Florida research open-source synchronization tool (FROST) for electrophysiology experiments

https://doi.org/10.1016/j.jneumeth.2020.108800Get rights and content

Highlights

  • Neuroscientific electrophysiology experiments often require alignment of many sources of data.

  • FROST is a device that provides an open-source, Arduino-based solution to this common problem.

  • This new tool is designed to be compatible with numerous experimental research devices.

  • We provide two examples of data acquired using FROST.

  • FROST is easily customizable and scalable for specific research needs.

Abstract

Background

Accurate interpretation of electrophysiological data in cognitive and behavioral experiments requires the acquisition of time labels, such as marking the exact start of a condition or moment a stimulus is presented to a research subject.

New Method

Here we present an inexpensive (∼30 USD) device used as a central relay for multiple peripheral devices, such as a computer screen presenting an experiment, a pressure-sensor push button, a multi-button responder, a pulse oximeter sensor, a light-emitting diode trigger for camera synchronization, and more. We refer to this device as the Florida Research Open-source Synchronization Tool (FROST). FROST allows for easy hardware and Arduino-based firmware modifications that enable a standard platform for the integration of novel peripheral sensors.

Results

With two examples, we demonstrate the application of this device during human research experiments: intracranial-electroencephalography (EEG) recordings in a patient with epilepsy and surface-EEG recordings in a healthy participant. We provide an example setup for a rodent experiment as well. We also demonstrate the timing delays of our device.

Comparison with existing methods

There is currently very few existing open-source synchronization tools for electrophysiological research that enable customization with new device compatibility. We developed this tool to enable widespread replication for many applications through an open-source platform.

Conclusions

FROST can be easily adapted for research experiments beyond the included example cases. All materials are open-source at github.com/Brain-Mapping-Lab/FROST.

Introduction

Animal or human electrophysiology research experiments during cognitive-behavioral paradigms typically involve a primary data source and any number of secondary measurement instruments. These inputs are sampled at a specified rate for digital storage. For instance, a neuroscientific experiment in a rodent might include voltage recordings from an invasive electrode in the brain or muscle and a camera for video capture (Masimore et al., 2005; Tort et al., 2008). As another example, a human electroencephalography (EEG) study might involve a research subject wearing an EEG cap while responding to a computer task using a push-button or while walking in a virtual reality environment via a joystick (Peterson et al., 2018; Savostyanov et al., 2009; Vourvopoulos et al., 2019; Wirth et al., 2017). Time-aligning multiple sources of input —electrophysiology, video camera, additional peripheral devices, and so on — is necessary but not trivial.

A computer can store the states of an ongoing task, but the delay in that computational process may lead to inaccuracies relative to the real-time sequence of events (e.g., computational delays, refresh rate of the monitor, etc.). A light sensor placed on the computer monitor has been a common solution to this problem and should be a capability integrated into a synchronization device for research involving computerized tasks. Overall, the multitude of devices used during experiments are typically manufactured by different companies and may lack a shared communication protocol or a centralized repository that receives all input sources simultaneously. This can pose a major barrier to research and there is a need for a comprehensive solution to this problem.

A much-needed resource for these multi-source experiments is a tool to enable a single cohesive dataset containing all input sources aligned in time. Electrical biosignal acquisition systems (e.g., g.HIamp amplifier, Guger Technologies, Linz, Austria) can simultaneously record from multiple sources of data but lack compatibility for diverse input sources and connectors. Existing platforms designed to work with these acquisition systems do exist and aid in aligning data, such as BCI2000 (Schalk et al., 2004) and Neuro Omega™(Alpha Omega Engineering, Nof HaGalil, Israel). Therefore, it seems most feasible to situate a synchronization hardware solution between the input sources and the acquisition system. If the primary measure of interest is electrical, such as a voltage recording, then with adequate data storage capabilities it is advantageous for all input sources to be aligned and recorded at the same sampling rate. This would avoid the need for techniques such as data interpolation, upsampling, or downsampling.

Critically, the solution should be flexible to accommodate the breadth of electrophysiological research. Suppose a human behavioral experiment requires a pressure-based push button. In this case, several questions arise, namely, how the pressure sensor’s analog input should be digitized and whether a certain pressure threshold should trigger an on-off button press. As another example, suppose a pulse oximeter digital signal is also needed and requires different threshold calibration from person to person. A solution to this acquisition/synchronization problem should entail the ability to handle these use cases in a user-friendly manner. The hardware should have input options for a variety of connector types and the firmware should be readily configurable through a simple do-it-yourself (DIY) platform. Finally, we believe the entire system – both the hardware and firmware – should be open-sourced.

Therefore, we set out to build the Florida Research Open-source Synchronization Tool (FROST) with circuit schematics freely available in standard formats for custom modification along with Arduino-based source code. Here we provide details regarding FROST’s hardware and firmware, highlighting areas where users may want to extend and adapt the capabilities of the presented platform. We include two real-world human research applications of this device demonstrating its capabilities as well as an example rodent experiment setup. All hardware and source code are freely available through github.com/Brain-Mapping-Lab/FROST. The circuit schematic and layout are available as Eagle files, all code is available as Arduino files, and further documentation is provided in a Wiki format.

Section snippets

Design overview

FROST transmits data to the electrical biosignal acquisition system in digital form, mainly because digital signals offer better isolation and reduce the possibility of injecting noise into the acquisition system. Our design includes the capability of reading from both analog and digital sensors. The proposed device may also send synchronization signals to other systems such as an electromyographic (EMG) acquisition system or to a video camera through an LED placed at the top corner of its

Timing accuracy

Measured delays in FROST were 276.59 +/− 35.736 (mean +/− standard deviation) microseconds (μs). The range was 125.3 μs to 444.7 μs. The code processes all inputs during each iteration and this timing accuracy is not dependent on the number of analog inputs. Thus, these delays represent efficient code and are within normal limits for the frequency of the oscillator of the microcontroller (16  MHz).

Experiment 1

Representative output from Experiment 1 (Section 2.5.1) is shown in Fig. 6. Due to the utilization

Discussion

In this article we have described a new tool for synchronization across multiple inputs for electrophysiological research. The primary source code and hardware elements have been included, but further details, such as the full circuit schematic, can be found online at github.com/Brain-Mapping-Lab/FROST. The most notable strengths of this device are that by design it is general purpose, customizable, and open-sourced. We have demonstrated that the device works as intended through specific timing

Declaration of Competing Interest

There are no current commercial interests, however the authors and the University of Florida (Research Foundation) may have a financial interest in the use of this technology, some aspect of which may be commercialized in the future. There are no other competing interests.

Funding sources

NIH UH3 NS09553 (Gunduz), NIH R01 NS096008 (Gunduz), NSF PECASE 1553482 (Gunduz), NIH F30 NS111841-01 (Eisinger), NIH TL1TR001428 (Eisinger), Fulbright Scholarship (Gomez).

CRediT authorship contribution statement

Jose D. Alcantara: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Robert S. Eisinger: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Enrico Opri:

Acknowledgements

We are thankful to our funding sources and to everyone who provided feedback on this device and manuscript, including the entire Brain Mapping Laboratory, the Fixel Institute for Neurological Diseases, and the Wilder Center for Epilepsy Research at the University of Florida. We are also thankful for the two research participants that contributed data to this manuscript.

References (17)

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