Evidence map›Paper›PMID 29621570›Full record

ArticleJournal of neuroscience methods2018

MNE Scan: Software for real-time processing of electrophysiological data.

Lorenz Esch, Limin Sun, Viktor Klüber, Seok Lew, Daniel Baumgarten, P Ellen Grant, Yoshio Okada, Jens Haueisen, Matti S Hämäläinen, Christoph Dinh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuroscience methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Lorenz EschMassachusetts General Hospital, Massachusetts Institute of Technology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St., Charlestown, MA 02129, USA; Boston Children's Hospital, Division of Newborn Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115 USA; Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Gustav-Kirchhoff- Str. 2, 98693 Ilmenau, Germany. Electronic address: lesch@mgh.harvard.edu.
Limin SunMassachusetts General Hospital, Massachusetts Institute of Technology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St., Charlestown, MA 02129, USA; Boston Children's Hospital, Division of Newborn Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115 USA.
Viktor KlüberInstitute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Gustav-Kirchhoff- Str. 2, 98693 Ilmenau, Germany; Institute of Nuclear and Energy Technologies, KIT - Karlsruher Institut für Technologie, 76344 Eggenstein-Leopoldshafen, Germany.
Seok LewMassachusetts General Hospital, Massachusetts Institute of Technology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St., Charlestown, MA 02129, USA; Department of Engineering, Olivet Nazarene University, 1 University Ave, Bourbonnais, 60914 IL, USA.
Daniel BaumgartenInstitute of Electrical and Biomedical Engineering, UMIT - University of Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria.
P Ellen GrantBoston Children's Hospital, Division of Newborn Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115 USA; Boston Children's Hospital, Division of Neuroradiology, Department of Radiology, Harvard Medical School, Boston, MA 02115 USA.
Yoshio OkadaBoston Children's Hospital, Division of Newborn Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115 USA.
Jens HaueisenInstitute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Gustav-Kirchhoff- Str. 2, 98693 Ilmenau, Germany; Biomagnetic Center, Clinic for Neurology, Jena University Hospital, Erlanger Allee 101, 07743 Jena, Germany.
Matti S HämäläinenMassachusetts General Hospital, Massachusetts Institute of Technology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St., Charlestown, MA 02129, USA; Boston Children's Hospital, Division of Newborn Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115 USA.
Christoph DinhMassachusetts General Hospital, Massachusetts Institute of Technology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St., Charlestown, MA 02129, USA; Boston Children's Hospital, Division of Newborn Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115 USA.

Funding

ULTRAHIGH FIELD TECHNOLOGY FOR FUNCTIONAL IMAGINGP41RR014075 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI HAMALAINEN, MATTI · 1999 to 2011
$16.4M
Project 4P41EB015896 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2012 to 2018
$9.8M
Device-Independent Acquisition and Real Time Spatiotemporal Analysis of Clinical Electrophysiology DataU01EB023820 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI MOSHER, JOHN COMPTON, OKADA, YOSHIO · 2017 to 2021
$5.8M
Tools for Large-Scale Platform-Independent MEG Data AnalysisR01EB009048 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI HAMALAINEN, MATTI, LEAHY, RICHARD M · 2009 to 2016
$4.6M
Elekta Neuromag Electronics UpgradeS10RR031599 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI HAMALAINEN, MATTI · 2011 to 2011
$350k
NCRR NIH HHS P41 RR014075NCRR NIH HHS S10 RR031599NIBIB NIH HHS P41 EB015896NIBIB NIH HHS R01 EB009048NIBIB NIH HHS U01 EB023820
6 · The paper itself

Abstract

backgroundMagnetoencephalography (MEG) and Electroencephalography (EEG) are noninvasive techniques to study the electrophysiological activity of the human brain. Thus, they are well suited for real-time monitoring and analysis of neuronal activity. Real-time MEG/EEG data processing allows adjustment of the stimuli to the subject's responses for optimizing the acquired information especially by providing dynamically changing displays to enable neurofeedback. NEW

methodWe introduce MNE Scan, an acquisition and real-time analysis software based on the multipurpose software library MNE-CPP. MNE Scan allows the development and application of acquisition and novel real-time processing methods in both research and clinical studies. The MNE Scan development follows a strict software engineering process to enable approvals required for clinical software.

resultsWe tested the performance of MNE Scan in several device-independent use cases, including, a clinical epilepsy study, real-time source estimation, and Brain Computer Interface (BCI) application. COMPARISON WITH EXISTING METHOD(S): Compared to existing tools we propose a modular software considering clinical software requirements expected by certification authorities. At the same time the software is extendable and freely accessible.

conclusionWe conclude that MNE Scan is the first step in creating a device-independent open-source software to facilitate the transition from basic neuroscience research to both applied sciences and clinical applications.

Indexed as

Brain-Computer InterfacesSignal Processing, Computer-AssistedSoftware DesignAdultChild, PreschoolElectroencephalographyHumansInfantInfant, NewbornMagnetoencephalographyNeurofeedbackNeurosciencesData acquisitionData processingMedical softwareMEG/EEGNeurofeedback/BCI

Identifiers

PMID29621570
PMCPMC5940556

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.