Craniux: a LabVIEW-based Modular Software Framework for Brain-machine Interface Research

Overview

Journal Comput Intell Neurosci

Specialty Biology

Date 2011 Jun 21

PMID 21687575

Citations 7

Authors

Alan D Degenhart

John W Kelly

Robin C Ashmore

Jennifer L Collinger

Elizabeth C Tyler-Kabara

Douglas J Weber

Wei Wang

Affiliations

Soon will be listed here.

Abstract

This paper presents "Craniux," an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.

Citing Articles

Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.

Edelman B, Zhang S, Schalk G, Brunner P, Muller-Putz G, Guan C IEEE Rev Biomed Eng. 2024; 18:26-49.

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Analyzing Passive BCI Signals to Control Adaptive Automation Devices.

Al-Hudhud G, Alqahtani L, Albaity H, AlSaeed D, Al-Turaiki I Sensors (Basel). 2019; 19(14).

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Remapping cortical modulation for electrocorticographic brain-computer interfaces: a somatotopy-based approach in individuals with upper-limb paralysis.

Degenhart A, Hiremath S, Yang Y, Foldes S, Collinger J, Boninger M J Neural Eng. 2017; 15(2):026021.

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Histological evaluation of a chronically-implanted electrocorticographic electrode grid in a non-human primate.

Degenhart A, Eles J, Dum R, Mischel J, Smalianchuk I, Endler B J Neural Eng. 2016; 13(4):046019.

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MEG-based neurofeedback for hand rehabilitation.

Foldes S, Weber D, Collinger J J Neuroeng Rehabil. 2015; 12:85.

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