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.

高密度脑电图（hdEEG）已成功用于健康和患病人脑中神经活动的大规模研究。由于它们的高计算需求，因此通常离线进行源投影的hdEEG数据分析。在这里，我们介绍了一个实时无创电生理工具箱RT-NET，该工具箱是专门为使用hdEEG在线重建神经活动而开发的。RT-NET依靠实验室流传输层从大量EEG放大器获取原始数据，并将处理后的数据流传输到外部应用程序。RT-NET使用校准数据集估算空间过滤器，以去除伪影和重建源活动。然后，在获取hdEEG数据时将其应用到该空间滤波器，从而确保低延迟和计算时间。总体而言，我们的分析表明，RT-NET可以估计实时神经活动，其性能可与离线分析方法媲美。因此，它可能使新颖的脑机接口应用程序的开发成为可能，例如基于源的神经反馈。