Lorenz Esch1, Limin Sun2, Viktor Klüber3, Seok Lew4, Daniel Baumgarten5, P Ellen Grant6, Yoshio Okada7, Jens Haueisen8, Matti S Hämäläinen2, Christoph Dinh2. 1. Massachusetts 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. 2. Massachusetts 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. 3. Institute 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. 4. Massachusetts 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. 5. Institute of Electrical and Biomedical Engineering, UMIT - University of Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria. 6. Boston 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. 7. Boston Children's Hospital, Division of Newborn Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115 USA. 8. Institute 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.
Abstract
BACKGROUND: Magnetoencephalography (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 METHOD: We 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. RESULTS: We 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. CONCLUSION: We 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.
BACKGROUND: Magnetoencephalography (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 METHOD: We 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. RESULTS: We 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. CONCLUSION: We 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.
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