Dynamic Graph Network for EEG Signal Connectivity
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Solution Overview
Problem
Existing brain-computer interface (BCI) methods based on static graph neural networks fail to fully capture the time-varying connectivity between EEG signal channels, limiting their performance in adapting to changes in brain networks between individuals or under different conditions.
Innovation Solution
A brain-computer target reading method using a dynamic graph representation network, which includes a dynamic temporal graph constructing module, a dual-branch graph pooling module, and a dynamic temporal attention module to capture time-varying connectivity and extract task-related features from EEG signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static graph neural network is used to model connectivity between EEG channels, then the model structure is simple and easy to implement, but the model fails to capture time-varying connectivity dynamics and adapts poorly to changes in brain networks between individuals or conditions
Solution Approach 1:
The patent applies the dynamics principle by transitioning from a static graph neural network to a dynamic graph neural network that can adapt to time-varying connectivity patterns. The model dynamically adjusts graph structures based on temporal dependencies in EEG signals, enabling it to capture changing brain network configurations between individuals and conditions without requiring manual reconfiguration.
2Loss of information
If convolutional neural networks or cyclic neural networks are used to extract features from EEG data, then the model structure is relatively simple, but the models ignore connectivity relationships between different channels and limit feature extraction to the Euclidean domain
Solution Approach 1:
The patent applies the intermediary principle by introducing a graph neural network as an intermediate layer between raw EEG data and final feature extraction. This intermediary graph structure explicitly models connectivity relationships between EEG channels through adjacency matrices, preserving crucial spatial-temporal dependencies that conventional CNNs and cyclic networks fail to capture.
Solution Approach 2:
The patent applies the dimensionality change principle by extending feature extraction from the traditional Euclidean domain to a non-Euclidean graph domain. The graph neural network operates on graph-structured data with nodes representing EEG channels and edges representing connectivity relationships, enabling feature extraction in a higher-dimensional manifold that captures complex temporal-spatial patterns.
3Measurement precision
If existing graph neural network methods construct adjacency matrices based on physical distance or signal correlation, then the model can model complex interactions between brain areas, but the models fail to fully consider temporal dynamics of connectivity and insufficiently adapt to changes in brain network
Solution Approach 1:
The patent applies the dynamics principle by constructing time-varying adjacency matrices that capture temporal dependencies in EEG connectivity. The model dynamically updates graph structures based on temporal patterns, enabling precise measurement of connectivity at different time points while adapting to temporal dynamics of brain network changes between individuals and conditions.
Data Source
AI summary
A brain-computer target reading method based on a dynamic graph representation network and a system thereof are provided. The system includes a dynamic temporal graph constructing module, a dual-branch graph pooling module and a dynamic temporal attention module. The dynamic temporal graph constructing module captures a time-varying connectivity relationship between Electroencephalography (EEG) signal channels. The dual-branch pooling module retains local structure information and global structure information in the process of purifying features, which reduces the loss of effective information. Finally, the dynamic temporal attention module allows a model to pay more attention to task-related representations, thus improving the overall classification performance of the model. Compared with the existing event-related potential identification method, the result of the brain-computer target reading method is better. The brain-computer target reading method overcomes the limitation of static graph network in terms of dynamically capturing the time-varying connectivity between the EEG signal channels.