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Research Papers
OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis
Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.
View Full Paper →An open-source hardware and software system for acquisition and real-time processing of electrophysiology during high field MRI
Simultaneous recording of electrophysiology and functional magnetic resonance imaging (fMRI) is a technique of growing importance in neuroscience. Rapidly evolving clinical and scientific requirements have created a need for hardware and software that can be customized for specific applications. Hardware may require customization to enable a variety of recording types (e.g., electroencephalogram, local field potentials, or multi-unit activity) while meeting the stringent and costly requirements of MRI safety and compatibility. Real-time signal processing tools are an enabling technology for studies of learning, attention, sleep, epilepsy, neurofeedback, and neuropharmacology, yet real-time signal processing tools are difficult to develop. We describe an open-source system for simultaneous electrophysiology and fMRI featuring low-noise (<0.6microV p-p input noise), electromagnetic compatibility for MRI (tested up to 7T), and user-programmable real-time signal processing. The hardware distribution provides the complete specifications required to build an MRI-compatible electrophysiological data acquisition system, including circuit schematics, print circuit board (PCB) layouts, Gerber files for PCB fabrication and robotic assembly, a bill of materials with part numbers, data sheets, and vendor information, and test procedures. The software facilitates rapid implementation of real-time signal processing algorithms. This system has been used in human EEG/fMRI studies at 3 and 7T examining the auditory system, visual system, sleep physiology, and anesthesia, as well as in intracranial electrophysiological studies of the non-human primate visual system during 3T fMRI, and in human hyperbaric physiology studies at depths of up to 300 feet below sea level.
View Full Paper →Standards for the Use of Quantitative Electroencephalography (QEEG) in Neurofeedback: A Position Paper of the International Society for Neuronal Regulation
Background. This paper presents the findings of an interdisciplinary committee on standards for quantitative electroencephalography (QEEG) in neurofeedback which has been unanimously accepted by the International Society for Neuronal Regulation (ISNR) Board as a position paper of ISNR. Method. The committee reviewed current standards for quantitative encephalography in other specialties as well as scholarly literature on QEEG. Results. The panel reached the following conclusions: Although clinical research indicates that a full 19 channel QEEG does not appear necessary for conducting successful neurofeedback training, an increasing number of clinicians are using comprehensive QEEG evaluations to guide their neurofeedback training. An impressive body of peer reviewed scientific literature attests to the utility of the QEEG in providing a scientifically objective and clinically practical assessment of a wide range of psychiatric, psychological and medical conditions. Many of the significant contributions to the field of QEEG have come from psychologists and the Board of Professional Affairs of the American Psychological Association has concluded that QEEG is within the scope of practice of psychologists trained in this specialty. Unlike neurology and psychiatry, where QEEG is principally used for purposes of diagnosing medical pathology, neurotherapists who use QEEG primarily do so to guide EEG biofeedback training. It is not necessary for a physician to screen raw EEG data as part of a QEEG evaluation for neurofeedback training. Conclusions. For the purpose of encouraging high standards, recommendations are made for areas of training and study in this specialty, for certification, for equipment/software, and for procedures in data collection and analysis.
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