With the recent success of artificial intelligence in neuroscience, a number
of deep learning (DL) models were proposed for classification, anomaly
detection, and pattern recognition tasks in electroencephalography (EEG). EEG
is a multi-channel time-series that provides information about the individual
brain activity for diagnostics, neuro-rehabilitation, and other applications
(including emotions recognition). Two main issues challenge the existing
DL-based modeling methods for EEG: the high variability between subjects and
the low signal-to-noise ratio making it difficult to ensure a good quality in
the EEG data. In this paper, we propose two variational autoencoder models,
namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG
reconstruction. We properly designed their architectures using the blocks of
the well-known EEGNet as the encoder, and proposed a loss function based on
dynamic time warping. We tested the models on the public Dataset 2a - BCI
Competition IV, where EEG was collected from 9 subjects and 22 channels.
hvEEGNet was found to reconstruct the EEG data with very high-fidelity,
outperforming most previous solutions (including our vEEGNet-ver3 ).
Furthermore, this was consistent across all subjects. Interestingly, hvEEGNet
made it possible to discover that this popular dataset includes a number of
corrupted EEG recordings that might have influenced previous literature
results. We also investigated the training behaviour of our models and related
it with the quality and the size of the input EEG dataset, aiming at opening a
new research debate on this relationship. In the future, hvEEGNet could be used
as anomaly (e.g., artefact) detector in large EEG datasets to support the
domain experts, but also the latent representations it provides could be used
in other classification problems and EEG data generation.

该研究提出了两种变分自编码器模型（vEEGNet-ver3 和 hvEEGNet），用于解决脑电重建中的问题。hvEEGNet 能够以非常高的准确度重建脑电数据，并发现了之前研究结果可能受到的腐败脑电记录的影响，同时提供了潜在的表征，用于其他分类问题和脑电数据生成。