Abstract：
Objective.Recently, the demand for wearable devices using electroencephalography (EEG) has increased rapidly in many fields. Due to its volume and computation constraints, wearable devices usually compress and transmit EEG to external devices for analysis. However, current EEG compression algorithms are not tailor-made for wearable devices with limited computing and storage. Firstly, the huge amount of parameters makes it difficult to apply in wearable devices; secondly, it is tricky to learn EEG signals\' distribution law due to the low signal-to-noise ratio, which leads to excessive reconstruction error and suboptimal compression performance.Approach.Here, a feature enhanced asymmetric encoding-decoding network is proposed. EEG is encoded with a lightweight model, and subsequently decoded with a multi-level feature fusion network by extracting the encoded features deeply and reconstructing the signal through a two-branch structure.Main results.On public EEG datasets, motor imagery and event-related potentials, experimental results show that the proposed method has achieved the state of the art compression performance. In addition, the neural representation analysis and the classification performance of the reconstructed EEG signals also show that our method tends to retain more task-related information as the compression ratio increases and retains reliable discriminative information after EEG compression.Significance.This paper tailors an asymmetric EEG compression method for wearable devices that achieves state-of-the-art compression performance in a lightweight manner, paving the way for the application of EEG-based wearable devices.
摘要：
目标：最近，使用脑电图（EEG）的可穿戴设备的需求在许多领域迅速增加。由于其体积和计算限制，可穿戴设备通常将EEG压缩并传输到外部设备进行分析。然而,当前的EEG压缩算法不是为具有有限计算和存储的可穿戴设备量身定制的。首先,大量的参数使得其难以应用于可穿戴设备;其次,由于信噪比低，学习脑电图信号的分布规律很棘手，这导致过度的重建误差和次优的压缩性能。
方法：这里，提出了一种特征增强的非对称编解码网络。脑电图是用轻量级模型编码的，然后通过深度提取编码特征并通过两分支结构重建信号，用多级特征融合网络进行解码。
结果：在公共EEG数据集上，运动图像和事件相关电位，实验结果表明,该方法具有较好的压缩性能。此外,重建EEG信号的神经表示分析和分类性能也表明，随着压缩比的增加，我们的方法倾向于保留更多的任务相关信息，并在EEG压缩后保留可靠的判别信息。
结论：本文为可穿戴设备量身定制了一种非对称EEG压缩方法，该方法以轻量级的方式实现了最先进的压缩性能，为基于EEG的可穿戴设备的应用铺平了道路。
方法：这里，提出了一种特征增强的非对称编解码网络。脑电图是用轻量级模型编码的，然后通过深度提取编码特征并通过两分支结构重建信号，用多级特征融合网络进行解码。
结果：在公共EEG数据集上，运动图像和事件相关电位，实验结果表明,该方法具有较好的压缩性能。此外,重建EEG信号的神经表示分析和分类性能也表明，随着压缩比的增加，我们的方法倾向于保留更多的任务相关信息，并在EEG压缩后保留可靠的判别信息。
结论：本文为可穿戴设备量身定制了一种非对称EEG压缩方法，该方法以轻量级的方式实现了最先进的压缩性能，为基于EEG的可穿戴设备的应用铺平了道路。
