EEG Source Localization and Brain Network Analysis for Natural Movement Recognition
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Solution Overview
Problem
Conventional EEG recognition methods struggle to distinguish between natural movements, such as palmar, pinch, and twist, due to their complexity and the activation of similar brain motor areas, resulting in an unnatural and uncoordinated user experience in brain-computer interface (BCI) rehabilitation training.
Innovation Solution
A natural movement EEG recognition method based on source localization and brain networks, which involves multi-channel EEG measurement, preprocessing, source localization using L1 regularization and successive over-relaxation, and constructing brain networks with phase locking value calculations to differentiate between movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional EEG recognition methods are used, then the system is simple to operate, but the accuracy of distinguishing natural movements is poor
Solution Approach 1:
The patent segments the EEG analysis process into multiple stages: signal acquisition, artifact removal, source localization, brain network construction, and feature extraction. By dividing the complex recognition task into manageable segments, the system achieves high accuracy in distinguishing natural movements while maintaining operational feasibility through systematic processing
Solution Approach 2:
The patent transitions from analyzing EEG signals at the electrode level to the source level, adding a spatial dimension to the analysis. By localizing brain sources and constructing brain networks, the system moves from two-dimensional electrode space to three-dimensional brain space, enabling better distinction of natural movements through additional spatial information
2Measurement precision
If source localization and brain network analysis are implemented, then the decoding accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary source localization before brain network construction, pre-processing the EEG signals to identify active brain sources. This preliminary action simplifies subsequent network analysis by reducing the number of nodes to consider, thereby improving decoding accuracy while managing computational complexity through staged processing
Solution Approach 2:
The patent extracts movement-related cortical potentials (MRCP) and specific frequency band features from the complex EEG signals, isolating the most relevant information for movement recognition. By extracting only the essential features needed for decoding, the system achieves high accuracy while reducing unnecessary computational burden
Data Source
AI summary
Disclosed is a natural movement electroencephalogram (EEG) recognition method based on source localization and a brain network, which includes the following steps: (1) performing multi-channel EEG measurement for natural movements; (2) preprocessing acquired EEG signals, and extracting the movement-related cortical potential (MRCP), and θ, α, β, and γ rhythms; (3) determining a lead field matrix of the signals, calculating initial solutions of sources by means of L1 regularization constraint, and then performing iteration by means of successive over-relaxation to obtain a source localization result; (4) by using the sources as nodes, calculating PLV between each pair of sources at each time point by means of short-time sliding window, and establishing brain networks; and (5) calculating a network adjacency matrix at each time point and five brain network indicators, introducing these features into a classifier for training and testing, and conducting a statistical test for the brain network indicators. The present disclosure makes improvements to the conventional source localization method by using the T-wMNE algorithm in combination with successive over-relaxation, and establishes brain networks by using the sources as nodes, thus improving the EEG decoding accuracy for natural movements and revealing the neural mechanism of the human body.