Learning Curve of a Short-Time Neurofeedback Training: Reflection of Brain Network Dynamics Based on Phase-Locking Value

Wang, Ze; Wong, Chi Man; Nan, Wenya; Tang, Qi; Rosa, Agostinho; Xu, Peng; Wan, Feng

doi:10.1109/tcds.2021.3125948

Cited by 1 publication

(1 citation statement)

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“…Additionally, they discovered significant differences in brain response levels, reaction times, and activation targets under different tasks. Wang et al (2022) used a phase-locked-value approach to construct functional brain networks, providing a better functional connectivity perspective for neurofeedback training. In the MI paradigm, directed causal connectivity provides insights into the causal interactions between nodes, making it more adept at uncovering hidden and overlooked connectivity compared to functional connectivity.…”

Section: Introductionmentioning

confidence: 99%

A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks

Ma,

Dong,

Lin

et al. 2024

Front. Neurosci.

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BackgroundThe development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals.ObjectiveThis paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task.MethodsThe signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL.ResultsFor Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm’s performance was found to be optimal.ConclusionThe feature fusion of DTF network and graph theory features enhanced CSP algorithm’s performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model’s sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.

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Section: Introductionmentioning

confidence: 99%