  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.
