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RT-NET: real-time reconstruction of neural activity using high-density electroencephalography
Roberto Guarnieri1, Mingqi Zhao1, Gaia Amaranta Taberna1
1Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium.
Neuroinformatics
|July 29, 2020
Summary
We developed RT-NET, a real-time toolbox for high-density electroencephalography (hdEEG) analysis. This system enables online neural activity reconstruction, matching offline performance for advanced brain-computer interfaces.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- High-density electroencephalography (hdEEG) is crucial for studying brain activity.
- Offline analysis of hdEEG data is computationally intensive, limiting real-time applications.
- There is a need for efficient online processing of hdEEG signals.
Purpose of the Study:
- To introduce RT-NET, a novel real-time electrophysiology toolbox.
- To enable online reconstruction of neural activity from hdEEG data.
- To facilitate low-latency hdEEG analysis for novel applications.
Main Methods:
- RT-NET utilizes the Lab Streaming Layer for data acquisition and streaming.
- A spatial filter is estimated for artifact removal and source activity reconstruction.
- The spatial filter is applied in real-time to acquired hdEEG data.
Main Results:
- RT-NET achieves real-time neural activity estimation.
- The system demonstrates performance comparable to offline analysis methods.
- Low latencies and computation times are maintained during online processing.
Conclusions:
- RT-NET provides a viable solution for real-time hdEEG analysis.
- The toolbox supports the development of advanced brain-computer interfaces.
- Source-based neurofeedback applications are enabled by this technology.
