The HF-1 system is an open source hardware and software system for acquisition and real-time processing of electrophysiology during fMRI. The open source aspect of this system allows end-users to develop and customize all aspects of hardware and software to suite their specific application. The open source nature of the HF-1 hardware is particularly important for MRI-related applications, as it provides researchers with a data acquisition hardware template that meets the stringent and costly requirements of MRI-safety and compatibility as well as electromagnetic compliance. The integrated software subsystems are user-customizable at all levels of organization, from low-level MCU firmware to high-level data acquisition and analysis software. The HF-1 open source software allows end users to rapidly develop customized real-time acquisition and processing applications. Within neuroscience and clinical medicine there is a growing need for such real-time acquisition and processing solutions, for applications including epilepsy, neurofeedback, sleep studies, and electrophysiological drug studies.
Hardware for simultaneous EEG and fMRI have been developed independently in numerous laboratories (Allen et al., 1998
; Garreffa et al., 2004
; Goldman et al., 2000
; Mirsattari et al., 2005
), and are also available commercially from a number of manufacturers such as BrainProducts (Munich, Germany) or Neuroscan-Compumedics (El Paso, Texas). Hardware for electrophysiological recordings during fMRI in animals has also been developed (Logothetis et al., 2001
). However, none of these systems provide a complete open-source solution integrating MRI-compatible hardware with user-customizable real-time software. The OpenEEG Project, http://openeeg.sourceforge.net/doc/
, is an open source EEG hardware system that provides an avenue for low-cost EEG acquisition. The OpenEEG system lacks the MRI-specific design features, the specifications and files for automated hardware assembly, and the degree of software integration found in HF-1. Stand-alone systems and software for real-time processing of EEG/fMRI have also been reported in the literature (Garreffa et al., 2003
; Mirsattari et al., 2005
). Compared with other data acquisition and real-time processing software, the HF-1 software has the advantage that it has been developed within the user-friendly LabView graphical programming environment, facilitating rapid development of real-time signal processing modules, as well as data acquisition and display modules that can be completely customized to the end-user’s specific needs.
A basic use for real-time signal processing is to assess whether or not an adequate amount of data have been recorded during a particular experiment. For simultaneous ERP/fMRI studies, real-time ERP averaging can be used to examine ERP signal quality to ensure that enough ERP epochs have been recorded. Sources of poor signal quality include subject level of arousal, subject movement, and electrode impedance, for instance, all of which can be difficult to detect during a study. Examining ERP results in real-time can help investigators detect and debug problems immediately, resulting in a tremendous savings of time and effort, not to mention peace of mind. For sleep, anesthesia, or resting-state EEG/fMRI studies, which often examine the spectral content of EEG signals as a variable of interest, real-time analysis of frequency spectra could be useful in monitoring the study subject’s state. In this paper, we have illustrated both real-time ERP averaging and frequency-domain analyses with real-time 40-Hz ASSR calculations.
Beyond ERP averaging and spectral analysis, one can imagine many new and important applications for real-time processing of electrophysiology during fMRI. For epilepsy studies, a real-time spike detection module could be used to determine whether a sufficient number of interictal spikes have been observed during an experiment. Similarly, a real-time sleep scoring module could be used to ensure that adequate amounts of data are being recorded during each desired stage of sleep. Numerous automatic sleep scoring systems have been proposed (Analog, 2004
; Anderer et al., 2005
; Atamer et al., 2002
; Callini et al., 2000
; Chang et al., 1988
; Heiss et al., 2002
; Holzmann et al., 1999
; Korpinen et al., 1994
; Penzel and Conradt, 2000
; Principe et al., 1989
; Principe and Smith, 1985
; Ray et al., 1986
; Schwaibold et al., 2002
) and many are commercially available, but have yet to be developed for simultaneous electrophysiology during fMRI. EEG-based neurofeedback during fMRI is another application for real-time processing. Neurofeedback is a technique where study subjects are taught how to modulate, through their own will, neural signals derived from EEG that are presented to the subject in real-time either visually or aurally (e.g., screen showing cursor position related to EEG signal features). This technique is becoming a widely used tool in neuroscience and clinical research, and by its very nature requires real-time signal processing (Basmajian, 1981
; Delorme and Makeig, 2003
; Fox, 1979
; Herrmann et al., 2004
; Hill and Raab, 2005
; Riddle and Baker, 2005
; Sinkjaer et al., 2003
). Real-time processing of electrophysiological signals for neurofeedback during fMRI would provide the means to correlate neurofeedback with simultaneously observed fMRI and identify neural systems involved in neurofeedback. Numerous drugs with sites of action in the central nervous system produce dose-dependent changes in EEG, including general anesthetics (Rampil, 1998
), sedative hypnotics (Veselis et al., 1991
), opioid analgesics (Scott et al., 1985
), and antidepressants (Dumont et al., 2005
). With real-time EEG processing during fMRI, drug levels could be titrated to specific EEG-based endpoints, to allow a systematic investigation of brain function at specific drug-induced electrophysiological states. Outside the MRI environment, HF-1’s flexible open source real-time processing and data acquisition could provide a means to implement variable-rate sampling or compression of electrophysiological data in the neurological intensive care unit (NeuroICU). In the NeuroICU, multi-day recordings can generate terabytes of data at the high sampling rates required to observe rare but important high-frequency bursting activity. With variable rate sampling or compression, temporal resolution could be changed in real-time as dictated by the data. The HF-1 open source system has been designed to provide a flexible starting point to rapidly develop real-time processing solutions for a wide range of studies such as these.
Open source dissemination of hardware and software developed from public funding helps fulfill the mandate of publicly funded medical research by making tools freely available to the research community and by providing building blocks for future collaborative development. Open source dissemination of software has enjoyed a long and fruitful history, and such developments within the neuroscience community have made a tremendous impact. Open source dissemination of hardware has been slower to develop, largely because of the relatively greater difficulty associated with constructing hardware devices compared to installing or modifying software code. However, resources for rapid construction of electronic devices have become readily available. For instance, companies offering services to build print circuit boards, place and solder components, and test assembled devices can be contracted for a modest fee in most locales or via the internet. The HF-1 system takes advantage of these resources by providing detailed construction files that allow the system to be constructed in a largely automated fashion using such services. In the near term, the increasing availability of such resources will make open source hardware developments such as HF-1 accessible to many research laboratories. In the longer term, the ongoing development of self-contained electronic fabrication laboratories (e.g., “FAB LAB,” http://fab.cba.mit.edu/
, (Mikhak et al., 2002
)) will allow researchers with even very modest resources to take advantage of open source hardware.