We are excited to present the newest addition to MNE-Scan: A plugin that utilizes the lab streaming layer (LSL) library in order to enable real-time streaming of EEG data. LSL is widely used in the field of electrophysiology and is supported by many EEG systems, either via vendor-provided software or dedicated programs supplied as part of the LSL distribution. The LSL library features extensive network support, thus enabling highly distributed […]

MNE Scan support for LSL

We are happy yo announce that the MNE-CPP project was granted research credits by the Amazon AWS Cloud Credits for Research program.

AWS Cloud Credits for Research

We are happy to announce the opening of new research positions to support the MNE-CPP team. One open postdoc position at the Athinoula A. Martinos Center for Biomedical Imaging which will be funded over a period of five years. Two full research positions which are set for three years. The positions are embedded in a collaborating research grant between the German Technische Universität Ilmenau (www.tu-ilmenau.de/bmti) and the Private University for […]

Join the team

We are grateful to announce that the German DFG and Austrian FWF will provide funding to support the development of “Online Neuronal Connectivity Estimation and Neurofeedback with Transcranial Magnetic Stimulation” in the frame of MNE-CPP. The new grant (397686322) will fund two full positions for a duration of 3 years, one located at the BMTI in Ilmenau and one at the UMIT in Hall in Tirol. You can find more […]

DFG and FWF: New Funding

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

We are excited to present the newest feature of MNE-CPP: Real-Time capable visualization of EEG/MEG sensor data. During a student project several new key features were implemented, which are described below. First a method to project sensors to the mesh, which is to be interpolated on, was implemented.  The projection is needed because sensor positions may be somewhat above the vertices of the used mesh. In order to interpolate signals, […]

Visualization of EEG/MEG Sensor Data

Elon Musk and Facebook recently presented their fascinating ideas to connect the computer directly with the brain. In this context, the MDR a central German broadcasting station featured MNE-CPP in their article about Brain Computer interfaces. Link to article: http://www.mdr.de/wissen/faszination-technik/computer-gehirn-anschluss-100.html  

MNE-CPP in the News

In the latest release (MNE-CPP v1.0.0-beta 3.0) we incorporated a technical preview of MNE Deep a deep learning library which facilitates Microsoft’s CNTK. CNTK is a framework which allows to analyze and reveal relations in massive datasets through deep learning by providing uncompromised scaling, speed and accuracy. The new MNE Deep library is used in the Brain Inside Out (BIO) project for modeling of brain structures with deep neuronal networks. MNE Deep also […]

Deep Learning with MNE-CPP

Our MNE Scan software can be used to acquire and process electrophysiological data in real-time. MNE Scan includes several sensor plugins, which can be used to connect to different devices. As of right now MNE Scan is able to connect to the following devices and thus provide an internal data stream for subsequent real-time processing: EEG devices EEGoSports (ANT Neuro) TMSI Refa gUSBAmo (g.Tec) BrainAmp (Brain Products) MEG Elekta Neuromag […]

MNE Scan: Improved Device Support

Magnetoencephalography (MEG) and Electroencephalography (EEG) provides high temporal resolution which allows a detailed investigation of time related neuronal activation properties. In real-time analysis two major challenges have to be handled: the low signal-to-noise ratio (SNR) and the limited time available for computations. We recently proposed Real-Time Clustered Multiple Signal Classification (RTC-MUSIC) as a real-time source localization algorithm, which can handle low SNRs and can cope with the high computational effort. It […]

RTC-MUSIC: Real-Time Source Localization

Together with our partners at the Boston Children’s Hospital (Harvad Medical School) we provided the software MNE Scan to acquire and process data from the novel BabyMEG system in real-time. The BabyMEG is a wohle head pediatric MEG system, which is able to measure kids up to three years of age. The BabyMEG is equipped with 270 inner layer magnetometers and 35×3-axis outer layer sensors. By using a recycling system, […]

BabyMEG: Real-Time Data Processing

In the past months the MNE-CPP team has been improving the disp3D library and adding new features for better data visualization. The recent release of Qt 5.7 brought much anticipated updates and new modules into the field. QtCharts module was implemented for data analysis in the form of distribution histogram. This will show the distribution of EEG and MEG data. In addition, users will now be able to effortlessly hide […]

New 3D Visualization Features

We are happy to announce that the BabyMEG was successfully cleared for clinical use 510(k) by the FDA. The BabyMEG uses MNE Scan as its acquisition tool.

BabyMEG Cleared for Clinical Use 510(k)

Next to providing a new fiff data browser (MNE Browse), we are also working on a newer version of MNE Analyze. The new MNE Analyze will provide a rich user interface with the new disp3D library as it core functionality. The following features are planned: Visualization of FreeSurfer data Visualization of source level activity, both in 3D and 2D (per vertex/active dipole) Convenient subject based data storing 2D topoplot for […]

MNE Analyze

During the last week the MNE-CPP team started to work on the new 3D library, called disp3D. The library is based on the new Qt3D module, which just has been released as a module in the current Qt version. The main benefit when using the Qt3D module is the fact that we do not have to work with low level OpenGL routines. This makes handling 3D stuff way easier, especially for none OpenGL experts. Also, […]

Real-Time 3D Visualization with MNE-CPP

Brain-Computer-Interfaces (BCIs) provide a novel way of communication by  interpreting different types of brain states. This principle of reading minds makes BCIs a challenging but at the same time fascinating topic among the different disciplines of electrophysiology and biomedical-signal-processing. Every BCI is dependent on a specific mental strategy. The mental strategy, or paradigm, can be seen as the BCI’s core translation algorithm, which defines specific brain activation induced by the […]

MNE Scan: Merging Brain and Computer with BCIs

MNE Browse Raw Qt Screenshot
Quite some time ago the MNE-CPP team launched the redevelopment of the well known MNE Browse Raw software by Matti Hämäläinen. Main motivation is to provide the fiff data browser for Windows system and thus reach a broader user base. Furthermore, we want to improve and add new functionalities to MNE Browse. See below for a screenshot of the current version. The new browser has been developed a lot during the […]

MNE Browse

We published our real-time acquisition, processing and analysis software in Biomedical Engineering: “MNE-X: MEG/EEG Real-Time Acquisition, Real-Time Processing, and Real-Time Source Localization Framework”

Publication on MNE Scan (former MNE-X)

Two years ago, in May 2010, the development of MNE-CPP was started. In the early version, MNE-CPP was in parts connected to closed source code. These respective code parts were in the meantime rewritten based on Qt. This allows us to now release MNE-CPP as free software to the open source community. We decided to choose the 3-Clause BSD License which imposes minimal restrictions on the redistribution of MNE-CPP. The development of MNE-CPP will […]

MNE-CPP Goes Open Source

RAP-MUSIC Algorithm GPU (CUDA 4.0) implementation is finished. To realize RAP-MUSIC real-time source localization we reduce in a first step the computational costs by modifying and pre-calculating components of the subspace correlation. In a second step we apply to the accelerated algorithm a modified Powell’s Conjugate Gradient Method, which highly optimizes the search process. Since the subspace correlations of the RAP-MUSIC algorithm are independent, they are predestined to be computed […]

RAP-MUSIC GPU Implementation