Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

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.

Automated Filtering of Common-Mode Artifacts in Multichannel Physiological Recordings

“…Signals were generated at 1200 Hz using the Craniux software suite [18]. S in (1) consisted of pink noise with a 1/ f power falloff to simulate a baseline ECoG recording [19].…”

Stable online control of an electrocorticographic brain-computer interface using a static decoder

“…The signals were filtered and processed using the Craniux Brain Computer Interface system [16]. Spectral estimation was performed using the Burg AR method [17] over the 40 to 180 Hz range (25th order, 10 Hz band width).…”

“…Estimates were calculated every 33 ms using a sliding window of 300 ms of raw data. AR data were logtransformed, then normalized to pseudo Z-scores relative to a baseline condition [16]. These spectral estimates for each frequency band were used as the neural features for BCI control.…”

Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies

“…First, signal conditioning is typically applied to neural signals, such as band-pass filtering, removal of line noise and artifacts (see section 4.1 above), and spatial filtering/beamforming (see section 4.3 above). Second, signal processing algorithms are used for real-time feature extraction, most commonly time-frequency analysis in various frequency bands (see section 4.2 above) (Degenhart et al 2011;Schalk et al 2004). Finally, decoding algorithms transform neural signal features into control signals for feedback, such as moving a computer cursor, a robotic/prosthetic arm, or other devices.…”

“…"BCI2000" is a successful example of real-time software that was developed for BMI (Schalk et al 2004) and adapted to work with rtMEG toolboxes for Elekta Neuromag and CTF systems (Mellinger et al 2007;Sudre et al 2011). Another example is "Craniux" which is written in LabVIEW engineering programming environment (National Instruments, TX, USA) (Degenhart et al 2011). The Craniux software package takes advantage of the advanced signal processing, real-time data visualization, and distributed processing capabilities offered by the LabVIEW environment.…”