Phase Synchrony Component Self-Organization in Brain Computer Interface

TL;DR

This paper introduces an adaptive deep learning approach for EEG analysis that automatically learns optimal spatial filters for phase synchrony, improving motor imagery classification and revealing significant synchrony patterns.

Contribution

It proposes the first end-to-end deep learning network that self-organizes phase synchrony components directly from raw EEG data, automating preprocessing and channel selection.

Findings

Outperforms state-of-the-art methods in MI classification.

Learns optimal filters that reveal significant phase synchronization phenomena.

Achieves an average PLV exceeding 0.87 in tongue MI samples.

Abstract

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.