Phase Synchrony Component Self-Organization in Brain Computer Interface
This work addresses the inconvenience and limited adaptability of manual feature extraction in brain-computer interfaces, offering an automated solution with strong performance gains.
The paper tackles the manual, expert-dependent pipeline for extracting phase synchrony features in motor imagery brain-computer interfaces by proposing a deep learning end-to-end network that learns data-dependent spatial filters automatically. The network outperforms state-of-the-art methods and achieves an average phase locking value exceeding 0.87 for tongue motor imagery samples, revealing a groundbreaking synchrony pattern.
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.