Xpc real time operating system

Manufactured by MathWorks

Sourced in United States

The XPC real-time operating system is a product from MathWorks that provides a real-time execution environment for embedded systems development. It supports rapid prototyping, hardware-in-the-loop (HIL) simulation, and real-time testing of algorithms and control systems.

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Lab products found in correlation

Neuroport system, by Blackrock Microsystems (2 mentions) Patient cable, by Blackrock Microsystems (1 mentions) Simulink, by MathWorks (1 mentions) Matlab software, by MathWorks (1 mentions)

4 protocols using xpc real time operating system

Neural Signal Preprocessing for Intracortical Arrays

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In both participants, each intracortical microelectrode array was attached to a percutaneous pedestal connector on the head. A Blackrock shielded Patient Cable connected the pedestals to front-end amplifiers and a NeuroPort System (Blackrock Microsystems) that bandpass filtered (0.3 Hz to 7.5 kHz) and digitized (30 kHz) the neural signals from each channel on the microelectrode array. These digitized signals were preprocessed in Simulink using the xPC real-time operating system (The MathWorks Inc.). Each channel was bandpass (BP) filtered (250–5000 Hz), common average referenced (CAR), and down-sampled to 15 kHz in real time. CAR was implemented by selecting 60 channels from each microelectrode array that exhibited the lowest variance, and then averaging these channels together to yield an array-specific CAR. This reference signal was subtracted from the signals from all channels within each of the arrays.

Rastogi A., Willett F.R., Abreu J., Crowder D.C., Murphy B.A., Memberg W.D., Vargas-Irwin C.E., Miller J.P., Sweet J., Walter B.L., Rezaii P.G., Stavisky S.D., Hochberg L.R., Shenoy K.V., Henderson J.M., Kirsch R.F, & Ajiboye A.B. (2021). The Neural Representation of Force across Grasp Types in Motor Cortex of Humans with Tetraplegia. eNeuro, 8(1), ENEURO.0231-20.2020.

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Real-Time Neural Signal Processing for iBCI

Cited 2 times

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Neural signals were recorded from the microelectrode arrays using the NeuroPort™ system from Blackrock Microsystems (Hochberg et al. [25 (link)] describes the basic setup). Neural signals were analog filtered from 0.3 Hz to 7.5 kHz and subsequently digitized at 30 kHz (with 250 nV resolution). The digitized signals were then sent to control computers for real-time processing to implement the iBCI. The real-time iBCI was implemented in custom Simulink Real-Time software running on a dedicated PC with the xPC real-time operating system (Mathworks Inc., Natick, MA).
To extract action potentials (spikes), the signal was first common-average re-referenced within each array. Next, a digital bandpass filter from 250 Hz to 3 kHz was applied to each electrode before spike detection. For threshold crossing detection, we used a −4.5 x RMS threshold applied to each electrode, where RMS is the electrode-specific root-mean-square of the voltage time series recorded on that electrode. In keeping with standard iBCI practice, we did not spike sort (i.e., assign threshold crossings to specific single neurons) [26 (link)]–[29 (link)].

Deo D.R., Rezaii P., Hochberg L.R., Okamura A.M., Shenoy K.V, & Henderson J.M. (2021). Effects of Peripheral Haptic Feedback on Intracortical Brain-Computer Interface Control and Associated Sensory Responses in Motor Cortex. IEEE transactions on haptics, 14(4), 762-775.

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Neural Feature Extraction for iBCI Performance

Cited 2 times

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In all participants, broad-band neural recordings were sampled at 30 kHz and pre-processed in Simulink using the xPC real-time operating system (The Mathworks Inc., Natick, MA, US; RRID: SCR_014744). From each pre-processed channel, extraction of two neural features from non-overlapping 20 millisecond time bins was performed in real time, as illustrated in Fig. 1A. These included 192 unsorted threshold crossing (TC) and 192 spike band power (SBP) features. Here, we evaluated the neural space by characterizing TC and SBP features as opposed to sorted single units, in order for our results to be directly applicable to iBCI systems, and because iBCI performance has been shown to be comparable when derived from thresholded data as opposed to spike-sorted data^{89 (link)–92 (link)}. Supplementary Figs. S8–S11 illustrate a visual comparison of neural activity from threshold crossings and sorted single units extracted from identical channels. Unless otherwise stated, all offline analyses were performed using MATLAB software (The Mathworks Inc., Natick, MA, US; RRID: SCR_001622). Neural recordings, multiunit feature extraction methods, and single unit sorting methods for this study are described in more detail within the Supplementary Information.

Rastogi A., Vargas-Irwin C.E., Willett F.R., Abreu J., Crowder D.C., Murphy B.A., Memberg W.D., Miller J.P., Sweet J.A., Walter B.L., Cash S.S., Rezaii P.G., Franco B., Saab J., Stavisky S.D., Shenoy K.V., Henderson J.M., Hochberg L.R., Kirsch R.F, & Ajiboye A.B. (2020). Neural Representation of Observed, Imagined, and Attempted Grasping Force in Motor Cortex of Individuals with Chronic Tetraplegia. Scientific Reports, 10, 1429.

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Intracortical Neural Signal Processing

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In both participants, each intracortical microelectrode array was attached to a percutaneous pedestal connector on the head. Cables connected the pedestals to amplifiers (Blackrock Microsystems, Salt Lake City, UT) that bandpass filtered (0.3 Hz -7.5 kHz) and digitized (30 kHz) the neural signals from each channel on the microelectrode array. These digitized signals were pre-processed in Simulink using the xPC real-time operating system (The Mathworks Inc., Natick, MA, US). Each channel was bandpass filtered (250-5000 Hz), common average referenced (CAR), and down-sampled to 15 kHz in real time. CAR was implemented by selecting 60 channels from each microelectrode array that exhibited the lowest variance, and then averaging these channels together to yield an array-specific common average reference. This reference signal was subtracted from the signals from all channels within each of the arrays.

Rastogi A., Willett F.R., Abreu J., Crowder D.C., Murphy B., Memberg W.D., Vargas-Irwin C.E., Miller J.P., Sweet J.A., Walter B.L., Rezaii P.G., Stavisky S.D., Hochberg L.R., Shenoy K.V., Henderson J.M., Kirsch R.F., & Ajiboye A.B. (2020). The neural representation of force across grasp types in motor cortex of humans with tetraplegia.

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