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Ads1299

Manufactured by Texas Instruments
Sourced in United States, Norway

The ADS1299 is a 24-bit analog-to-digital converter (ADC) designed for biopotential measurement applications. It features low-noise performance, programmable gain, and on-chip reference, making it suitable for a variety of biomedical and instrumentation applications.

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9 protocols using ads1299

1

EMG-Guided Functional Electrical Stimulation

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The experimental setup is shown in Figure 3. First, an EMG board comprising ADS-1299 (Texas Instruments [27 ]) and Nucleo-F767ZI (STMicroelectornics [28 ]) was designed. We used a commercially available EMG chip to sample a set of EMG data at 1 kHz and transferred the data to a computer through a USB interface. Second, an FES device (RehaStim, Hasomed) was utilized to apply electrical stimulation. Finally, a Kendall hydrogel electrode (Covidien) and ValuTrode electrode (Axelgaard Manufacturing) were utilized for EMG and FES, respectively.
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2

Wireless 8-Channel Surface EMG Armband

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For data collection, we fabricated a custom wireless 8-channel surface EMG data acquisition armband, as shown in Figure 1. The armband’s electronics is a derivative of the OpenBCI Cyton (OpenBCI), miniaturized to a 19 mm by 22 mm footprint. We use an ADS1299 (Texas Instruments) as our analog front-end (AFE) for signal amplification and analog-to-digital conversion, and an nRF52832 (Nordic Semiconductor) microcontroller for wireless data streaming. Our armband’s system can stream 8 channels of 24-bit EMG readings at 1,000 Hz wirelessly to USB dongle that forwards the readings to a host PC, smartphone or virtual reality headset.
Our armband’s electrodes are dry electrodes 3D-printed from electrically conductive carbon-black infused thermoplastic polyurethane (TPU), PI-ETPU 95–250 Carbon Black (Palmiga Innovation). The electrodes are circular in shape with a diameter of 14 mm and have brass snap fasteners. We 3D-printed a flexible armband using Shore 98A TPU to house pairs of electrodes circumferentially, and connect the electrodes using snap fasteners wired to our wireless EMG system. A more thorough description of hardware design and characterization of the electrodes, including dependence on mechanical grounding with the skin, may be found in Prechayasomboon and Rombokas (2023) .
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3

Elbow Joint Kinematics and Muscle Activity Measurement

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The participant was seated and secured in a Biodex chair (Biodex Medical Systems, Inc., Shirley, NY) with nylon belts to constrain movement of the upper body and positioned with her shoulder abducted 85° with 0° horizontal adduction and with neutral internal and external rotation. The elbow center of rotation was positioned over a freely rotating mount with a rotational potentiometer (acting as a digital goniometer) to measure elbow rotation. The medial and lateral epicondyles were clamped with foam in the custom mount. A lightweight fiberglass cast was applied to her forearm (not crossing the elbow or wrist joints) to facilitate a comfortable rigid attachment of the arm to the robot at the forearm (Fig. 2).
Eight pairs of Ag/Ag-Cl gel electrodes were placed over the following muscle sites as prescribed in the guidelines set forth in Anatomical Guide for the Electromyographer: anterior, middle, and posterior deltoid, pectoralis major, teres complex, latissimus dorsi, biceps, and triceps [16 ]. A custom amplifier system based on the Texas Instruments ADS1299 was used to sample EMG at a frequency of 1 kHz and with a gain of 1k.
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4

Closed-Loop Seizure Detection and Stimulation

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The developed closed-loop feedback stimulation system comprised a recording module, a stimulation module, and custom LabVIEW (National Instruments, USA) program for computation (Figs. 1a and 8a). A biosignal sensing analog front-end chip (ADS1299, Texas Instruments, USA) and active filter circuitry (implemented with AD8508, Analog Devices, USA) were adopted as a measuring device for LFP recording. This module provides simultaneous recording for 8-channels (up to a 16 kS/s sampling rate for each channel with 24-bit resolution and a programmable gain amplifier). The isolated current source in the stimulation device primarily comprised an analog-to-digital converter (DAC8580, Texas Instruments; AD5420, Analog Devices), an operational amplifier (MC34074, ON Semiconductor, USA), and several analog multiplexer chips. This stimulator was able to drive voltages up to 14 V with a maximum amplitude of 3 mA and an arbitrary waveform. Both modules were connected to a PC using a microcontroller unit (SAM3X8E, Microchip Technology, USA), which provides DMA to enable high-speed USB communication. The recorded LFP was processed using custom LabVIEW program to detect electrographic seizures in real-time. When a seizure was detected, a trigger signal was delivered to the stimulation module.
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5

Smart Finger Sensor Array for Tactile Sensing

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The smart finger was integrated with a sensor array, signal acquisition module, processing module, communication module, and OLED screen. The sensor array was composed of four TENGs, each with high-purity materials: PA66, PET, PS, and PTFE film were used as the friction layer, and the Al foil was used as the friction layer attached on the surface of the high-purity materials, respectively. The size of each sensor is 1 cm by 1 cm. The case of the smart finger was printed by a three-dimensional printer. The sensor array was integrated under the fingertip, the acquisition module and the communication module were inside the cavity of the smart finger, and the OLED screen was fixed in the card slot above the finger. OpenBCI multichannel open-source module was used as the acquisition module, the analog front-end chip was Texas Instruments ADS1299 (high-gain, low-noise analog-to-digital converter (ADC), 24-bit channel resolution, and up to 16-kHz sampling rate), the main control chip was Arduino Uno (Atmel Atmega328P), and the communication was wireless (Bluetooth Low Energy) to realize wireless signal transmission. The machine learning part was completed on the computer side, using Python language.
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6

Portable Biosignal Acquisition and Analysis

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The ECG and SKNA were recorded by a portable data acquisition device designed in our previous work (Xing et al., 2022b (link)). It consists of low-noise analog-front-end (ADS1299, Texas Instruments, Dallas, TX) for bio-potential signal acquisition, a microcontroller (STM32L476, STMicroelectronics) for the management of the whole system, and a power management circuits (powered by a 3.7 V rechargeable lithium polymer battery). In order to reduce the system noise floor, a low-noise first-stage amplifier (INA128) was implemented with the ADS1299 chip. The clinical signals were measured at 4 kHz sampling frequency using conventional disposable silver/silver-chloride (Ag/AgCl) electrodes attached to the users’ chest. The signal measurements were carried out in a noise-free sound insulation room. After an adjustment period of at least 10-min, the 10-min signal of each subject was acquired in a supine position. The recorded signals were stored on a local trans-flash card, and processed off-line by MATLAB.
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7

EMG-Based Arm Control in VR

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EMG signals were sampled at 1 kHz (Texas Instruments ADS1299), amplified with a hardware gain of 2, and a software gain of 1000, and band-pass filtered between 70–300 Hz. Data were then processed using an embedded System on Module (Logic PD SOMDM3730). We extracted four time-domain features (mean absolute value, waveform length, zero crossings, and slope sign changes) and six autoregressive coefficients from each channel using 200 ms windows in 25 ms increments. These features were used to train a linear discriminant analysis (LDA) classifier. We computed output velocity using previously described proportional control and velocity ramp algorithms [34 (link)], [35 ]. After each processing window, the output class and velocity were wirelessly transmitted to a desktop computer and used to control an arm in a Unity-based virtual reality environment. The virtual arm was projected onto each subjects forearm using the location and orientation of the HTC Vive tracker.
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8

Validating Wearable GSR in Sleep Studies

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To validate the measured GSR in a sleep study, we made a simultaneous comparison of the fabricated bioelectronics with a conventional PSG system. For sleep quality recording, it is at least required to measure EEG, electrooculogram (EOG), and electromyogram (EMG) based on the contemporary American Academy of Sleep Medicine (AASM) guidelines [35 ]. Cloth gel-covered Ag/AgCl electrodes were placed on locations on a subject’s facial area for derivations of EEG (Fpz-M1), EOG (E1-E2), and chin EMG (Chin1-Chin2) as shown in Figure S2. Three nights of sleep were monitored by using both GSR and EEG devices at a subject’s home. The soft bioelectronics was powered by a rechargeable battery (capacity: 110 mAh) that could continuously record GSR data ~7 h during sleep (Figure S3). The GSR device was attached to the inner wrist of the subject’s non-dominant hand. For recording EEG, EOG, and EMG data, a customized circuit with nRF52 (Nordic Semiconductor, Trondheim, Norway) and ADS 1299 (Texas Instruments, Dallas, TX, USA) was used during a subject’s sleep. The study involved volunteers aged 18 and 40 and was conducted by following the approved IRB protocol (#H20211) at the Georgia Institute of Technology. Before the study, subjects agreed with the study procedures and provided signed consent forms.
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9

Multi-modal Wearable Biosensing System

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This study used a custom-built wearable system including two 8-channel Texas Instruments ADS1299 amplifiers combined to create a 16-channel recording platform. The system was thoroughly evaluated on EEG recordings by comparing it to a high quality laboratory-based system (NuAmps, Compumedics Neuroscan, Dresden, Germany) [19 (link)]. Compared to the laboratory-based system, the custom-built system showed no significant differences in both EEG-specific measures (such as power across bands, power ratio across bands, and pre-movement noise), and movement-related cortical potential specific measures (such as signal-to-noise ratio as well as time and amplitude of the negative peak) [19 (link)]. In this study, we developed the system to simultaneously record 9 channels of EEG and 4 channels of ECG (Figure 1A).
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