IEEE Transactions on Biomedical Circuits and Systems

Journal: IEEE Transactions on Biomedical Circuits and Systems

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IEEE

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1932-4545
1940-9990

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Publications 1 - 10 of 54

A Multimodal and Multifunctional CMOS Cellular Interfacing Array for Digital Physiology and Pathology Featuring an Ultra Dense Pixel Array and Reconfigurable Sampling Rate
Wang, Adam Y.; Sheng, Yuguo; Li, Wanlu; et al. (2022)
IEEE Transactions on Biomedical Circuits and Systems
The article presents a fully integrated multimodal and multifunctional CMOS biosensing/actuating array chip and system for multi-dimensional cellular/tissue characterization. The CMOS chip supports up to 1,568 simultaneous parallel readout channels across 21,952 individually addressable multimodal pixels with 13 μm × 13 μm 2-D pixel pitch along with 1,568 Pt reference electrodes. These features allow the CMOS array chip to perform multimodal physiological measurements on living cell/tissue samples with both high throughput and single-cell resolution. Each pixel supports three sensing and one actuating modalities, each reconfigurable for different functionalities, in the form of full array (FA) or fast scan (FS) voltage recording schemes, bright/dim optical detection, 2-/4-point impedance sensing (ZS), and biphasic current stimulation (BCS) with adjustable stimulation area for single-cell or tissue-level stimulation. Each multi-modal pixel contains an 8.84 μm × 11 μm Pt electrode, 4.16 μm × 7.2 μm photodiode (PD), and in-pixel circuits for PD measurements and pixel selection. The chip is fabricated in a standard 130nm BiCMOS process as a proof of concept. The on-chip electrodes are constructed by unique design and in-house post-CMOS fabrication processes, including a critical Al shorting of all pixels during fabrication and Al etching after fabrication that ensures a high-yield planar electrode array on CMOS with high biocompatibility and long-term measurement reliability. For demonstration, extensive biological testing is performed with human and mouse progenitor cells, in which multidimensional biophysiological data are acquired for comprehensive cellular characterization.
Efficient Sample Delay Calculation for 2-D and 3-D Ultrasound Imaging
Ibrahim, Aya; Hager, Pascal A.; Bartolini, Andrea; et al. (2017)
IEEE Transactions on Biomedical Circuits and Systems
Flexible Unipolar IGZO Transistor-Based Integrate and Fire Neurons for Spiking Neuromorphic Applications
Lebanov, Ana; Velazquez Lopez, Mauricio; De Roose, Florian; et al. (2023)
IEEE Transactions on Biomedical Circuits and Systems
In this article, three different implementations of an Axon-Hillock circuit are presented, one of the basic building blocks of spiking neural networks. In this work, we explored the design of such circuits using a unipolar thin-film transistor technology based on amorphous InGaZnO, often used for large-area electronics. All the designed circuits are fabricated by direct material deposition and patterning on top of a flexible polyimide substrate. Axon-Hillock circuits presented in this article consistently show great adaptability of the basic properties of a spiking neuron such as output spike frequency adaptation and output spike width adaptation. Additional degrees of adaptability are demonstrated with each of the Axon-Hillock circuit varieties: neuron circuit threshold voltage adaptation, differentiation between input signal importance, and refractory period modulation. The proposed neuron can change its firing frequency up to three orders of magnitude by varying a single voltage brought to a circuit terminal. This allows the neuron to function, and potentially learn, at vastly different timescales that coincide with the biologically meaningful timescales, going from milliseconds to seconds, relevant for circuits meant for interaction with the environment. Thanks to careful design choices, the average measured power consumption is kept in the nW range, realistically allowing upscaling towards the spiking neural networks in the future. The spiking neuron with refractory period modulation presented in this work has an area of 607.3 μm × 492.2 μm, it experimentally demonstrated firing rates as low as 11.926 mHz, and its energy consumption per spike is ≈ 700 pJ at 30 Hz.
Real-Time Motor Unit Tracking From sEMG Signals With Adaptive ICA on a Parallel Ultra-Low Power Processor
Orlandi, Mattia; Rapa, Pierangelo Maria; Zanghieri, Marcello; et al. (2024)
IEEE Transactions on Biomedical Circuits and Systems
Spike extraction by blind source separation (BSS) algorithms can successfully extract physiologically meaningful information from the sEMG signal, as they are able to identify motor unit (MU) discharges involved in muscle contractions. However, BSS approaches are currently restricted to isometric contractions, limiting their applicability in real-world scenarios. We present a strategy to track MUs across different dynamic hand gestures using adaptive independent component analysis (ICA): first, a pool of MUs is identified during isometric contractions, and the decomposition parameters are stored; during dynamic gestures, the decomposition parameters are updated online in an unsupervised fashion, yielding the refined MUs; then, a Pan-Tompkins-inspired algorithm detects the spikes in each MUs; finally, the identified spikes are fed to a classifier to recognize the gesture. We validate our approach on a 4-subject, 7-gesture + rest dataset collected with our custom 16-channel dry sEMG armband, achieving an average balanced accuracy of 85.58 ± 14.91% and macro-F1 score of 85.86 ± 14.48%. We deploy our solution onto GAP9, a parallel ultra-low-power microcontroller specialized for computation-intensive linear algebra applications at the edge, obtaining an energy consumption of 4.72 mJ @ 240 MHz and a latency of 121.3 ms for each 200 ms-long window of sEMG signal.
Impedance Spectroscopy and Electrophysiological Imaging of Cells With a High-Density CMOS Microelectrode Array System
Viswam, Vijay; Bounik, Raziyeh; Shadmani, Amir; et al. (2018)
IEEE Transactions on Biomedical Circuits and Systems
sEMG-Driven Hand Dynamics Estimation With Incremental Online Learning on a Parallel Ultra-Low-Power Microcontroller
Zanghieri, Marcello; Rapa, Pierangelo Maria; Orlandi, Mattia; et al. (2024)
IEEE Transactions on Biomedical Circuits and Systems
Surface electromyography (sEMG) is a State-of-the-Art (SoA) sensing modality for non-invasive human-machine interfaces for consumer, industrial, and rehabilitation use cases. The main limitation of the current sEMG-driven control policies is the sEMG's inherent variability, especially cross-session due to sensor repositioning; this limits the generalization of the Machine/Deep Learning (ML/DL) in charge of the signal-to-command mapping. The other hot front on the ML/DL side of sEMG-driven control is the shift from the classification of fixed hand positions to the regression of hand kinematics and dynamics, promising a more versatile and fluid control. We present an incremental online-training strategy for sEMG-based estimation of simultaneous multi-finger forces, using a small Temporal Convolutional Network suitable for embedded learning-on-device. We validate our method on the HYSER dataset, cross-day. Our incremental online training reaches a cross-day Mean Absolute Error (MAE) of (9.58 +/- 3.89)% of the Maximum Voluntary Contraction on HYSER's RANDOM dataset of improvised, non-predefined force sequences, which is the most challenging and closest to real scenarios. This MAE is on par with an accuracy-oriented, non-embeddable offline training exploiting more epochs. Further, we demonstrate that our online training approach can be deployed on the GAP9 ultra-low power microcontroller, obtaining a latency of 1.49 ms and an energy draw of just 40.4 uJ per forward-backward-update step. These results show that our solution fits the requirements for accurate and real-time incremental training-on-device.
Online Learning and Classification of EMG-Based Gestures on a Parallel Ultra-Low Power Platform using Hyperdimensional Computing
Benatti, Simone; Montagna, Fabio; Kartsch, Victor; et al. (2019)
IEEE Transactions on Biomedical Circuits and Systems
This work presents a wearable EMG gesture recognition system based on the hyperdimensional (HD) computing paradigm, running on a programmable Parallel Ultra-Low-Power (PULP) platform. The processing chain includes efficient on-chip training, which leads to a fully embedded implementation with no need to perform any offline training on a personal computer. The proposed solution has been tested on 10 subjects in a typical gesture recognition scenario achieving 85% average accuracy on 11 gestures recognition, which is aligned with the State-Of-the-Art (SoA), with the unique capability of performing online learning. Furthermore, by virtue of the Hardware (HW) friendly algorithm and of the efficient PULP System-on-Chip (SoC) (Mr. Wolf) used for prototyping and evaluation, the energy budget required to run the learning part with 11 gestures is 10.04mJ, and 83.2uJ per classification. The system works with a average power consumption of 10.4mW in classification, ensuring around 29h of autonomy with a 100mAh battery. Finally, the scalability of the system is explored by increasing the number of channels (up-to 256 electrodes), demonstrating the suitability of our approach as universal, energy-efficient biopotential wearable recognition framework.
Robust Real-Time Embedded EMG Recognition Framework Using Temporal Convolutional Networks on a Multicore IoT Processor
Zanghieri, Marcello; Benatti, Simone; Burrello, Alessio; et al. (2020)
IEEE Transactions on Biomedical Circuits and Systems
A CMOS Multi-Modal Electrochemical and Impedance Cellular Sensing Array for Massively Paralleled Exoelectrogen Screening
Kumashi, Sagar R.; Jung, Doohwan; Park, Jongseok; et al. (2021)
IEEE Transactions on Biomedical Circuits and Systems
The paper presents a 256-pixel CMOS sensor array with in-pixel dual electrochemical and impedance detection modalities for rapid, multi-dimensional characterization of exoelectrogens. The CMOS IC has 16 parallel readout channels, allowing it to perform multiple measurements with a high throughput and enable the chip to handle different samples simultaneously. The chip contains a total of 2 × 256 working electrodes of size 44 μm × 52 μm, along with 16 reference electrodes of dimensions 56 μm × 399 μm and 32 counter electrodes of dimensions 399 μm × 106 μm, which together facilitate the high resolution screening of the test samples. The chip was fabricated in a standard 130nm BiCMOS process. The on-chip electrodes are subjected to additional fabrication processes, including a critical Al-etch step that ensures the excellent biocompatibility and long-term reliability of the CMOS sensor array in bio-environment. The electrochemical sensing modality is verified by detecting the electroactive analyte NaFeEDTA and the exoelectrogenic Shewanella oneidensis MR-1 bacteria, illustrating the chip's ability to quantify the generated electrochemical current and distinguish between different analyte concentrations. The impedance measurements with the HEK-293 cancer cells cultured on-chip successfully capture the cell-to-surface adhesion information between the electrodes and the cancer cells. The reported CMOS sensor array outperforms the conventional discrete setups for exoelectrogen characterization in terms of spatial resolution and speed, which demonstrates the chip's potential to radically accelerate synthetic biology engineering.
Sleep and Wake Classification With ECG and Respiratory Effort Signals
Karlen, Walter Christian; Mattiussi, Claudio; Floreano, Dario (2009)
IEEE Transactions on Biomedical Circuits and Systems

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Journal: IEEE Transactions on Biomedical Circuits and Systems

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