Epilepsy is one of the most common neurological disorders, typically observed
via seizure episodes. Epileptic seizures are commonly monitored through
electroencephalogram (EEG) recordings due to their routine and low expense
collection. The stochastic nature of EEG makes seizure identification via
manual inspections performed by highly-trained experts a tedious endeavor,
motivating the use of automated identification. The literature on automated
identification focuses mostly on supervised learning methods requiring expert
labels of EEG segments that contain seizures, which are difficult to obtain.
Motivated by these observations, we pose seizure identification as an
unsupervised anomaly detection problem. To this end, we employ the first
unsupervised transformer-based model for seizure identification on raw EEG. We
train an autoencoder involving a transformer encoder via an unsupervised loss
function, incorporating a novel masking strategy uniquely designed for
multivariate time-series data such as EEG. Training employs EEG recordings that
do not contain any seizures, while seizures are identified with respect to
reconstruction errors at inference time. We evaluate our method on three
publicly available benchmark EEG datasets for distinguishing seizure vs.
non-seizure windows. Our method leads to significantly better seizure
identification performance than supervised learning counterparts, by up to 16%
recall, 9% accuracy, and 9% Area under the Receiver Operating Characteristics
Curve (AUC), establishing particular benefits on highly imbalanced data.
Through accurate seizure identification, our method could facilitate widely
accessible and early detection of epilepsy development, without needing
expensive label collection or manual feature extraction.

本研究提出了一种基于 Transformer 模型和无监督学习的方法，在没有专家标注和带有癫痫发作的 EEG 记录的情况下对癫痫发作进行检测和识别，实验结果表明该方法在识别癫痫发作方面的性能优于传统监督学习方法。

无监督多元时间序列变换器用于脑电信号癫痫识别

Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG

The electroencephalogram (EEG) provides a non-invasive, minimally
restrictive, and relatively low cost measure of mesoscale brain dynamics with
high temporal resolution. Although signals recorded in parallel by multiple,
near-adjacent EEG scalp electrode channels are highly-correlated and combine
signals from many different sources, biological and non-biological, independent
component analysis (ICA) has been shown to isolate the various source generator
processes underlying those recordings. Independent components (IC) found by ICA
decomposition can be manually inspected, selected, and interpreted, but doing
so requires both time and practice as ICs have no particular order or intrinsic
interpretations and therefore require further study of their properties.
Alternatively, sufficiently-accurate automated IC classifiers can be used to
classify ICs into broad source categories, speeding the analysis of EEG studies
with many subjects and enabling the use of ICA decomposition in near-real-time
applications. While many such classifiers have been proposed recently, this
work presents the ICLabel project comprised of (1) an IC dataset containing
spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG
recordings, (2) a website for collecting crowdsourced IC labels and educating
EEG researchers and practitioners about IC interpretation, and (3) the
automated ICLabel classifier. The classifier improves upon existing methods in
two ways: by improving the accuracy of the computed label estimates and by
enhancing its computational efficiency. The ICLabel classifier outperforms or
performs comparably to the previous best publicly available method for all
measured IC categories while computing those labels ten times faster than that
classifier as shown in a rigorous comparison against all other publicly
available EEG IC classifiers.

本文介绍 ICLabel 项目，该项目包括 IC 数据集、用于收集众包 IC 标签的网站和自动分类器。ICLabel 分类器的优点在于改进了计算的标签估计的准确性和增强了计算效率，它在所有测量 IC 类别中优于或与此前公开可用的最佳方法表现相当，并比该分类器快 10 倍。

ICLabel：一种自动化的脑电独立成分分类器、数据集和网站

ICLabel: An automated electroencephalographic independent component  classifier, dataset, and website

We introduce and compare several strategies for learning discriminative
features from electroencephalography (EEG) recordings using deep learning
techniques. EEG data are generally only available in small quantities, they are
high-dimensional with a poor signal-to-noise ratio, and there is considerable
variability between individual subjects and recording sessions. Our proposed
techniques specifically address these challenges for feature learning.
Cross-trial encoding forces auto-encoders to focus on features that are stable
across trials. Similarity-constraint encoders learn features that allow to
distinguish between classes by demanding that two trials from the same class
are more similar to each other than to trials from other classes. This
tuple-based training approach is especially suitable for small datasets.
Hydra-nets allow for separate processing pathways adapting to subsets of a
dataset and thus combine the advantages of individual feature learning (better
adaptation of early, low-level processing) with group model training (better
generalization of higher-level processing in deeper layers). This way, models
can, for instance, adapt to each subject individually to compensate for
differences in spatial patterns due to anatomical differences or variance in
electrode positions. The different techniques are evaluated using the publicly
available OpenMIIR dataset of EEG recordings taken while participants listened
to and imagined music.

本文介绍了使用深度学习技术从脑电图（EEG）记录中学习区分特征的几种策略，并比较了其效果。这些策略包括跨试验编码，相似性约束编码和 Hydra-nets。通过使用公开可用的 OpenMIIR EEG 数据集对这些策略进行了评估。