Phase synchrony information plays a crucial role in analyzing functional brain connectivity and identifying brain activities. A widely adopted feature extraction pipeline, composed of preprocessing, selection of EEG acquisition channels, and phase locking value (PLV) calculation, has achieved success in motor imagery classification (MI). However, this pipeline is manual and reliant on expert knowledge, limiting its convenience and adaptability to different application scenarios. Moreover, most studies have employed mediocre data-independent spatial filters to suppress noise, impeding the exploration of more significant phase synchronization phenomena. To address the issues, we propose the concept of phase synchrony component self-organization, which enables the adaptive learning of data-dependent spatial filters for automating both the preprocessing and channel selection procedures. Based on this concept, the first deep learning end-to-end network is developed, which directly extracts phase synchrony-based features from raw EEG signals and perform classification. The network learns optimal filters during training, which are obtained when the network achieves peak classification results. Extensive experiments have demonstrated that our network outperforms state-of-the-art methods. Remarkably, through the learned optimal filters, significant phase synchronization phenomena can be observed. Specifically, by calculating the PLV between a pair of signals extracted from each sample using two of the learned spatial filters, we have obtained an average PLV exceeding 0.87 across all tongue MI samples. This high PLV indicates a groundbreaking discovery in the synchrony pattern of tongue MI.

翻译：相位同步信息在分析大脑功能连接性和识别脑活动方面具有关键作用。由预处理、脑电图采集通道选择和相位锁定值（PLV）计算组成的广泛采用的特征提取流程，已在运动想象分类中取得成功。然而，该流程依赖人工操作和专家知识，限制了其便捷性和对不同应用场景的适应性。此外，大多数研究采用性能平庸的数据无关空间滤波器来抑制噪声，阻碍了对更显著相位同步现象的探索。为解决这些问题，我们提出相位同步分量自组织的概念，能够自适应学习数据相关的空间滤波器，实现预处理和通道选择流程的自动化。基于该概念，我们开发了首个深度学习端到端网络，可直接从原始脑电图信号中提取基于相位同步的特征并进行分类。该网络在训练过程中学习最优滤波器，当网络达到分类峰值时获得这些滤波器。大量实验表明，我们的网络优于最先进的方法。值得注意的是，通过学习得到的最优滤波器，可以观察到显著的相位同步现象。具体而言，通过使用两个学习到的空间滤波器计算每个样本中提取的信号对之间的PLV，我们在所有舌头运动想象样本上获得了超过0.87的平均PLV。这一高PLV值揭示了舌头运动想象同步模式中的突破性发现。