A High Performance Delta-Sigma Modulator for Neurosensing

Xu, Jian; Zhao, Mi; Wu, Xiaobo; Islam, Kafiul; Yang, Zhi

doi:10.3390/s150819466

Cited by 8 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neuronix : members of the Neuronix chip family are a fully-integrated neural recorder application-specific integrated circuits (ASIC) based on the frequency-shaping (FS) architecture [58,59,[61][62][63][65][66][67] and high-resolution analog-to-digital converters (ADC) [38,56,60,64] that we have pioneered. At the sampling rate of 40 kHz, the recorder is optimized to acquire neural data with a maximum gain of 60 dB in the bandwidth 300-3000 Hz and approximately 40 dB in the bandwidth 10-300 Hz while suppressing motion and stimulation artifacts.…”

Section: The Scorpius Neural Recording Systemmentioning

confidence: 99%

A bioelectric neural interface towards intuitive prosthetic control for amputees

Nguyen

Jiang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

ObjectiveWhile prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful.ApproachHere we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject’s motor intention.Main resultsA pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient’s true intention.SignificanceOur study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways.Clinical trialDExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.

show abstract

Section: The Scorpius Neural Recording Systemmentioning

confidence: 99%

A bioelectric neural interface towards intuitive prosthetic control for amputees

Nguyen

Jiang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Neuronix: members of the Neuronix chip family are a fully-integrated neural recorder applicationspecific integrated circuits (ASIC) based on the frequency-shaping (FS) architecture [13,35,[42][43][44][45][46][47] and high-resolution analog-to-digital converters (ADC) [48][49][50][51] that we have pioneered. At the sampling rate of 40 kHz, the recorder is optimized to acquire neural data with a maximum gain of 60 dB in the bandwidth 300-3000 Hz and approximately 40 dB in the bandwidth 10-300 Hz while suppressing motion and stimulation artifacts.…”

Section: The Scorpius Neural Recording Systemmentioning

confidence: 99%

A bioelectric neural interface towards intuitive prosthetic control for amputees

Nguyen

Jiang

et al. 2020

J. Neural Eng.

Self Cite

View full text Add to dashboard Cite

Objective. While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful. Approach. Here we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject’s motor intention. Main results. A pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient’s true intention. Significance. Our study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways. Clinical trial: DExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.

show abstract

“…The total power consumption of the modulator is 13 µW, which corresponds to a figure-of-merit (FOM) of 45 fJ/conversion step. These competitive circuit specifications make this design a good candidate for building high precision neuro sensors [3].…”

Section: Introductionmentioning

confidence: 99%

A 143 dB Delta-Sigma Modulator for Biomedical Applications

Hussain

Ahmad²,

Ahmad³

et al. 2023

IJEMD-BMCR

View full text Add to dashboard Cite

This paper presents a single loop forth-order delta-sigma modulator topology of cascade of integrator with multiple feedback This paper presents a single loop forth-order delta-sigma modulator topology of cascade of integrator with multiple feedback (CIFB) . The number of levels in the quantizer increases for higher performance. The modulator noice transfer function (NTF) and signal transfer function (STF) exploited for higher performance. The out-of-band gain (OBG) of 1.5 selected considering the stability of higher order modulator. The high oversampling ratio (OSR) considered for small bandwidth applications. The maximum quantization noise is suppressed by NTF zeros optimization. A full-scale signal of the modulator is 0.55-V considering the higher order loop filter stability. Due to CIFB topology, there is no peaking in the STF while STF response is flat. The zeros of the NTF are optimized for maximum quantization noise and poles are placed inside the unit circle. The complete modulator loop filter has four integrators in the loop filter with multiple feedback digital-to-analog converter (DAC) for maximum stability. The complete modulator simulation shows it an achieve signal-to-noise (SNR) of 1.43 dB with oversampling ratio (OSR) of 128.

show abstract

A High Performance Delta-Sigma Modulator for Neurosensing

Cited by 8 publications

References 47 publications

A bioelectric neural interface towards intuitive prosthetic control for amputees

A bioelectric neural interface towards intuitive prosthetic control for amputees

A bioelectric neural interface towards intuitive prosthetic control for amputees

A 143 dB Delta-Sigma Modulator for Biomedical Applications

Contact Info

Product

Resources

About