Investigating the application of graph theory features in hand movement directions decoding using EEG signals.

In recent years, functional analysis of brain networks based on graph theory properties has attracted considerable attention. This approach has usually been exploited for structural and functional brain analysis, while its potential in motor decoding tasks has remained unexplored. This study aimed to investigate the feasibility of using graph-based features in hand direction decoding in movement execution and preparation intervals. Hence, EEG signals were recorded from nine healthy subjects while performing a four-target center-out reaching task. The functional brain network was calculated based on the magnitude-squared coherence (MSC) at six frequency bands. Then, the features based on eight graph theory metrics were extracted from brain networks. The classification was performed with a support vector machine classifier. The results revealed that in four-class direction discrimination, the mean accuracy of the graph-based method surpassed 63% and 53% on movement and pre-movement data, respectively. Additionally, a feature fusion approach that combines the graph theory features with power features was proposed. The fusion method raised the classification accuracy to 70.8% and 61.2% for movement and pre-movement intervals, respectively. This work has verified the feasibility of using graph theory properties and their superiority over band power features in a hand movement decoding task. • Investigating the feasibility of using graph theory analysis in movement decoding. • Both movement and pre-movement intervals were investigated. • Graph theory metrics attain superior results compare to band power features. • The fusion approach of graph metrics with power features led to best results. [ABSTRACT FROM AUTHOR]