MNE Scan: Software for real-time processing of electrophysiological data - Esch, Lorenz; Sun, Limin; Klüber, Viktor; Lew, Seok; Baumgarten, Daniel; Grant, P. Ellen; Okada, Yoshio; Haueisen, Jens; Hämäläinen, Matti S; Dinh, Christoph (Journal of neuroscience methods, 2018)
•MNE Scan is a new software for acquiring and processing electrophysiological data in real-time.•This work is a first step in establishing a standardized real-time processing software targeting large parts of the neuroscience community.•The employed software development cycle considers the requirements needed for clinical software approval processes.•MNE Scan was tested in multiple real-time scenarios. It is in active use with a new pediatric MEG system, which was already approved by the FDA.•MNE Scan is developed under an open-source license and is freely available as source code or pre-built binaries. 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. 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. 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. 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. 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.