Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data

被引:0
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作者
Taberna G.A. [1 ]
Samogin J. [1 ]
Zhao M. [1 ,2 ]
Marino M. [1 ,3 ]
Guarnieri R. [1 ]
Cuartas Morales E. [1 ,4 ]
Ganzetti M. [1 ,5 ]
Liu Q. [1 ,6 ]
Mantini D. [1 ,7 ]
机构
[1] Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven
[2] Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou
[3] Department of General Psychology, University of Padova, Padova
[4] Dirección Académica, Universidad Nacional de Colombia, Sede de La Paz, La Paz
[5] Roche Pharma Research and Early Development (pRED), pRED Data & Analytics, Roche Innovation Center Basel, F. Hoffmann–La Roche Ltd, Basel
[6] Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen
[7] KU Leuven Brain Institute, KU Leuven, Leuven
关键词
Brain activity; Brain connectivity; EEG; Electrophysiological monitoring; GUI; Head modelling; Signal processing; Source localization;
D O I
10.1016/j.compbiomed.2024.108704
中图分类号
学科分类号
摘要
Introduction: High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets. Findings: Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software. Conclusions: NET is freely available (https://github.com/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows. © 2024
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