Self-supervised learning has produced impressive results in multimedia
domains of audio, vision and speech. This paradigm is equally, if not more,
relevant for the domain of biosignals, owing to the scarcity of labelled data
in such scenarios. The ability to leverage large-scale unlabelled data to learn
robust representations could help improve the performance of numerous inference
tasks on biosignals. Given the inherent domain differences between multimedia
modalities and biosignals, the established objectives for self-supervised
learning may not translate well to this domain. Hence, there is an unmet need
to adapt these methods to biosignal analysis. In this work we propose a
self-supervised model for EEG, which provides robust performance and remarkable
parameter efficiency by using state space-based deep learning architecture. We
also propose a novel knowledge-guided pre-training objective that accounts for
the idiosyncrasies of the EEG signal. The results indicate improved embedding
representation learning and downstream performance compared to prior works on
exemplary tasks. Also, the proposed objective significantly reduces the amount
of pre-training data required to obtain performance equivalent to prior works.

提出了一种自监督模型用于 EEG 信号分析，采用基于状态空间的深度学习架构，提供了稳健性能和显著参数效率，同时提出了一种新颖的知识引导的预训练目标，改善了嵌入式表示学习和下游任务的性能。

知识引导的脑电图表示学习

Knowledge-guided EEG Representation Learning

Embedding representation learning via neural networks is at the core
foundation of modern similarity based search. While much effort has been put in
developing algorithms for learning binary hamming code representations for
search efficiency, this still requires a linear scan of the entire dataset per
each query and trades off the search accuracy through binarization. To this
end, we consider the problem of directly learning a quantizable embedding
representation and the sparse binary hash code end-to-end which can be used to
construct an efficient hash table not only providing significant search
reduction in the number of data but also achieving the state of the art search
accuracy outperforming previous state of the art deep metric learning methods.
We also show that finding the optimal sparse binary hash code in a mini-batch
can be computed exactly in polynomial time by solving a minimum cost flow
problem. Our results on Cifar-100 and on ImageNet datasets show the state of
the art search accuracy in precision@k and NMI metrics while providing up to
98X and 478X search speedup respectively over exhaustive linear search. The
source code is available at
this https URL

通过神经网络进行嵌入表示学习是现代基于相似性的搜索的核心基础。该研究旨在直接学习可量化的嵌入表示和稀疏二进制哈希码，从而实现构建高效哈希表，提供显著的搜索数据减少和超越以往的度量学习方法的最新搜索准确性。