Monthly Archives: September 2017

We are grateful to announce that the MNE-CPP project accomplished to achieve two Microsoft’s Azure cloud computing for research awards. MNE-CPP was accepted to be sponsored throughout their research programs with a total of 30.000$. These funds are part of cloud computing resources which will be used to train and investigate new deep neural networks in conjunction with neuroscientific hypothesis facilitating the MNE Deep library.

Microsoft Azure for Research Award

Under the auspices of their recently funded grant (1U01EB023820) from NIBIB/NIH: “Device-Independent Acquisition and Real Time Spatiotemporal Analysis of Clinical Electrophysiology Data”, Matti Hämäläinen (contact PI), Yoshio Okada (PI) and John Mosher (PI) are developing a device-independent data acquisition & processing software (MNE-CE). The basis for this software is provided by MNE-CPP with MNE Scan. This Bioengineering Research Partnerships (BRP) grant has a duration of five years. Throughout this time the […]

MNE CE funded by NIH

  With real-time clustered multiple signal classification (RTC-MUSIC) we present a real-time source localization algorithm, which can handle low SNRs and can reduce the computational effort. It provides correlation information together with sparse source estimation results, which can, e.g., be used to identify evoked responses with high sensitivity. RTC-MUSIC clusters the forward solution based on an anatomical brain atlas and optimizes the scanning process inherent to MUSIC approaches. Publisher’s Version

Publication on RTC-MUSIC