For subject S1, ECoG and EFPs were recorded at 500 Hz (Nihon Kohden EEG-1100). For S2 and S3, ECoG and EFPs were recorded at 1000 Hz using a RZ2 Bioamp (Tucker Davis Technologies, Inc.). For S4 and S5, ECoG and EFPs were recorded at 2000 Hz using a Neuroport Neural Signal Processor (Blackrock Microsystems, Inc.). For all subjects, finger joint angles were recorded using a 22-sensor CyberGlove (Immersion) at the same sampling rate as the ECoG or EFP.
Neuroport neural signal processor
Manufactured by Blackrock Microsystems
Sourced in United States
The Neuroport Neural Signal Processor is a device designed for the acquisition, processing, and transmission of neural signals. It is capable of recording and transmitting neural data in real-time. The device features multiple channels and supports a range of neural recording modalities. It is intended for use in research and clinical applications involving neural signal analysis and monitoring.
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12 protocols using neuroport neural signal processor
1
Multimodal Neurophysiological Data Acquisition
2
Intracranial Depth Electrode Recording
3
Neural Spike Activity Analysis Protocol
4
Neural Signal Processing Protocol
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Neural data was acquired with the Neuroport Neural Signal Processor (Blackrock Microsystems). At the beginning of each test session a threshold was set for all recorded channels at −4.5 times the root-mean-square voltage (RMS) for Subject 1 or −5.25 times RMS for Subject 2. Firing rates for Subject 1 were estimated for each channel by binning the number of recorded threshold crossings every 30 ms (33 Hz update rate). For Subject 2, a 20 ms bin size was used (50 Hz update rate). Firing rates were low-pass filtered using an exponential smoothing function with a 450 ms window for Subject 1, and a 440 ms window for Subject 2. Each channel was considered to be a neural unit, though many channels recorded multi-unit activity.
5
Multimodal Neural Data Acquisition
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Neural data were acquired from each microelectrode at 30 kilosamples per second (0.3 Hz–7.5 kHz bandpass, 16-bit precision, range ± 8 mV) using a Neuroport Neural Signal Processor (Blackrock Microsystems, LLC). ECoG and sEEG signals were acquired using the clinical amplifier (Natus Medical, Inc.) at either 500 or 2000 samples per second (0.5 Hz high pass, low pass set to ¼ sampling rate, 24-bit precision). All ECoG data with sampling rates higher than 500 samples per second were subsequently filtered (4th order Butterworth) and downsampled to 500 samples per second in order to facilitate comparisons across patients and clinically-relevant sampling rates.
6
Neural and Electrocorticographic Data Acquisition
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Neural data were acquired from each microelectrode at 30 kilosamples per second (0.3 Hz-7.5 kHz bandpass, 16-bit precision, range ± 8 mV) using a Neuroport Neural Signal Processor (Blackrock Microsystems, LLC). ECoG and sEEG signals were acquired using the clinical amplifier (Natus Medical, Inc.) at either 500 or 2000 samples per second (0.5 Hz high pass, low pass set to ¼ sampling rate, 24-bit precision). All ECoG data with sampling rates higher than 500 samples per second were subsequently downsampled to 500 samples per second in order to facilitate comparisons across patients and clinically-relevant sampling rates.
7
Neural and Electrocorticographic Data Acquisition
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Neural data were acquired from each microelectrode at 30 kilosamples per second (0.3 Hz-7.5 kHz bandpass, 16-bit precision, range ± 8 mV) using a Neuroport Neural Signal Processor (Blackrock Microsystems, LLC). ECoG and sEEG signals were acquired using the clinical amplifier (Natus Medical, Inc.) at either 500 or 2000 samples per second (0.5 Hz high pass, low pass set to ¼ sampling rate, 24-bit precision). All ECoG data with sampling rates higher than 500 samples per second were subsequently downsampled to 500 samples per second in order to facilitate comparisons across patients and clinically-relevant sampling rates.
8
Neural Signals Acquisition and Processing
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Neural signals were acquired, amplified, bandpass-filtered (0.3 Hz–7.5 kHz) and digitized (30 kHz, 16 bits/sample) from the electrodes using NeuroPort Neural Signal Processors (Blackrock Microsystems Inc.).
Action potentials (spikes) were detected by high-pass filtering (250 Hz cut-off) the full-bandwidth signal, then thresholding at −3.5 times the root-mean-square voltage of the respective electrode. Although one or more source neurons may generate threshold crossings, we used raw threshold crossings for online control and only sorted spikes for offline analyses. Single neurons were identified using the k-medoids clustering method. We used the gap criteria [58 (link)] to determine the total number of waveform clusters. Clustering was performed on the first n ∈ {2, 3, 4} principal components, where n was selected to account for 95% of waveform variance.
Action potentials (spikes) were detected by high-pass filtering (250 Hz cut-off) the full-bandwidth signal, then thresholding at −3.5 times the root-mean-square voltage of the respective electrode. Although one or more source neurons may generate threshold crossings, we used raw threshold crossings for online control and only sorted spikes for offline analyses. Single neurons were identified using the k-medoids clustering method. We used the gap criteria [58 (link)] to determine the total number of waveform clusters. Clustering was performed on the first n ∈ {2, 3, 4} principal components, where n was selected to account for 95% of waveform variance.
9
Microelectrode Array Neural Signal Processing
10
Single Unit Recordings for Intracortical Brain-Computer Interface
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The signals from each array were analog filtered between 0.3–7500 Hz then sampled at 30 kHz by two Neuroport Neural Signal Processors (Blackrock Microsystems, Salt Lake City, UT). Custom code on a real-time computer running Simulink Real-Time Operating System (MathWorks, Natick, MA) was used to pre-process the signals, starting with down-sampling from 30 to 15 kHz. Refer to Supplementary Materials for details on channel selection, feature extraction, and decoder calibration. Differences in spike waveform shape and amplitude were used to identify single unit activity using custom-made software63 (link), and then manually inspected for consistency (OfflineSorter, Plexon Inc., Dallas, TX). All analyses of array data were performed on single unit spike rates. For simplicity we referred to well isolated single units as ‘neurons’ throughout the paper. Single units and trial numbers for the iBCI tasks are summarized in Table 2 .
Summary data for single unit and trial numbers during iBCI tasks.
| Eye-hand iBCI task | Multi-modal cues iBCI task | |||||
|---|---|---|---|---|---|---|
| MFG units | PCG units | Number of trials | MFG units | PCG units | Number of trials | |
| Session 1 | 52 | 64 | 153 | 72 | 56 | 267 |
| Session 2 | 62 | 67 | 185 | 73 | 58 | 470 |
| Session 3 | 37 | 63 | 131 | 51 | 61 | 273 |
MFG, middle frontal gyrus; PCG, precentral gyrus; iBCI, intracortical Brain Computer Interface.
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