This paper presents a novel graph convolutional neural network (GCNN)-based
approach for improving the diagnosis of neurological diseases using
scalp-electroencephalograms (EEGs). Although EEG is one of the main tests used
for neurological-disease diagnosis, the sensitivity of EEG-based expert visual
diagnosis remains at $\sim$50\%. This indicates a clear need for advanced
methodology to reduce the false negative rate in detecting abnormal scalp-EEGs.
In that context, we focus on the problem of distinguishing the abnormal scalp
EEGs of patients with neurological diseases, which were originally classified
as 'normal' by experts, from the scalp EEGs of healthy individuals. The
contributions of this paper are three-fold: 1) we present EEG-GCNN, a novel
GCNN model for EEG data that captures both the spatial and functional
connectivity between the scalp electrodes, 2) using EEG-GCNN, we perform the
first large-scale evaluation of the aforementioned hypothesis, and 3) using two
large scalp-EEG databases, we demonstrate that EEG-GCNN significantly
outperforms the human baseline and classical machine learning (ML) baselines,
with an AUC of 0.90.

使用 EEG-GCNN 模型对神经疾病的诊断进行改进，重点是区分由专家分类为 “正常” 的病人的异常头皮脑电波与健康人的头皮脑电波。在两个大型头皮脑电波数据库中的实验表明，EEG-GCNN 模型的 AUC 值达到 0.90，表现显著优于人工基准和传统机器学习基准。

基于域引导图卷积神经网络增强基于脑电图的神经疾病诊断

EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease  Diagnosis using a Domain-guided Graph Convolutional Neural Network

An electroencephalography (EEG) based brain activity recognition is a
fundamental field of study for a number of significant applications such as
intention prediction, appliance control, and neurological disease diagnosis in
smart home and smart healthcare domains. Existing techniques mostly focus on
binary brain activity recognition for a single person, which limits their
deployment in wider and complex practical scenarios. Therefore, multi-person
and multi-class brain activity recognition has obtained popularity recently.
Another challenge faced by brain activity recognition is the low recognition
accuracy due to the massive noises and the low signal-to-noise ratio in EEG
signals. Moreover, the feature engineering in EEG processing is time-consuming
and highly re- lies on the expert experience. In this paper, we attempt to
solve the above challenges by proposing an approach which has better EEG
interpretation ability via raw Electroencephalography (EEG) signal analysis for
multi-person and multi-class brain activity recognition. Specifically, we
analyze inter-class and inter-person EEG signal characteristics, based on which
to capture the discrepancy of inter-class EEG data. Then, we adopt an
Autoencoder layer to automatically refine the raw EEG signals by eliminating
various artifacts. We evaluate our approach on both a public and a local EEG
datasets and conduct extensive experiments to explore the effect of several
factors (such as normalization methods, training data size, and Autoencoder
hidden neuron size) on the recognition results. The experimental results show
that our approach achieves a high accuracy comparing to competitive
state-of-the-art methods, indicating its potential in promoting future research
on multi-person EEG recognition.

本文提出了一种新方法，通过对多人和多类脑活动的原始脑电信号进行分析，利用不同类别的脑电数据差异来捕捉 EEG 信号的特征，并采用自动编码器模型自动地提取 EEG 信号的相关特征，从而提高 EEG 信号的释义能力和准确度， 进而解决智能家居和智能医疗领域中 EEG 信号处理的一些挑战。在公共数据集和本地数据集上，本文通过大量实验验证了该方法的高准确性，表现优于竞争对手最先进的 EEG 信号处理方法，并具有促进未来多人 EEG 识别研究的潜力。