Patient cable

Manufactured by Blackrock Microsystems

The Patient Cable is a component used to connect a patient to medical equipment, such as an electroencephalogram (EEG) or electrocardiogram (ECG) device. It is designed to transmit physiological signals from the patient to the medical equipment for monitoring and analysis purposes.

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Lab products found in correlation

Xpc real time operating system, by MathWorks (1 mentions) Neuroport system, by Blackrock Microsystems (1 mentions) Cerebus neural signal processor, by Blackrock Microsystems (1 mentions) Matlab, by MathWorks (1 mentions)

5 protocols using patient cable

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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Neuronal Data Recording for Primate Dexterity

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Neuronal data were recorded using the 128-channel Cerebus acquisition system (NSP, Blackrock Microsystems). The signal from each active electrode (96 out of the 100 electrodes were connected) was preprocessed by a head stage (monkey L: CerePort plug to Samtec adaptor, monkeys N and E: Patient cable, Blackrock Microsystems) with unity gain and then amplified with a gain of 5000 using the Front End Amplifier (Blackrock Microsystems). The raw signal was obtained with 30 kHz time resolution in a range of 0.3 Hz to 7.5 kHz. From this raw signal, two filter settings allowed us to obtain two different signals by using filters in two different frequency bands, the local field potential (LFP, low-pass filter at 250 Hz) and spiking activity (high-pass filter at 250 Hz). The LFPs were sampled at 1 kHz and saved on disk. On each channel, the experimenter set online a threshold for detection and extraction of potential spikes. All waveforms crossing the threshold were sampled at 30 kHz and snippets of 1.6 ms duration for monkey L and 1.3 ms for monkey N and E were saved for offline spike sorting. All behavioral data such as stimuli, switch release, force traces for thumb and index fingers and object displacement were also fed into the Cerebus, sampled at 1 kHz and stored for offline analysis. For more details and two representative data sets, see Brochier et al. (2018 (link)).

Riehle A., Brochier T., Nawrot M, & Grün S. (2018). Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex. Frontiers in Neural Circuits, 12, 52.

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Neurophysiology of Dexterous Monkey Hand Control

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The experimental apparatus used for these experiments is the same as described previously (Irwin et al., 2017 ; Nason et al., 2020 (link); Vaskov et al., 2018 ). Briefly, the monkeys’ Utah arrays were connected to the patient cable (Blackrock Microsystems) and raw 0.1Hz-7.5kHz unfiltered broadband activity at 30kSps, 300–1,000Hz activity at 2kSps, and threshold crossings at a −4.5RMS threshold were extracted from the neural recordings by the Cerebus for storage. The 2kSps and threshold crossing features were streamed to the xPC Target computer in real-time via a User Datagram Protocol packet structure. The xPC Target computer coordinated several components of the experiments. It binned threshold crossings and SBP in customizable bin sizes, coordinated target presentation, acquired measured finger group positions from one flex sensor per group (FS-L-0073–103-ST, Spectra Symbol, Salt Lake City, UT, USA), and transmitted finger positions along with target locations to an additional computer simulating movements of a virtual monkey hand (MusculoSkeletal Modeling Software) (Davoodi et al., 2007 (link)). Task parameters, states, and neural features were stored in real-time for later offline analysis.

Nason S.R., Mender M.J., Vaskov A.K., Willsey M.S., Kumar N.G., Kung T.A., Patil P.G, & Chestek C.A. (2021). Real-Time Linear Prediction of Simultaneous and Independent Movements of Two Finger Groups Using an Intracortical Brain-Machine Interface. Neuron, 109(19), 3164-3177.e8.

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Impedance Testing for Neural Electrodes

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Prior to (pre-stim) and after (post-stim) each 4 h stimulation run, we performed an impedance test by delivering a sinusoidal current at 1 kHz, ~10 nA to all electrodes (including control electrodes), simultaneously, using a Cerebus neural signal processor with a patient cable (Blackrock Microsystems Inc., Salt Lake City, UT). Impedance measurements were analyzed offline using custom code in MATLAB (Mathworks, Inc., Natick, MA). Initial impedances (that is, on the first day of stimulation) greater than 800 kΩ were considered out-of-specification (19% of the electrodes, most of which were on one array, which was damaged during sterilization). An additional 2% of the electrodes were excluded due to impedances exceeding 800 kΩ over several measurements later in the study (these eventually recovered to within specification, and so were attributed to transient failure of the impedance measurement apparatus).

Chen K.H., Dammann J.F., Boback J.L., Tenore F.V., Otto K.J., Gaunt R.A, & Bensmaia S.J. (2014). The effect of chronic intracortical microstimulation on the electrode–tissue interface. Journal of neural engineering, 11(2), 026004.

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Neural Control of Dexterous Monkey Hand

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The experimental apparatus used for these experiments is the same as described previously (Irwin et al., 2017; Nason et al., 2020; Vaskov et al., 2018) . Briefly, the monkeys' Utah arrays were connected to the patient cable (Blackrock Microsystems) and raw 0.1Hz-7.5kHz unfiltered broadband activity at 30kSps, 300-1,000Hz activity at 2kSps, and threshold crossings at a -4.5RMS threshold were extracted from the neural recordings by the Cerebus for storage. The 2kSps and threshold crossing features were streamed to the xPC Target computer in real-time via a User Datagram Protocol packet structure. The xPC Target computer coordinated several components of the experiments. It binned threshold crossings and SBP in customizable bin sizes, coordinated target presentation, acquired measured finger group positions from one flex sensor per group (FS-L-0073-103-ST, Spectra Symbol, Salt Lake City, UT, USA), and transmitted finger positions along with target locations to an additional computer simulating movements of a virtual monkey hand (MusculoSkeletal Modeling Software) (Davoodi et al., 2007) . Task parameters, states, and neural features were stored in real-time for later offline analysis.

Nason S.R., Mender M.J., Vaskov A.K., Willsey M.S., Patil P.G., & Chestek C.A. (2020). Real-Time Linear Prediction of Simultaneous and Independent Movements of Two Finger Groups Using an Intracortical Brain-Machine Interface.

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