ArticleNeuroinformatics2021
RT-NET: real-time reconstruction of neural activity using high-density electroencephalography.
Article in Neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- EEG hyperscanning reveals dynamic interbrain network patterns during interactive social decision-making.Communications biology · 2026Article
- Current implications of EEG and fNIRS as functional neuroimaging techniques for motor recovery after stroke.Medical review (2021) · 2024Review
- Article
- Online functional connectivity analysis of large all-to-all networks in MNE Scan.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Source-Based EEG Neurofeedback for Sustained Motor Imagery of a Single Leg.Sensors (Basel, Switzerland) · 2023Article
- Coefficient-of-variation-based channel selection with a new testing framework for MI-based BCI.Cognitive neurodynamics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-density electroencephalography (hdEEG) has been successfully used for large-scale investigations of neural activity in the healthy and diseased human brain. Because of their high computational demand, analyses of source-projected hdEEG data are typically performed offline. Here, we present a real-time noninvasive electrophysiology toolbox, RT-NET, which has been specifically developed for online reconstruction of neural activity using hdEEG. RT-NET relies on the Lab Streaming Layer for acquiring raw data from a large number of EEG amplifiers and for streaming the processed data to external applications. RT-NET estimates a spatial filter for artifact removal and source activity reconstruction using a calibration dataset. This spatial filter is then applied to the hdEEG data as they are acquired, thereby ensuring low latencies and computation times. Overall, our analyses show that RT-NET can estimate real-time neural activity with performance comparable to offline analysis methods. It may therefore enable the development of novel brain-computer interface applications such as source-based neurofeedback.
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Registered trials
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