RT-NET: real-time reconstruction of neural activity using high-density electroencephalography.
Neuroinformatics
; 19(2): 251-266, 2021 04.
Article
em En
| MEDLINE
| ID: mdl-32720212
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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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Sistemas Computacionais
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Encéfalo
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Mapeamento Encefálico
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Eletroencefalografia
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Interfaces Cérebro-Computador
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article