Electroencephalografic (EEG) data are complex multi-dimensional time-series
that are very useful in many applications, from diagnostics to driving
brain-computer interface systems. Their classification is still a challenging
task, due to the inherent within- and between-subject variability and their low
signal-to-noise ratio. On the other hand, the reconstruction of raw EEG data is
even more difficult because of the high temporal resolution of these signals.
Recent literature has proposed numerous machine and deep learning models that
could classify, e.g., different types of movements, with an accuracy in the
range 70% to 80% (with 4 classes). On the other hand, a limited number of works
targeted the reconstruction problem, with very limited results. In this work,
we propose vEEGNet, a DL architecture with two modules, i.e., an unsupervised
module based on variational autoencoders to extract a latent representation of
the data, and a supervised module based on a feed-forward neural network to
classify different movements. To build the encoder and the decoder of VAE we
exploited the well-known EEGNet network. We implemented two slightly different
architectures of vEEGNet, thus showing state-of-the-art classification
performance, and the ability to reconstruct both low-frequency and middle-range
components of the raw EEG. Although preliminary, this work is promising as we
found out that the low-frequency reconstructed signals are consistent with the
so-called motor-related cortical potentials, well-known motor-related EEG
patterns and we could improve over previous literature by reconstructing faster
EEG components, too. Further investigations are needed to explore the
potentialities of vEEGNet in reconstructing the full EEG data, generating new
samples, and studying the relationship between classification and
reconstruction performance.

该论文提出了 vEEGNet 模型，结合了变分自编码器和前馈神经网络，用于对复杂的多维时间序列电脑脑电图（EEG）数据进行分类和重构，取得了最先进的分类性能，并能够重构低频和中频范围的原始 EEG 信号。

vEEGNet: 通过变分自动编码器学习潜在表示来重构 EEG 原始数据

vEEGNet: learning latent representations to reconstruct EEG raw data via  variational autoencoders

Brain computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This neural signal is generally
chosen from a variety of well-studied electroencephalogram (EEG) signals. For a
given BCI paradigm, feature extractors and classifiers are tailored to the
distinct characteristics of its expected EEG control signal, limiting its
application to that specific signal. Convolutional Neural Networks (CNNs),
which have been used in computer vision and speech recognition, have
successfully been applied to EEG-based BCIs; however, they have mainly been
applied to single BCI paradigms and thus it remains unclear how these
architectures generalize to other paradigms. Here, we ask if we can design a
single CNN architecture to accurately classify EEG signals from different BCI
paradigms, while simultaneously being as compact as possible. In this work we
introduce EEGNet, a compact convolutional network for EEG-based BCIs. We
introduce the use of depthwise and separable convolutions to construct an
EEG-specific model which encapsulates well-known EEG feature extraction
concepts for BCI. We compare EEGNet to current state-of-the-art approaches
across four BCI paradigms: P300 visual-evoked potentials, error-related
negativity responses (ERN), movement-related cortical potentials (MRCP), and
sensory motor rhythms (SMR). We show that EEGNet generalizes across paradigms
better than the reference algorithms when only limited training data is
available. We demonstrate three different approaches to visualize the contents
of a trained EEGNet model to enable interpretation of the learned features. Our
results suggest that EEGNet is robust enough to learn a wide variety of
interpretable features over a range of BCI tasks, suggesting that the observed
performances were not due to artifact or noise sources in the data.

本文主要介绍一种基于深度卷积神经网络的脑 - 计算机接口通信模型 EEGNet，该模型考虑到不同 BCI 范式中 EEG 信号的特点，成功解决了不同信号间的分类问题。