Rhd2216

Manufactured by Intan Technologies

Sourced in United States

The RHD2216 is a multi-channel digital potentiometer IC designed for precision electronic control applications. It features sixteen independent digital potentiometer channels with non-volatile memory, allowing for the storage of settings. The device operates on a wide range of supply voltages and provides a programmable resistance range.

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Lab products found in correlation

Rhd2132, by Intan Technologies (2 mentions) C3100, by Intan Technologies (1 mentions) Matlab, by MathWorks (1 mentions) C57bl 6j mice, by CLEA Japan (1 mentions)

9 protocols using rhd2216

Modular Wearable Electronics Assembly

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The electronics assembly was composed of a Kapton flex tab, a custom designed printed circuit board (PCB), sealing gasket, and custom glass filled nylon enclosure. 300 µm thick polyimide flex tabs were fabricated following standard flex PCB procedures using ½ oz. copper and plated with electroless nickel/immersion gold (ENIG).
The PCB consisted primarily of an electrophysiology amplifier chip (RHD2216, INTAN technologies), FPC socket (FH52, Hirose), and SPI connector for serial communication with the processing board (C3100, INTAN technologies) or, in other versions, a 32-bit embedded microcontroller (EFM32, Giant Gecko). The amplifier received, amplified, and digitized the sEMG signals collected by the garment. The embedded system interfaced with the garment via the FPC socket, and additionally performed the on-board level of effort analysis.
The enclosure served as the protection for the electronics from dust, impact, and water. It was a custom-designed printed clamshell made from glass filled nylon beads (PA 3200 GF, EOS) using selective laser sintering (P395, EOS). Magnets embedded in the plastic clamshell provided the closing force and sealing pressure to the TPU gasket. A circular hole at one end allowed the RS485 serial cable to connect the embedded system inside the enclosure to external systems outside the enclosure.

Ohiri K.A., Pyles C.O., Hamilton L.H., Baker M.M., McGuire M.T., Nguyen E.Q., Osborn L.E., Rossick K.M., McDowell E.G., Strohsnitter L.M, & Currano L.J. (2022). E-textile based modular sEMG suit for large area level of effort analysis. Scientific Reports, 12, 9650.

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Low-power Differential Amplifier for Wireless EEG

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A digital, low-power, 16-channel differential amplifier (RHD2216, 16-bit, INTAN Technologies LLC, Los Angeles, CA, USA) was embedded on the board. The output was sent to the Bluetooth SoC (nRF52832, Nordic Semiconductor) via a serial peripheral interface at 8 Mbit/s after band-pass filter was applied (cutoff frequencies of 1 Hz and 4 kHz).

Kim J., Kim C., Han H.B., Cho C.J., Yeom W., Lee S.Q, & Choi J.H. (2020). A bird’s-eye view of brain activity in socially interacting mice through mobile edge computing (MEC). Science Advances, 6(49), eabb9841.

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Continuous EEG Monitoring with Portable Logger

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One of the authors (TM) constructed a logger (Neury) with the capability to continuously acquire and store EEG from two bipolar channels at 200 Hz (band-with 0.5-70 Hz), using standard scalp disk electrodes. The device uses an Intan Technologies RHD2216 amplifier chip, and the data are stored in an internal memory (128 MB). The dimensions are 53 × 32 × 15 mm, and the weight is 35 g, including an internal Li-ion rechargeable battery (Fig. 3c). During the procedure, the device is attached to the chest of the patients below the clavicle, using a snap EKG gel electrode which can also function as electrical ground. For the validation studies, the electrodes of the logger were applied 1 cm apart from electrode positions of a medical grade aEEG device (TREX, XLTEK®) so that EEG from the two devices could be compared for the various rhythms and graphic elements.
The position of electrodes on the scalp was established by the clinical neurophysiologist with the aim of recording with the maximal amplitude the spikes selected for quantification in previous clinical sleep EEG recordings (either short-or long-term). At the end of the recording, the EEG signal is downloaded to a PC through a mini-USB port connection in the device. The study has been approved by the ethics committee of Centro Hospitalar Psiquiátrico de Lisboa..

Carvalho D., Mendes T., Dias A.I, & Leal A. (2020). Interictal spike quantification in continuous spike-wave of sleep (CSWS): Clinical usefulness of a wearable EEG device. Epilepsy & behavior : E&B, 104(Pt A).

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Characterization of Nerve Fiber Activity

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We delivered bipolar electrical stimulation to the L5 DRG using symmetric, biphasic waveforms of varying current-amplitude, pulse width, and frequency, depending on the experimental paradigm (see Recruitment, Tonic DRGS, and Mechanisms of ECAP Suppression). While DRGS was delivered, we recorded ECAPs from the sciatic nerve cuff electrodes. Electroneurogram (ENG) signals were sampled from the sciatic nerve cuff electrode at 30 kHz and digitized using an Intan headstage and recording system (RHD2216, Intan Technologies, Los Angeles, CA) (Fig. 1a). We used conduction velocities to determine which afferent fiber types were contributing to each identified ECAP (Aα/β: >14 m/s; Aδ: We calculated conduction velocities of the ECAPs using the distance between the DRGS and cuff electrodes and the latency of the first peak of the ECAP. We performed all processing and analysis of the data in MATLAB (MathWorks, Natick, MA). We bandpass-filtered (300-3000 Hz) the ENG data before further processing to identify ECAPs. We blanked stimulation artifacts over a time window ranging from 100 μs to 1 ms in duration, depending on the pulse width applied in a particular trial.

Ting J.E., Hooper C.A., Dalrymple A.N, & Weber D.J. (2024). Tonic Stimulation of Dorsal Root Ganglion Results in Progressive Decline in Recruitment of Aα/β-Fibers in Rats. Neuromodulation : journal of the International Neuromodulation Society.

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Wireless LFP Recording via CBRAIN

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Simultaneous wireless local field potential (LFP) recordings were performed using an implanted custom module called CBRAIN. The CBRAIN module used in this study has been previously described^{6 (link)}. Briefly, it is a complete mobile edge computing solution consisting of an amplifier (RHD2216, Intan Technologies, Los Angeles, CA), telemetry based on a Bluetooth SoC (nRF52832, Nordic Semiconductor, Trondheim, Norway), a Cortex-M4 microprocessor (embedded in the Bluetooth SoC), a power supply, and LEDs in a small headstage with a weight of 2.6 g, inclusive of a 2.0 g lithium polymer battery. A custom GUI software (CBRAIN Studio) written in MATLAB was used for data acquisition. The sampling frequency was set to 1024 Hz. The data were segmented at the 30-min interval, and each segment was saved onto a separate text file. The data in a hexadecimal, space-delimited text format were transmitted via a Bluetooth Enhanced ShockBurst broadcasting scheme to the recording computer.

Lee J., Kwak D., Lee G.U., Kim C.Y., Kim J., Park S.H., Choi J.H., Lee S.Q, & Choe H.K. (2024). Social context modulates multibrain broadband dynamics and functional brain-to-brain coupling in the group of mice. Scientific Reports, 14, 11439.

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EMG and CNAP Signal Acquisition

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The RHD 2216 (Intan Technologies, LA, USA) was used for EMG acquisition at a sampling rate of 30 kHz. A 60 Hz notch filter and a band-pass filter with cutoff frequencies of 0.1 Hz and 2.5 kHz were applied in signal recording. The RHD 2132 (Intan Technologies, Los Angeles, CA, USA) was used for compound neural action potential (CNAP) acquisition at sampling rate of 30 kHz. A notch filter and band-pass filter were applied in the same way as the RHD 2216. The acquired signal was signal-processed using MATLAB 2020a software. Sixty evoked EMGs from a TA muscle and CNAPs from a CPN in every second were recorded and averaged to reduce noise.

Cho Y., Shin H., Park J, & Lee S. (2021). Advanced Neural Interface toward Bioelectronic Medicine Enabled by Micro-Patterned Shape Memory Polymer. Micromachines, 12(6), 720.

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Multimodal Neurophysiological Recordings

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Signals from all electrodes were recorded using an Intan Technologies amplifier chip (RHD2216, sampling rate 20 KHz). Local field potentials were stored at 1250 Hz. Analyses were performed with custom made Matlab programs, based on generic code that can be downloaded at www.battaglia.nl/computing/ and http://fmatoolbox.sourceforge.net/.
Local field potentials were recorded using tungsten wires with PFA isolation (0.002” bare) and single units using home-made tetrodes formed by twisting insulated Nichrome Wire (0.001” bare). Tetrodes were plated to reduce impedances to around 100 kΩ using gold solution (Neuralynx).
For spike sorting, extracted waveforms were sorted via a semi-automated cluster cutting procedure using KlustaKwik and Klusters (http://neurosuite.sourceforge.net/) Recordings were visualized and processed using NeuroScope and NDManager (http://neurosuite.sourceforge.net/).

Bagur S., Lefort J.M., Lacroix M.M., de Lavilléon G., Herry C., Chouvaeff M., Billand C., Geoffroy H, & Benchenane K. (2021). Breathing-driven prefrontal oscillations regulate maintenance of conditioned-fear evoked freezing independently of initiation. Nature Communications, 12, 2605.

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Electrophysiological Signal Acquisition and Processing

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We used Intan Technologies differential (RHD2216) and referential (RHD2132) preamplifiers to acquire electrophysiological signals. The default pass-band for the filters in these preamplifiers is between 0.1 Hz and 7.5 kHz. A 16-bit analog to digital converter then samples the signal from the AC-coupled amplifiers. At 7.5 kHz, the cut-off frequency in the analog low-pass filter is much higher than what is used conventionally (e.g. 3.5 kHz in 12, 23 ). Data segments were filtered with a causal band-pass filter with 250 Hz to 7.5 kHz pass-band, and then thresholded to detect single-unit activity. Units were detected as instances of threshold crossings where thresholds were specified as multiples of the standard deviation of the filtered background signals. The background signals represent separate epochs of time that are free of distinguishable spiking activity and are used to calculate the mean and variance of the background distribution. We detected units with a threshold of 5-7 standard deviations. When spike events were detected, the waveform of each action potential was extracted from the data.

Bahari F., Billard M.W., Kimbugwe J., Curay C.M., Watson G.D., Alloway K.D., & Gluckman B.J. (2020). Probing Causality of the Brainstem-Hypothalamic Murine Models of Sleep-Wake Regulation.

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Comparative Analysis of Mouse EEG and EMG Datasets

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Two separate mouse datasets were used in this study. We used our own datasets for preliminary algorithm development. EEG and EMG signals from male C57BL/6J mice 10 to 14 weeks old (Clea Japan Inc., Japan) were recorded as described previously 9 . Briefly, mice were anesthetized with isoflurane, and stainless-steel screws and wires were surgically implanted in the skull and into the trapezius muscle, respectively. They served as electrodes and were connected to a microtip amplifier (Intan, RHD2216, 16-channel amplifier chip with bipolar inputs) and an Open Ephys acquisition board for recording 14 . All data were recorded at least one week after the electrode implant surgery. EEG and EMG signals from four mice were saved in a digital format file for further processing. For the performance test of our new algorithm, we used a small-scale dataset (Tsukuba-14 dataset) from a previous study 8 .
The Tsukuba-14 dataset contains data segments from 14 mice, and each segment contains four days of data (17,280 epochs of 20 s) for a single mouse.

Gao T., Li J., Watanabe Y., Hung C.J., Yamanaka A., Horie K., Yanagisawa M., Ohsawa M., & Kume K. (2021). GI-SleepNet: A Highly Versatile Image-based Sleep Classification using a Deep Learning Algorithm.

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