing the loss of the
domain discriminator and strive to generate data representations to confuse
the judgment of the domain discriminator. The loss function of the domain
discriminator is defined as follows:

$L_{d_{3}}\left(X_{R};\theta_{f_{1}},\theta_{f_{2}},\theta_{d_{3}}\right)=\ell_{d_{3}}\left(\tilde{Y}_{d_{3}},Y_{d_{3}}\right)$
(9)
$\tilde{Y}_{d_{3}}=\psi_{d_{3}}\left(\xi_{1}\left(X_{R};\theta_{f_{1}}\right),\xi_{2}\left(X_{R};\theta_{f_{2}}\right);\theta_{d_{3}}\right)$
(10)

where $\ell_{d_{3}}$ denotes loss function, $\tilde{Y}_{d_{3}}\in D$
represents the prediction result of $\psi_{d_{3}}$, $Y_{d_{3}}\in D$ denotes
domain label, $\psi_{d_{3}}$ is domain label classifier, $\theta_{d_{3}}$
represents the parameters of domain discriminator.

Algorithm 1 Optimization of GF-DANN

0: Individual discriminator train data: $X_{S}^{c_{1}},X_{S}^{c_{2}}$;
Individual discriminator train label: $Y_{d_{1}},Y_{d_{2}}$; Domain
discriminator train data: $X_{R}$; Domain discriminator train label:
$Y_{d_{3}}$; Classifier train data: $X_{S}$; Classifier train label: $Y_{c}$;
Initial learning rate:
$\alpha_{d_{1}},\alpha_{d_{2}},\alpha_{d_{3}},\alpha_{c}$ Discriminator and
classifier train epochs: $n_{d}$, $n_{c}$

0:
$\hat{\theta}_{f_{1}},\hat{\theta}_{f_{2}},\hat{\theta}_{c},\hat{\theta}_{d_{1}},\hat{\theta}_{d_{2}},\hat{\theta}_{d_{3}}$

1: for $i=1$ to $n_{d}$ do

2: Input $X_{S}^{c_{1}}$ and $Y_{d_{1}}$ to update parameters
$\theta_{f_{1}},\theta_{d_{1}}$:
$\theta_{f_{1}}\leftarrow\theta_{f_{1}}+\alpha_{d_{1}}\frac{\partial
L_{d_{1}}}{\partial\theta_{f_{1}}},\theta_{d_{1}}\leftarrow\theta_{d_{1}}-\alpha_{d_{1}}\frac{\partial
L_{d_{1}}}{\partial\theta_{d_{1}}}$;

3: Input $X_{S}^{c_{2}}$ and $Y_{d_{2}}$ to update parameters
$\theta_{f_{2}},\theta_{d_{2}}$:
$\theta_{f_{2}}\leftarrow\theta_{f_{2}}+\alpha_{d_{2}}\frac{\partial
L_{d_{2}}}{\partial\theta_{f_{2}}},\theta_{d_{2}}\leftarrow\theta_{d_{2}}-\alpha_{d_{2}}\frac{\partial
L_{d_{2}}}{\partial\theta_{d_{2}}}$;

4: Input $X_{R}$ and $Y_{d_{3}}$ to update parameters
$\theta_{f_{1}},\theta_{f_{2}},\theta_{d_{3}}$:
$\theta_{f_{1}}\leftarrow\theta_{f_{1}}+\alpha_{d_{3}}\frac{\partial
L_{d_{3}}}{\partial\theta_{f_{1}}},\theta_{f_{2}}\leftarrow\theta_{f_{2}}+\alpha_{d_{3}}\frac{\partial
L_{d_{3}}}{\partial\theta_{f_{2}}}$,
$\theta_{d_{3}}\leftarrow\theta_{d_{3}}-\alpha_{d_{3}}\frac{\partial
L_{d_{3}}}{\partial\theta_{d_{3}}}$;

5: end for

6: for $j=1$ to $n_{c}$ do

7: Input $X_{S}$ and $Y_{c}$ to update parameters
$\theta_{f_{1}},\theta_{f_{2}},\theta_{c}$:
$\theta_{f_{1}}\leftarrow\theta_{f_{1}}-\alpha_{c}\frac{\partial
L_{c}}{\partial\theta_{f_{1}}},\theta_{f_{2}}\leftarrow\theta_{f_{2}}-\alpha_{c}\frac{\partial
L_{c}}{\partial\theta_{f_{2}}}$,
$\theta_{c}\leftarrow\theta_{c}-\alpha_{c}\frac{\partial
L_{c}}{\partial\theta_{c}}$;

8: end for

9: return
$\hat{\theta}_{f_{1}},\hat{\theta}_{f_{2}},\hat{\theta}_{c},\hat{\theta}_{d_{1}},\hat{\theta}_{d_{2}},\hat{\theta}_{d_{3}}$.

###  II-C Classification Module

To diagnose aMCI, the Classifier shares the feature extractor with GFE and
DBDA to use the learned features. Voting Diagnosis Framework is proposed to
give the diagnosis result.

Classifier: Fully connected layer and softmax are used to obtain the
classification results, which can be expressed as:

$f_{S}=\left[f_{S_{1}}^{T},f_{S_{2}}^{T}\right]^{T}$ (11)
$\tilde{y}=\eta\left(A*f_{S}+b\right)$ (12)

where $f_{S_{1}}\in F_{S_{1}}$, $f_{S_{2}}\in F_{S_{2}}$, $f_{S}\in R^{2d}$,
$A\in R^{2\times 2d}$, $b\in R^{2}$, $\eta$ denote softmax function. The loss
function of classifier can be expressed as:

$L_{c}\left(X_{S};\theta_{f_{1}},\theta_{f_{2}},\theta_{c}\right)=\ell_{c}\left(\tilde{Y}_{c},Y_{c}\right)$
(13)
$\tilde{Y}_{c}=\psi_{c}\left(\xi_{1}\left(X_{S};\theta_{f_{1}}\right),\xi_{2}\left(X_{S};\theta_{f_{2}}\right);\theta_{c}\right)$
(14)

where $\ell_{c}$ denotes loss function, $\hat{Y}_{c}\in\left\\{0,1\right\\}$
represents the prediction result of $\psi_{c}$, $Y_{c}\in\left\\{0,1\right\\}$
denotes true group label, $\psi_{c}$ is classifier, $\theta_{c}$ represents
the parameters of classifier.

Voting Diagnosis Framework: Since each subject has multiple samples, in order
to give the diagnosis results of the subject, we propose the voting diagnosis
framework . In the testing phase, all $k$ samples of a single subject obtain
the corresponding sample-level results
$\tilde{y}_{i}\in\\{0,1\\},i=1,\cdots,k$ through GF-DANN.

$\tilde{y}_{\text{subject}}=\left\\{\begin{array}[]{c}1,\text{if}\sum_{i=1}^{k}\tilde{y}_{i}>\frac{k}{2}\\\
0,\text{if}\sum_{i=1}^{k}\tilde{y}_{i}<\frac{k}{2}\\\
\text{refuse},\text{if}\sum_{i=1}^{k}\tilde{y}_{i}=\frac{k}{2}\end{array}\right.$
(15)

##  III Optimization of GF-DANN

The overall loss function is defined as:

$L\left(\mathbf{X}_{\mathcal{R}};\theta_{f_{1}},\theta_{f_{2}},\theta_{c},\theta_{d_{1}},\theta_{d_{2}},\theta_{d_{3}}\right)=L_{c}-L_{d_{1}}-L_{d_{2}}-L_{d_{3}}$
(16)

The optimization of GF-DANN can be described as the following minimax problem:

$\min_{\theta_{f_{1}},\theta_{f_{2}},\theta_{c}}\max_{\theta_{d_{1}},\theta_{d_{2}},\theta_{d_{3}}}L\left(\mathbf{X}_{\mathcal{R}};\theta_{f_{1}},\theta_{f_{2}},\theta_{c},\theta_{d_{1}},\theta_{d_{2}},\theta_{d_{3}}\right)$
(17)

The optimization algorithm of GF-DANN is shown in algorithm 1. The
optimization problem can be described as a minimax problem:

$\hat{\theta}_{f_{1}},\hat{\theta}_{d_{1}}=\arg\min_{\theta_{d_{1}}}\max_{\theta_{f_{1}}}L_{d_{1}}\left(X_{S}^{c_{1}};\theta_{f_{1}},\theta_{d_{1}}\right)$
(18)
$\hat{\theta}_{f_{2}},\hat{\theta}_{d_{2}}=\arg\min_{\theta_{d_{2}}}\max_{\theta_{f_{2}}}L_{d_{2}}\left(X_{S}^{c_{2}};\theta_{f_{2}},\theta_{d_{2}}\right)$
(19)
$\hat{\theta}_{f_{1}},\hat{\theta}_{f_{2}},\hat{\theta}_{d_{3}}=\arg\min_{\theta_{d_{3}}}\max_{\theta_{f_{1}},\theta_{f_{2}}}L_{d_{3}}\left(X_{R};\theta_{f_{1}},\theta_{f_{2}},\theta_{d_{3}}\right)$
(20)
$\hat{\theta}_{f_{1}},\hat{\theta}_{f_{2}},\hat{\theta}_{c}=\arg\min_{\theta_{f},\theta_{2},\theta_{c}}L_{c}\left(X_{S};\theta_{f_{1}},\theta_{f_{2}},\theta_{c}\right)$
(21)

To solve the minimax optimization problem of Eq.(18-20), we use the gradient
reverse layer (GRL) to transform the problem into a minimization problem. Adam
optimizer is used to minimize $L_{d_{1}}\left(L_{d_{2}},L_{d_{3}}\right)$. The
parameters $\theta_{f_{1}}\left(\theta_{f_{2}},\theta_{f_{1,2}}\right)$ before
GRL are updated in the direction of maximizing
$L_{d_{1}}\left(L_{d_{2}},L_{d_{3}}\right)$, and the parameter
$\theta_{d_{1}}\left(\theta_{d_{2}},\theta_{d_{3}}\right)$ after GRL are
updated in the direction of minimizing
$L_{d_{1}}\left(L_{d_{2}},L_{d_{3}}\right)$. For the problem of Eq.(21), we
use Adam optimizer to update parameters
$\theta_{f_{1}},\theta_{f_{2}},\theta_{c}$ to minimize $L_{c}$.

##  IV Experiments and Results

###  IV-A Dataset

TABLE I: Summary of participant demographics, MMSE and MOCA.

| Gender | Age | MMSE | MOCA
---|---|---|---|---
aMCI | 5 M / 5 F |  65.70$\pm$5.10 |  25.20$\pm$3.26 |  20.10$\pm$3.35
HC | 4 M / 5 F |  68.00$\pm$3.24 |  28.00$\pm$1.22 |  25.00$\pm$2.06
$p$ | 0.809 | 0.263 | $0.027^{*}$ | $0.001^{**}$
${}^{*}p<0.05\ ^{**}p<0.01$

TABLE II: Experimental results. Acc is the accuracy rate, ACP is the average confidence probability, Sen is the sensitivity, and Spe is the specificity. Resnet-18, Densenet-121, VGG-11-bn all have been pre-trained on ImageNet.  Method | DMS Dataset | REST Dataset | STROOP Dataset
---|---|---|---
Acc | ACP | Sen | Spec | Acc | ACP | Sen | Spe | Acc | ACP | Sen | Spe
KNN [14] | 73.68 | 75.48 | 80.00 | 66.67 | 42.11 | 50.49 | 80.00 | 0.00 | 47.37 | 45.99 | 30.00 | 66.67
LR classifier [15] | 78.95 | 75.92 | 70.00 | 88.89 | 57.89 | 56.45 | 90.00 | 22.22 | 42.11 | 43.19 | 20.00 | 66.67
SVM [16] | 84.21 | 78.45 | 90.00 | 77.78 | 57.89 | 56.59 | 90.00 | 22.22 | 68.42 | 61.57 | 80.00 | 55.56
Random Forest [17] | 84.21 | 79.14 | 80.00 | 89.00 | 52.63 | 42.59 | 90.00 | 11.00 | 57.89 | 59.01 | 60.00 | 55.56
Resnet-18 [18] | 84.21 | 78.28 | 80.00 | 88.89 | 57.89 | 52.47 | 100.00 | 11.11 | 63.16 | 53.13 | 60.00 | 66.67
Densenet-121 [19] | 78.95 | 69.71 | 70.00 | 88.89 | 57.89 | 59.83 | 70.00 | 44.44 | 68.42 | 63.82 | 70.00 | 66.67
VGG-11-bn [20] | 84.21 | 73.08 | 80.00 | 88.89 | 57.89 | 54.61 | 70.00 | 44.44 | 57.89 | 55.43 | 40.00 | 77.78
GF-DANN(ours) | 89.47 | 83.86 | 90.00 | 89.00 | 78.95 | 60.37 | 90.00 | 66.67 | 78.95 | 61.03 | 80.00 | 77.78
Figure 3: Data preprocess on DMS dataset.

EEG recordings were obtained at the First People’s Hospital of Foshan City,
Foshan, China. It includes ten patients with aMCI and nine healthy people. The
diagnosis results are given by professional doctors based on biomarkers and
scale scores. Summary of participant demographics, mini-mental state
examination (MMSE), and montreal cognitive assessment (MOCA) are shown in
Table I. There is no significant difference in age ($p=0.263$) and gender
($p=0.809$) distribution between the two groups, and there is a significant
difference in the MMSE ($p=0.027$) and MOCA ($p=0.001$) scores. EEG data were
collected using Wearable Sensing’s DSI-24 EEG device, with a sampling
frequency of 300 Hz. Twenty-one sensors positioned according to the 10-20
international system. These electrodes used Pz as the reference electrode
during the recording process, so the data of twenty electrodes were finally
obtained.

In this study, we collected three types of data by EEG equipment: (1) DMS [21]
Dataset: Each subject performed 150 trials. We extract the epoch data
corresponding to 2.5s (-1s, 1.5s) at the time of each trial start. The data
set size is 19 subjects and 2850 samples. (2) REST Dataset: participants were
asked to close their eyes and stay awake for 2 minutes to obtain resting data.
The data set is obtained by extract epoch data at 2s intervals. The data set
size is 19 subjects and 1140 samples. (3) STROOP [22] Dataset: Each subject
needs to make 80 judgments, we extract the epoch data corresponding to 3s
(-1s, 2s) at the time of each trial start. The data set size is 19 subjects
and 1520 samples. The detailed data collection method can be viewed in the
attached video.

(a) $X_{S}^{c_{1}}$ input to GF-DANN

(b) $X_{S}^{c_{2}}$ input to GF-DANN

(c) $X_{S}^{c_{1}}$ input to BaseNet-1

(d) $X_{S}^{c_{2}}$ input to BaseNet-1

Figure 4: Comparison of vectors obtained by inputting different categories of
data for two feature extractors. All points in the figure are the feature
vectors obtained by the corresponding input samples through the feature
extractor (for visualization, the feature vectors are reduced to two
dimensions by PCA), where the red dots and the yellow dots represent the
feature vectors extracted by feature extractors 1 and 2, respectively.

###  IV-B Shallow Feature Extraction

The collected data firstly use the band-pass filtering of 0.5-70 Hz and then
apply the notch filtering of 48-52 Hz to eliminate power line interference. We
select the epochs with correct responses by subjects and remove the bad epochs
by manual inspection. ICA algorithm is used to remove artifacts such as
electrooculograms and abnormal movements. Taking the DMS data set as an
example, the details of shallow feature extraction are as follows:

As shown in Figure 3, the epoch data is band-pass filtered with a window
length of 4 Hz and a step size of 2 Hz to obtain data in 13 frequency bands
among 2-30 Hz. For each frequency band, the data is divided into 5-time bands
(window length 0.5s, step size 0.5s). We use the MNE [23] toolkit to design
CSP [24, 25] filters for each frequency band and time band data. A total of 65
CSP filters are designed for the DMS data set. Note that in the experiment, we
use leave-one-group-out cross-validation. In each fold, the corresponding
train data is used to design the CSP filter, and then the train and test data
are filtered by the designed CSP filter. Therefore, the design of the CSP
filter does not cause leakage of test data. Finally, the first five components
after filtering are selected, and the average power is obtained for each
component to obtain the shallow feature tensor.

###  IV-C Implementation Details

We use leave-one-group-out cross-validation to evaluate model performance.
Each time, select samples of one subject (1/19) as the test set and the
remaining subject’s (18/19) data as the train set. According to the voting
diagnosis framework, in the testing stage, the corresponding sample-level
classification results $\tilde{y}_{i}\in\\{0,1\\},i=1,\cdots,k$ of the test
subject are obtained through GF-DANN. Subject-level diagnosis results
$\tilde{y}_{\text{subject}}$ were generated by voting on the classification
results of these sample levels. The confidence probability $P_{i}$ of a single
subject $i$ is defined as follows:

$P_{i}=\frac{N_{\text{correct}}(i)}{N(i)}$ (22)

where $N_{\text{correct}}(i)$ represents the number of correctly classified
samples of subject $i$, $N(i)$ represents the number of all samples of subject
$i$.

Define the average confidence probability (ACP) as follows:

$ACP=\frac{1}{N_{\text{subject}}}\sum_{i=1}^{N_{\text{subject}}}P_{i}$ (23)

where $N_{\text{subject}}$ represents the number of all subjects.

In this paper, the classification accuracy, average confidence probability
(ACP), sensitivity, and specificity are used as evaluation indicators.
Traditional machine learning and classic deep learning methods are used to
compare with GF-DANN. The machine learning method is based on scikit-learn and
performs a parameter search to obtain the best performing model. Deep learning
methods and GF-DANN is constructed based on PyTorch and trained using Tesla
V100-SXM2-32GB, using Adam as the optimizer, focal loss [26] as the loss
function. A learning rate decay strategy is used during the training process.
Each model is tested on the corresponding test set after training 20 epochs.

TABLE III: Ablation study for GFE and DBDA on DMS dataset.

Method | GFE | DBDA | Acc | ACP | Sen | Spe
---|---|---|---|---|---|---
BaseNet-1 |  |  | 78.95 | 77.57 | 80.00 | 78.00
BaseNet-2 | $\surd$ |  | 89.47 | 78.41 | 90.00 | 89.00
BaseNet-3 |  | $\surd$ | 89.47 | 79.93 | 90.00 | 89.00
GF-DANN | $\surd$ | $\surd$ | 89.47 | 83.86 | 90.00 | 89.00

###  IV-D Ablation Study

GFE is used to make the feature extractor learn the common features of the
individuals in the group. DBDA is used to reduce the feature distribution gap
between the source domain and the target domain. The ablation experiment is
used to demonstrate the performance of the discriminator. The experimental
results are shown in Table III.

GF-DANN without GFE and DBDA is used as the base network. When using GFE or
DBDA, the network outperforms the base network 10.52%, 10%, and 11% by
accuracy, sensitivity, and specificity. The effect of using both two types of
discriminators is mainly reflected in the improvement of the ACP. The network
applying DBDA outperforms the base network by 2.36% ACP. The ACP increases by
0.84% due to the use of GFE. The network applyling both GFE and DBDA achieves
83.86% ACP, which outperforms the base network by 6.29% ACP. These
improvements indicate that GFE and DBDA can significantly improve
classification accuracy.

###  IV-E Results on Three Dataset

GF-DANN achieved the best accuracy rates on all three data sets (Table II),
increasing by 5.26%, 21.06%, and 10.53% respectively from the second place
method. It is worth mentioning that in the DMS dataset, the Acc, ACP, Sen, and
Spe of our method are 89.47%, 83.86%, 90%, and 89%, respectively, which
demonstrates a result with potential for clinical application. All methods
have achieved higher scores in the DMS data set than the REST and STROOP data
sets, which shows that the DMS acquisition paradigm is more suitable for the
diagnosis of aMCI. The main clinical manifestation of aMCI is memory
impairment, which is more in line with the investigation of working memory
ability that DMS focuses on, which may be the reason for the better diagnosis
of DMS data sets.

###  IV-F Feature Vector Visualization

To confirm whether the two feature extractors of GF-DANN have learned the
corresponding group features, we conducted a feature vector visualization
experiment. Input $X_{S}^{c_{1}}$ or $X_{S}^{c_{2}}$ into the trained GF-DANN
or BaseNet-1 to obtain the feature vector sets F1 and F2 of the corresponding
class samples through the two feature extractors. After that, PCA dimension
reduction is performed on the feature vectors for two-dimensional
visualization. Figure 4 shows the feature vector visualization for four
configurations. All points in the figure are the feature vectors obtained by
the corresponding input samples through the feature extractor, where the red
dots and the yellow dots represent the feature vectors extracted by feature
extractors 1 and 2, respectively. For example, Figure 4(a) shows the two-
dimensional visualization of the corresponding two feature vector sets
obtained by the trained feature extractor extract class 1 data
$X_{S}^{c_{1}}$. It represents the distribution of the class 1 data after
passing through two feature extractors.

Comparing Figure 4(a) and 4(b), it can be seen that when the feature extractor
extracts the corresponding category data, the resulting feature vector
distribution is more concentrated. In contrast, when extracting other
categories of data, the feature vector distribution has a dispersed
distribution. This analysis demonstrates that the feature extractor of GF-DANN
has learned the common features of the corresponding categories in a targeted
manner, thus obtaining a more compact distribution. However, similar phenomena
cannot be observed in Figure 4(c) and 4(d).

Compared with BaseNet-1, the distance among the feature vector sets learned by
the two feature extractors of GF-DANN is more considerable. This shows that
the two feature extractors learn features with more significant differences,
which helps obtain more features for classification.

##  V Conclusion

This paper proposes a novel neural network and introduces the idea of
adversarial learning to solve the problem of huge differences in individual
data in aMCI diagnosis, which has achieved better results than classical
machine learning and deep learning methods during three types of EEG data
sets. We believe the proposed method takes a significant step in constructing
an EEG based medical diagnostic robot system.

## ACKNOWLEDGMENT

This work was supported in part by the National Key R&D Program of China
(Grant 2018YFC2001700), National Natural Science Foundation of China (Grants
61720106012, U1913601, 62073319, 62003343, U20A20224), Beijing Natural Science
Foundation (Grant L172050), the Youth Innovation Promotion Association of CAS
under Grant 2020140, and by the Strategic Priority Research Program of Chinese
Academy of Science (Grant XDB32040000).

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