Fascicle-Selective Bidirectional Peripheral Nerve Interface IC with 173dB FOM Noise-Shaping SAR ADCs and 1.38pJ/b Frequency-Multiplying Current-Ripple Radio Transmitter

Xu, Jianxiong; Filho, Jose Sales; Nag, Sayan; Long, Liam; Tejeiro, Camilo; Hwang, Eui-Hyoung; O'Leary, Gerard; Huang, Yulei; Kanchwala, Mustafa; Abdolrazzaghi, Mohammad; Tang, Chenxi; Liu, Patty; Yuan, Shuai; Li, Xilin; Eleftheriades, George V.; Zariffa, José; Genov, Roman

doi:10.1109/isscc42615.2023.10067626

Cited by 16 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 illustrates the primary components of a typical neural interface. The interface functions by recording data from the neural side, subsequently converting this analog data into a digital format [1], [2]. This digitized data is then relayed to a wearable or handheld device for comprehensive analysis.…”

Section: Introductionmentioning

confidence: 99%

MEDSA: A Memristive-passive Delta-Sigma ADC Circuit for Detecting Neural Signals

You,

Xu,

Amirsoleimani

et al. 2023

Preprint

View full text Add to dashboard Cite

In this study, we present an analog-to-digital converter (ADC) optimized for implantable neural interfaces. The proposed ADC integrates a series of memristors in both the input and feedback Digital-to-Analog Converter (DAC), significantly boosting the input impedance and making it suitable for neural interfaces. A defining feature of the ADC is the ability of the memristor resistance to adapt to various conditions such as large DC offset, motion, and stimulation artifacts. The model was simulated using 65nm MOSFET technology along with a physical memristor model, yielding an impressive signal-to-noiseand-distortion ratio (SNDR) of 62.7dB and a substantial Nyquist sampling rate of 50kHz. Power consumption is remarkably low, with less than nW for integrators, 5µW for the comparator, and 0.45 µW for the feedback DAC -a key requirement for neural interfaces implanted in the brain. The ADC demonstrates strong resilience against component mismatch, maintaining circuit stability even in variable conditions. Through its ability to adjust input resistance, the ADC can enhance its SNDR. This adaptive and robust ADC design shows promising potential for implantable neural interface applications.

show abstract

Section: Introductionmentioning

confidence: 99%

MEDSA: A Memristive-passive Delta-Sigma ADC Circuit for Detecting Neural Signals

You,

Xu,

Amirsoleimani

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Emerging trends of integrated-mixed-signal chips in ISSCC 2023

Chen¹,

Yang²

2023

J. Semicond.

View full text Add to dashboard Cite

Closed-loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface

Hwang

Long

Filho

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: Discriminating recorded afferent neural information can provide sensory feedback for closed-loop control of functional electrical stimulation, which restores movement to paralyzed limbs. Previous work achieved state-of-the-art off-line classification of electrical activity in different neural pathways recorded by a multi-contact nerve cuff electrode, by applying deep learning to spatiotemporal neural patterns. Objective: To incorporate this approach into closed-loop stimulation. Methods: Acute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel (8 x 8) nerve cuff electrode was implanted on each rat's sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each of the three target classes. A rule-based closed-loop controller was implemented to produce ankle movement trajectories using neural stimulation, based on the classified nerve recordings. Closed-loop stimulation was initiated by the detection of a heel prick, and induced dorsiflexion. The detection of dorsiflexion triggered stimulation to induce plantarflexion, and vice versa. A single trial began with a heel prick and ended when an incorrect state transition occurred or when a second heel prick was detected. Results: Closed-loop stimulation was successfully demonstrated in 6 subjects. Number of successful trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. Conclusion: This work demonstrates that a CNN applied to multi-contact nerve cuff recordings can be used for closed-loop control of functional electrical stimulation.

show abstract

Fascicle-Selective Bidirectional Peripheral Nerve Interface IC with 173dB FOM Noise-Shaping SAR ADCs and 1.38pJ/b Frequency-Multiplying Current-Ripple Radio Transmitter

Cited by 16 publications

References 7 publications

MEDSA: A Memristive-passive Delta-Sigma ADC Circuit for Detecting Neural Signals

MEDSA: A Memristive-passive Delta-Sigma ADC Circuit for Detecting Neural Signals

Emerging trends of integrated-mixed-signal chips in ISSCC 2023

Closed-loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface

Contact Info

Product

Resources

About