A Bidirectional Neural Interface Circuit With Active Stimulation Artifact Cancellation and Cross-Channel Common-Mode Noise Suppression

Mendrela, Adam E.; Cho, Ji-Hyun; Fredenburg, Jeffrey A.; Nagaraj, Vivek; Netoff, Theoden I.; Flynn, Michael P.; Yoon, Euisik

doi:10.1109/jssc.2015.2506651

Cited by 61 publications

(13 citation statements)

References 23 publications

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“…Besides that, template subtraction techniques suffer from varying artefact morphology stemming from undersampling the artefact shape and misalignment between stimulation and sample timing (Qian et al 2017, Zhou et al 2018. Similarly, adaptive filtering methods filter the stimulation pulse (Mendrela et al 2016) or the artefact recorded on a neighbouring channel (Basir-Kazeruni et al 2017) in order to estimate and subtract the artefact while filter coefficients are adapted. According to Zhou et al (2018), these subtraction methods can be implemented with low latency, but require artefact detection, template building and on-board memory for template storage.…”

Section: Methodological Significancementioning

confidence: 99%

A high-performance 4 nV (√Hz)⁻¹ analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

et al. 2019

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Objective Recording of local field potentials (LFPs) during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, existing recording systems lack the ability to provide artefact-free high-frequency (>100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. Approach To solve this problem, we designed and developed a novel, low-noise and versatile analog front-end (AFE) that uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, we assessed the performance of the realised AFE by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 μs). A full performance comparison between the proposed AFE and an identical AFE, equipped with an 8th order analog Bessel notch filter, was also conducted. Main results A high-performance, 4 nV (Hz)−1 AFE that is capable of recording nV-scale signals was designed in accordance with the imposed specifications. Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5–250 Hz) during stimulation. Its sensing and stimulation artefact suppression capabilities outperformed the capabilities of the AFE equipped with the Bessel notch filter. Significance The designed AFE can precisely record LFP signals, in and without the presence of either unipolar or bipolar DBS, which renders it as a functional and practical AFE architecture to be utilised in a wide range of applications and environments. This work paves the way for the development of externalized research tools for closed-loop neuromodulation that use low- and higher-frequency LFPs as control signals.

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Section: Methodological Significancementioning

confidence: 99%

A high-performance 4 nV (√Hz)⁻¹ analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

et al. 2019

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“…The artifact is frequently larger than the neural signal of interest and can confound the latter (Hottowy et al, 2012). Although solutions to artifact removal have been developed for protocols with periodic stimulation pulses (Stanslaski et al, 2012;Mendrela et al, 2016;Basir-Kazeruni et al, 2017;Culaclii et al, 2018;Zhou et al, 2019), removal of artifacts from continuous complex stimulation waveforms, such as DS, are yet to be demonstrated. Still, a system level approach in Culaclii et al (2018), which learns the initial artifact template and subsequently subtracts it from the recurring artifacts in the recordings, can be extended to accommodate the continuous artifacts from DS.…”

Section: Stimulation Artifacts In Neural Recordings During Continuous Biomimetic Stimulationmentioning

confidence: 99%

A Biomimetic, SoC-Based Neural Stimulator for Novel Arbitrary-Waveform Stimulation Protocols

et al. 2021

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Novel neural stimulation protocols mimicking biological signals and patterns have demonstrated significant advantages as compared to traditional protocols based on uniform periodic square pulses. At the same time, the treatments for neural disorders which employ such protocols require the stimulator to be integrated into miniaturized wearable devices or implantable neural prostheses. Unfortunately, most miniaturized stimulator designs show none or very limited ability to deliver biomimetic protocols due to the architecture of their control logic, which generates the waveform. Most such designs are integrated into a single System-on-Chip (SoC) for the size reduction and the option to implement them as neural implants. But their on-chip stimulation controllers are fixed and limited in memory and computing power, preventing them from accommodating the amplitude and timing variances, and the waveform data parameters necessary to output biomimetic stimulation. To that end, a new stimulator architecture is proposed, which distributes the control logic over three component tiers – software, microcontroller firmware and digital circuits of the SoC, which is compatible with existing and future biomimetic protocols and with integration into implantable neural prosthetics. A portable prototype with the proposed architecture is designed and demonstrated in a bench-top test with various known biomimetic output waveforms. The prototype is also tested in vivo to deliver a complex, continuous biomimetic stimulation to a rat model of a spinal-cord injury. By delivering this unique biomimetic stimulation, the device is shown to successfully reestablish the connectivity of the spinal cord post-injury and thus restore motor outputs in the rat model.

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“…Several approaches have been proposed to use the temporal properties of spikes and artifacts to perform spike sorting [6,8,17,38,58,67,68]. In template subtraction methods, the estimated artifacts are subtracted from the measurements to isolate neural activity [13,23,46,65] and identify spikes [40]. However, obtaining templates of the artifact in isolation is not always possible [51].…”

Section: Introductionmentioning

confidence: 99%

Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach

Shokri,

Gogliettino,

Hottowy

et al. 2024

J. Neural Eng.

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Background: Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To address this issue, we introduce a novel approach that employs dynamical control systems to detect and decipher electrically evoked neural activity despite the presence of electrical artifacts.Methods: Our proposed method leverages the unique spatiotemporal patterns of neural activity and electrical artifacts to distinguish and identify individual neural spikes. We designed distinctive dynamical models for both the stimulation artifact and each neuron observed during spontaneous neural activity. We can estimate which neurons were active by analyzing the recorded voltage responses across multiple electrodes post-stimulation. This technique also allows us to exclude signals from electrodes heavily affected by stimulation artifacts, such as the stimulating electrode itself, yet still accurately differentiate between evoked spikes and electrical artifacts.Results: We applied our method to high-density multi-electrode recordings from the primate retina in an ex vivo setup, using a grid of 512 electrodes. Through repeated electrical stimulations at varying amplitudes, we were able to construct activation curves for each neuron. The curves obtained with our method closely resembled those derived from manual spike sorting. Additionally, the stimulation thresholds we estimated strongly agreed with those determined through manual analysis, demonstrating high reliability (R^2 = 0.951 for human 1 and R^2 = 0.944 for human 2).Conclusion: Our method can effectively separate evoked neural spikes from stimulation artifacts by exploiting the distinct spatiotemporal propagation patterns captured by a dense, large-scale multi-electrode array. This technique holds promise for future applications in real-time closed-loop stimulation systems and for managing multi-channel stimulation strategies.

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A Bidirectional Neural Interface Circuit With Active Stimulation Artifact Cancellation and Cross-Channel Common-Mode Noise Suppression

Cited by 61 publications

References 23 publications

A high-performance 4 nV (√Hz)⁻¹ analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

A high-performance 4 nV (√Hz)⁻¹ analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

A Biomimetic, SoC-Based Neural Stimulator for Novel Arbitrary-Waveform Stimulation Protocols

Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach

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A Bidirectional Neural Interface Circuit With Active Stimulation Artifact Cancellation and Cross-Channel Common-Mode Noise Suppression

Cited by 61 publications

References 23 publications

A high-performance 4 nV (√Hz)−1 analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

A high-performance 4 nV (√Hz)−1 analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

A Biomimetic, SoC-Based Neural Stimulator for Novel Arbitrary-Waveform Stimulation Protocols

Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach

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A high-performance 4 nV (√Hz)⁻¹ analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

A high-performance 4 nV (√Hz)⁻¹ analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation