EEG signal is important for brain-computer interfaces (BCI). Nevertheless, existing dry and wet electrodes are difficult to balance between high signal-to-noise ratio and portability in EEG recording, which limits the practical use of BCI. In this study, we propose a Denoising Encoder via Contrastive Alignment Network (DECAN) for dry electrode EEG, under the assumption of the EEG representation consistency between wet and dry electrodes during the same task. Specifically, DECAN employs two parameter-sharing deep neural networks to extract task-relevant representations of dry and wet electrode signals, and then integrates a representation-consistent contrastive loss to minimize the distance between representations from the same timestamp and category but different devices. To assess the feasibility of our approach, we construct an emotion dataset consisting of paired dry and wet electrode EEG signals from 16 subjects with 5 emotions, named PaDWEED. Results on PaDWEED show that DECAN achieves an average accuracy increase of 6.94$\%$ comparing to state-of-the art performance in emotion recognition of dry electrodes. Ablation studies demonstrate a decrease in inter-class aliasing along with noteworthy accuracy enhancements in the delta and beta frequency bands. Moreover, an inter-subject feature alignment can obtain an accuracy improvement of 5.99$\%$ and 5.14$\%$ in intra- and inter-dataset scenarios, respectively. Our proposed method may open up new avenues for BCI with dry electrodes. PaDWEED dataset used in this study is freely available at https://huggingface.co/datasets/peiyu999/PaDWEED.

翻译：脑电信号对脑机接口至关重要。然而，现有干电极和湿电极难以在脑电记录的高信噪比与便携性之间取得平衡，这限制了脑机接口的实际应用。本研究提出一种基于对比对齐网络的去噪编码器，其基于相同任务下湿电极与干电极脑电表征一致性的假设。具体而言，DECAN采用两个参数共享的深度神经网络提取干湿电极信号中任务相关的表征，并通过表征一致性对比损失最小化来自相同时间戳与类别但不同设备的表征距离。为验证方法的可行性，我们构建了包含16名受试者5种情绪的配对干湿电极脑电数据集PaDWEED。在PaDWEED上的实验表明，DECAN在干电极情绪识别任务中相比现有最优方法平均准确率提升6.94%。消融研究显示该方法能降低类间混淆，并在δ与β频段带来显著的准确率提升。此外，跨被试特征对齐在数据集内与跨数据集场景中分别实现5.99%与5.14%的准确率提升。本方法或将为干电极脑机接口开辟新途径。研究所用PaDWEED数据集公开于https://huggingface.co/datasets/peiyu999/PaDWEED。