Electroencephalography (EEG) is a prominent non-invasive neuroimaging technique providing insights into brain function. Unfortunately, EEG data exhibit a high degree of noise and variability across subjects hampering generalizable signal extraction. Therefore, a key aim in EEG analysis is to extract the underlying neural activation (content) as well as to account for the individual subject variability (style). We hypothesize that the ability to convert EEG signals between tasks and subjects requires the extraction of latent representations accounting for content and style. Inspired by recent advancements in voice conversion technologies, we propose a novel contrastive split-latent permutation autoencoder (CSLP-AE) framework that directly optimizes for EEG conversion. Importantly, the latent representations are guided using contrastive learning to promote the latent splits to explicitly represent subject (style) and task (content). We contrast CSLP-AE to conventional supervised, unsupervised (AE), and self-supervised (contrastive learning) training and find that the proposed approach provides favorable generalizable characterizations of subject and task. Importantly, the procedure also enables zero-shot conversion between unseen subjects. While the present work only considers conversion of EEG, the proposed CSLP-AE provides a general framework for signal conversion and extraction of content (task activation) and style (subject variability) components of general interest for the modeling and analysis of biological signals.

翻译：脑电图作为一种重要的非侵入性神经影像技术，能够提供对脑功能的深刻见解。然而，EEG数据存在较高的噪声和受试者间差异性，阻碍了可泛化信号的提取。因此，EEG分析的一个关键目标是提取潜在的神经活动（内容），同时表征个体受试者间的变异性（风格）。我们假设，在不同任务和受试者之间转换EEG信号需要提取包含内容与风格的潜在表征。受语音转换技术最新进展的启发，我们提出了一种新颖的对比分裂-潜变量置换自编码器框架，该框架直接针对EEG转换进行优化。重要的是，我们利用对比学习引导潜在表征，以明确分离出表征受试者（风格）和任务（内容）的潜变量分裂。我们将CSLP-AE与传统的监督、无监督（AE）及自监督（对比学习）训练方法进行对比，发现所提出的方法能够更优地泛化表征受试者与任务的特性。关键的是，该流程还能实现未见受试者间的零样本转换。尽管本研究仅涉及EEG转换，但所提出的CSLP-AE为生物信号建模与分析中普遍关注的内容（任务激活）与风格（受试者变异性）成分的提取及信号转换提供了通用框架。