Artefact-Suppressing Analog Spike Detection Circuit for Firing-Rate Measurements in Closed-Loop Retinal Neurostimulators

Erbsloh, Andreas; Viga, Reinhard; Seidl, Karsten; Kokozinski, Rainer

doi:10.1109/jsen.2021.3133716

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Similar to Blanking technology, soft-reset technology also uses the periodicity of artifacts to shield them ( Liu et al, 2016 ; Viswam et al, 2016 ; Shadmani et al, 2019 ; Erbsloh et al, 2021 ). The difference is that blanking technology performs shielding in the time domain, while soft-reset technology performs shielding in the frequency domain.…”

Section: Anti-artifacts Technologymentioning

confidence: 99%

“…During the stimulation cycle, the system will increase the adjustment current

in the pseudo resistor to reduce the equivalent impedance, thereby increasing the high-pass cutoff frequency of the amplifier to the kHz level to reduce its gain. In addition, the time constant of the amplifier becomes very small at this time (approximately in the range of 100

( Erbsloh et al, 2021 )). This allows the neural recording front end to quickly recover and function normally after the stimulation cycle ends.…”

Section: Anti-artifacts Technologymentioning

confidence: 99%

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Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs

Chen,

Liu,

Wan

et al. 2024

Front. Neurosci.

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In recent years, thanks to the development of integrated circuits, clinical medicine has witnessed significant advancements, enabling more efficient and intelligent treatment approaches. Particularly in the field of neuromedical, the utilization of brain-machine interfaces (BMI) has revolutionized the treatment of neurological diseases such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. The BMI acquires neural signals via recording circuits and analyze them to regulate neural stimulator circuits for effective neurological treatment. However, traditional BMI designs, which are often isolated, have given way to closed-loop brain-machine interfaces (CL-BMI) as a contemporary development trend. CL-BMI offers increased integration and accelerated response speed, marking a significant leap forward in neuromedicine. Nonetheless, this advancement comes with its challenges, notably the stimulation artifacts (SA) problem inherent to the structural characteristics of CL-BMI, which poses significant challenges on the neural recording front-ends (NRFE) site. This paper aims to provide a comprehensive overview of technologies addressing artifacts in the NRFE site within CL-BMI. Topics covered will include: (1) understanding and assessing artifacts; (2) exploring the impact of artifacts on traditional neural recording front-ends; (3) reviewing recent technological advancements aimed at addressing artifact-related issues; (4) summarizing and classifying the aforementioned technologies, along with an analysis of future trends.

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Section: Anti-artifacts Technologymentioning

confidence: 99%

“…During the stimulation cycle, the system will increase the adjustment current

( Erbsloh et al, 2021 )). This allows the neural recording front end to quickly recover and function normally after the stimulation cycle ends.…”

Section: Anti-artifacts Technologymentioning

confidence: 99%

Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs

Chen,

Liu,

Wan

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Benefiting from the computational function of neurons' firing behaviors, crickets can complete the escape instantly, which provides a new idea for robots to accomplish tasks such as obstacle avoidance or even higher-order intelligent behaviors. Nevertheless, it is expensive to implement such a computation principle in conventional hardware platforms by simulating high-fidelity neurons, especially those with diverse firing behaviors that need rich dynamics [15][16][17][18] . Locally active memristors (LAMs), with rich dynamics [19][20][21] , low power consumption 22 and good scalability [23][24][25] , show intriguing potential to build Hodgkin-Huxley (H-H) neurons with excellent bio-plausibility [26][27][28][29] .…”

mentioning

confidence: 99%

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Yang,

Zhu,

Zhang

et al. 2024

Nat Commun

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Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

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Technical survey of end-to-end signal processing in BCIs using invasive MEAs

Erbslöh,

Buron,

Ur-Rehman

et al. 2024

J. Neural Eng.

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Modern brain-computer interfaces and neural implants allow interaction between the tissue, the user and the environment, where people suffer from neurodegenerative diseases or injuries. This interaction can be achieved by using penetrating/invasive microelectrodes for extracellular recordings and stimulation, such as Utah or Michigan arrays. The application-specific signal processing of the extracellular recording enables the detection of interactions and enables user interaction. For example, it allows to read out movement intentions from recordings of brain signals for controlling a prosthesis or an exoskeleton. To enable this, computationally complex algorithms are used in research that cannot be executed onchip or on embedded systems. Therefore, an optimization of the end-to-end processing pipeline, from the signal condition on the electrode array over the analog pre-processing to spike-sorting and finally the neural decoding process, is necessary for hardwareinference in order to enable a local signal processing in real-time and to enable a compact system for achieving a high comfort level. is This paper presents a survey of system architectures and algorithms for end-to-end signal processing pipelines of neural activity on the hardware of such neural devices, including (i) on-chip signal pre-processing, (ii) spike-sorting on-chip or on embedded hardware and (iii) neural decoding on workstations. A particular focus for the hardware implementation is on low-power electronic design and artifact-robust algorithms with low computational effort and very short latency. For this, current challenges and possible solutions with support of novel machine learning techniques are presented in brief. In addition, we describe our future vision for next-generation BCIs.

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Artefact-Suppressing Analog Spike Detection Circuit for Firing-Rate Measurements in Closed-Loop Retinal Neurostimulators

Cited by 6 publications

References 19 publications

Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs

Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Technical survey of end-to-end signal processing in BCIs using invasive MEAs

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