Falcon: a highly flexible open-source software for closed-loop neuroscience
Software engineers Davide Ciliberti and Fabian Kloosterman meticulously report on their new open-source software, Falcon, in the paper “Falcon: a highly flexible open-source software for closed-loop neuroscience”, published in the Journal of Neural Engineering.
Closed-loop neuroscience experiments seek to rely on the outputs of brain activity to determine inputs of stimulation, such as that which comes from TMS (transcranial magnetic stimulation), or other neural stimulators. Falcon attempts to implement a software option for processing and controlling closed-loop data. Falcon can be used in conjunction with Neurolynx or Open Ephys, which collect the physical data from bursts of electricity, while Falcon processes said data and can control for changes in inputs and outputs. What separates Falcon from other current methods of applying closed-loop circuits is its reliance on software. Falcon is written on C++, a widely spoken computer language. Most current closed-loop systems currently rely on hardware, making quick changes to outputs and inputs difficult for experimenters unfamiliar with said hardware. Falcon also displays the inputs and outputs in graphical form, providing for easy viewing and manipulation of current activity. Specifically, each node of incoming data, likely stemming from an EEG node, is clearly visible in graphical form, and Falcon is capable of compiling data from multiple nodes into one data input.
This research tool was created by your colleagues. Please acknowledge the Principal Investigator, cite the article in which the tool was described, and include an RRID in the Materials and Methods of your future publications. RRID:SCR_023199
Special thanks to Lucas Steinbruegge, an undergraduate neuroscience major, for providing this project summary! This summary is part of a collection from students in a Computational Methods for Neuroscience Course at American University.
Read the publication!
Learn more about Falcon’s development and implementation from the 2017 Journal of Neural Engineering publication!
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