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Wang F, Chen X, Roelfsema PR. Comparison of electrical microstimulation artifact removal methods for high-channel-count prostheses. J Neurosci Methods 2024; 408:110169. [PMID: 38782123 DOI: 10.1016/j.jneumeth.2024.110169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 04/15/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Neuroprostheses are used to electrically stimulate the brain, modulate neural activity and restore sensory and motor function following injury or disease, such as blindness, paralysis, and other movement and psychiatric disorders. Recordings are often made simultaneously with stimulation, allowing the monitoring of neural signals and closed-loop control of devices. However, stimulation-evoked artifacts may obscure neural activity, particularly when stimulation and recording sites are nearby. Several methods have been developed to remove stimulation artifacts, but it remains challenging to validate and compare these methods because the 'ground-truth' of the neuronal signals may be contaminated by artifacts. NEW METHOD Here, we delivered stimulation to the visual cortex via a high-channel-count prosthesis while recording neuronal activity and stimulation artifacts. We quantified the waveforms and temporal properties of stimulation artifacts from the cortical visual prosthesis (CVP) and used them to build a dataset, in which we simulated the neuronal activity and the stimulation artifacts. We illustrate how to use the simulated data to evaluate the performance of six software-based artifact removal methods (Template subtraction, Linear interpolation, Polynomial fitting, Exponential fitting, SALPA and ERAASR) in a CVP application scenario. RESULTS We here focused on stimulation artifacts caused by electrical stimulation through a high-channel-count cortical prosthesis device. We find that the Polynomial fitting and Exponential fitting methods outperform the other methods in recovering spikes and multi-unit activity. Linear interpolation and Template subtraction recovered the local-field potentials. CONCLUSION Polynomial fitting and Exponential fitting provided a good trade-off between the quality of the recovery of spikes and multi-unit activity (MUA) and the computational complexity for a cortical prosthesis.
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Affiliation(s)
- Feng Wang
- Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), Amsterdam 1105 BA, the Netherlands.
| | - Xing Chen
- Department of Ophthalmology, University of Pittsburgh School of Medicine, 203 Lothrop St, Pittsburgh, PA 15213, US.
| | - Pieter R Roelfsema
- Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), Amsterdam 1105 BA, the Netherlands; Department of Ophthalmology, University of Pittsburgh School of Medicine, 203 Lothrop St, Pittsburgh, PA 15213, US; Department of Integrative Neurophysiology, VU University, De Boelelaan 1085, Amsterdam 1081 HV, the Netherlands; Department of Neurosurgery, Academic Medical Centre, Postbus 22660, Amsterdam 1100 DD, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut de la Vision, Paris F-75012, France.
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2
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Amjad U, Choi J, Gibson DJ, Murray R, Graybiel AM, Schwerdt HN. Synchronous Measurements of Extracellular Action Potentials and Neurochemical Activity with Carbon Fiber Electrodes in Nonhuman Primates. eNeuro 2024; 11:ENEURO.0001-24.2024. [PMID: 38918051 PMCID: PMC11232371 DOI: 10.1523/eneuro.0001-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 06/03/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
Measuring the dynamic relationship between neuromodulators, such as dopamine, and neuronal action potentials is imperative to understand how these fundamental modes of neural signaling interact to mediate behavior. We developed methods to measure concurrently dopamine and extracellular action potentials (i.e., spikes) in monkeys. Standard fast-scan cyclic voltammetric (FSCV) electrochemical (EChem) and electrophysiological (EPhys) recording systems are combined and used to collect spike and dopamine signals, respectively, from an array of carbon fiber (CF) sensors implanted in the monkey striatum. FSCV requires the application of small voltages at the implanted sensors to measure redox currents generated from target molecules, such as dopamine. These applied voltages create artifacts at neighboring EPhys measurement sensors which may lead to misclassification of these signals as physiological spikes. Therefore, simple automated temporal interpolation algorithms were designed to remove these artifacts and enable accurate spike extraction. We validated these methods using simulated artifacts and demonstrated an average spike recovery rate of 84.5%. We identified and discriminated cell type-specific units in the monkey striatum that were shown to correlate to specific behavioral task parameters related to reward size and eye movement direction. Synchronously recorded spike and dopamine signals displayed contrasting relations to the task variables, suggesting a complex relationship between these two modes of neural signaling. Future application of our methods will help advance our understanding of the interactions between neuromodulator signaling and neuronal activity, to elucidate more detailed mechanisms of neural circuitry and plasticity mediating behaviors in health and in disease.
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Affiliation(s)
- Usamma Amjad
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Jiwon Choi
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815
| | - Daniel J Gibson
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Raymond Murray
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Ann M Graybiel
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Helen N Schwerdt
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815
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Amjad U, Choi J, Gibson DJ, Murray R, Graybiel AM, Schwerdt HN. Synchronous Measurements of Extracellular Action Potentials and Neurochemical Activity with Carbon Fiber Electrodes in Nonhuman Primates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.23.573130. [PMID: 38187624 PMCID: PMC10769335 DOI: 10.1101/2023.12.23.573130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Measuring the dynamic relationship between neuromodulators, such as dopamine, and neuronal action potentials is imperative to understand how these fundamental modes of neural signaling interact to mediate behavior. Here, we developed methods to measure concurrently dopamine and extracellular action potentials (i.e., spikes) and applied these in a monkey performing a behavioral task. Standard fast-scan cyclic voltammetric (FSCV) electrochemical (EChem) and electrophysiological (EPhys) recording systems are combined and used to collect spike and dopamine signals, respectively, from an array of carbon fiber (CF) sensors implanted in the monkey striatum. FSCV requires the application of small voltages at the implanted sensors to measure redox currents generated from target molecules, such as dopamine. These applied voltages create artifacts at neighboring EPhys-measurement sensors, producing signals that may falsely be classified as physiological spikes. Therefore, simple automated temporal interpolation algorithms were designed to remove these artifacts and enable accurate spike extraction. We validated these methods using simulated artifacts and demonstrated an average spike recovery rate of 84.5%. This spike extraction was performed on data collected from concurrent EChem and EPhys recordings made in a task-performing monkey to discriminate cell-type specific striatal units. These identified units were shown to correlate to specific behavioral task parameters related to reward size and eye-movement direction. Synchronous measures of spike and dopamine signals displayed contrasting relations to the behavioral task parameters, as taken from our small set of representative data, suggesting a complex relationship between these two modes of neural signaling. Future application of our methods will help advance our understanding of the interactions between neuromodulator signaling and neuronal activity, to elucidate more detailed mechanisms of neural circuitry and plasticity mediating behaviors in health and in disease.
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Affiliation(s)
- Usamma Amjad
- Department of Bioengineering, University of Pittsburgh, USA
| | - Jiwon Choi
- Department of Bioengineering, University of Pittsburgh, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Daniel J Gibson
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
| | - Raymond Murray
- Department of Bioengineering, University of Pittsburgh, USA
| | - Ann M Graybiel
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
| | - Helen N Schwerdt
- Department of Bioengineering, University of Pittsburgh, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
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Sohn WJ, Lim J, Wang PT, Pu H, Malekzadeh-Arasteh O, Shaw SJ, Armacost M, Gong H, Kellis S, Andersen RA, Liu CY, Heydari P, Nenadic Z, Do AH. Benchtop and bedside validation of a low-cost programmable cortical stimulator in a testbed for bi-directional brain-computer-interface research. Front Neurosci 2023; 16:1075971. [PMID: 36711153 PMCID: PMC9878125 DOI: 10.3389/fnins.2022.1075971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/16/2022] [Indexed: 01/15/2023] Open
Abstract
Introduction Bi-directional brain-computer interfaces (BD-BCI) to restore movement and sensation must achieve concurrent operation of recording and decoding of motor commands from the brain and stimulating the brain with somatosensory feedback. Methods A custom programmable direct cortical stimulator (DCS) capable of eliciting artificial sensorimotor response was integrated into an embedded BCI system to form a safe, independent, wireless, and battery powered testbed to explore BD-BCI concepts at a low cost. The BD-BCI stimulator output was tested in phantom brain tissue by assessing its ability to deliver electrical stimulation equivalent to an FDA-approved commercial electrical cortical stimulator. Subsequently, the stimulator was tested in an epilepsy patient with subcortical electrocorticographic (ECoG) implants covering the sensorimotor cortex to assess its ability to elicit equivalent responses as the FDA-approved counterpart. Additional safety features (impedance monitoring, artifact mitigation, and passive and active charge balancing mechanisms) were also implemeneted and tested in phantom brain tissue. Finally, concurrent operation with interleaved stimulation and BCI decoding was tested in a phantom brain as a proof-of-concept operation of BD-BCI system. Results The benchtop prototype BD-BCI stimulator's basic output features (current amplitude, pulse frequency, pulse width, train duration) were validated by demonstrating the output-equivalency to an FDA-approved commercial cortical electrical stimulator (R 2 > 0.99). Charge-neutral stimulation was demonstrated with pulse-width modulation-based correction algorithm preventing steady state voltage deviation. Artifact mitigation achieved a 64.5% peak voltage reduction. Highly accurate impedance monitoring was achieved with R 2 > 0.99 between measured and actual impedance, which in-turn enabled accurate charge density monitoring. An online BCI decoding accuracy of 93.2% between instructional cues and decoded states was achieved while delivering interleaved stimulation. The brain stimulation mapping via ECoG grids in an epilepsy patient showed that the two stimulators elicit equivalent responses. Significance This study demonstrates clinical validation of a fully-programmable electrical stimulator, integrated into an embedded BCI system. This low-cost BD-BCI system is safe and readily applicable as a testbed for BD-BCI research. In particular, it provides an all-inclusive hardware platform that approximates the limitations in a near-future implantable BD-BCI. This successful benchtop/human validation of the programmable electrical stimulator in a BD-BCI system is a critical milestone toward fully-implantable BD-BCI systems.
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Affiliation(s)
- Won Joon Sohn
- Department of Neurology, University of California, Irvine, Irvine, CA, United States,*Correspondence: Won Joon Sohn ✉
| | - Jeffrey Lim
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Po T. Wang
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Haoran Pu
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Omid Malekzadeh-Arasteh
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Susan J. Shaw
- Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States,Department of Neurology, University of Southern California, Los Angeles, CA, United States
| | - Michelle Armacost
- Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States,Department of Neurology, University of Southern California, Los Angeles, CA, United States
| | - Hui Gong
- Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States,Department of Neurology, University of Southern California, Los Angeles, CA, United States
| | - Spencer Kellis
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Richard A. Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Charles Y. Liu
- Department of Neurosurgery, Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States,Department of Neurological Surgery, University of Southern California, Los Angeles, CA, United States
| | - Payam Heydari
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Zoran Nenadic
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States,Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
| | - An H. Do
- Department of Neurology, University of California, Irvine, Irvine, CA, United States,An H. Do ✉
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Momeni K, Pilkar R, Ravi M, Forrest GF. Isolating Transcutaneous Spinal Cord Stimulation Artifact to Identify Motor Response during Walking. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6569-6572. [PMID: 34892614 DOI: 10.1109/embc46164.2021.9630099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The objective of this investigation was to demonstrate the applicability of a custom-developed EMD-Notch filtering algorithm to isolate the scTS-induced artifact from sEMG signals during walking in an individual with motor-incomplete SCI. Overall, the EMD-Notch filtering algorithm provides an effective approach to isolate the scTS artifact, extract the sEMG data, and further study the modulation of the spinal neuronal networks during dynamic activities.Clinical Relevance- This investigation will help with the modification of individualized scTS parameters to achieve task-specific neuromodulatory effects.
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6
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Kim M, Moon Y, Hunt J, McKenzie KA, Horin A, McGuire M, Kim K, Hargrove LJ, Jayaraman A. A Novel Technique to Reject Artifact Components for Surface EMG Signals Recorded During Walking With Transcutaneous Spinal Cord Stimulation: A Pilot Study. Front Hum Neurosci 2021; 15:660583. [PMID: 34149379 PMCID: PMC8209256 DOI: 10.3389/fnhum.2021.660583] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022] Open
Abstract
Transcutaneous spinal cord electrical stimulation (tSCS) is an emerging technology that targets to restore functionally integrated neuromuscular control of gait. The purpose of this study was to demonstrate a novel filtering method, Artifact Component Specific Rejection (ACSR), for removing artifacts induced by tSCS from surface electromyogram (sEMG) data for investigation of muscle response during walking when applying spinal stimulation. Both simulated and real tSCS contaminated sEMG data from six stroke survivors were processed using ACSR and notch filtering, respectively. The performance of the filters was evaluated with data collected in various conditions (e.g., simulated artifacts contaminating sEMG in multiple degrees, various tSCS intensities in five lower-limb muscles of six participants). In the simulation test, after applying the ACSR filter, the contaminated-signal was well matched with the original signal, showing a high correlation (r = 0.959) and low amplitude difference (normalized root means square error = 0.266) between them. In the real tSCS contaminated data, the ACSR filter showed superior performance on reducing the artifacts (96% decrease) over the notch filter (25% decrease). These results indicate that ACSR filtering is capable of eliminating artifacts from sEMG collected during tSCS application, improving the precision of quantitative analysis of muscle activity.
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Affiliation(s)
- Minjae Kim
- Shirley Ryan AbilityLab, Chicago, IL, United States.,Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.,Interaction and Robotics Research Center, Korea Institute of Science and Technology (KIST), Seoul, South Korea
| | - Yaejin Moon
- Shirley Ryan AbilityLab, Chicago, IL, United States.,Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Jasmine Hunt
- Shirley Ryan AbilityLab, Chicago, IL, United States
| | | | - Adam Horin
- Shirley Ryan AbilityLab, Chicago, IL, United States.,Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Matt McGuire
- Shirley Ryan AbilityLab, Chicago, IL, United States
| | - Keehoon Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Levi J Hargrove
- Shirley Ryan AbilityLab, Chicago, IL, United States.,Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Arun Jayaraman
- Shirley Ryan AbilityLab, Chicago, IL, United States.,Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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Shah NP, Chichilnisky EJ. Computational challenges and opportunities for a bi-directional artificial retina. J Neural Eng 2020; 17:055002. [PMID: 33089827 DOI: 10.1088/1741-2552/aba8b1] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A future artificial retina that can restore high acuity vision in blind people will rely on the capability to both read (observe) and write (control) the spiking activity of neurons using an adaptive, bi-directional and high-resolution device. Although current research is focused on overcoming the technical challenges of building and implanting such a device, exploiting its capabilities to achieve more acute visual perception will also require substantial computational advances. Using high-density large-scale recording and stimulation in the primate retina with an ex vivo multi-electrode array lab prototype, we frame several of the major computational problems, and describe current progress and future opportunities in solving them. First, we identify cell types and locations from spontaneous activity in the blind retina, and then efficiently estimate their visual response properties by using a low-dimensional manifold of inter-retina variability learned from a large experimental dataset. Second, we estimate retinal responses to a large collection of relevant electrical stimuli by passing current patterns through an electrode array, spike sorting the resulting recordings and using the results to develop a model of evoked responses. Third, we reproduce the desired responses for a given visual target by temporally dithering a diverse collection of electrical stimuli within the integration time of the visual system. Together, these novel approaches may substantially enhance artificial vision in a next-generation device.
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Affiliation(s)
- Nishal P Shah
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America. Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA, United States of America. Department of Neurosurgery, Stanford University, Stanford, CA, United States of America. Author to whom any correspondence should be addressed
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A wireless and artefact-free 128-channel neuromodulation device for closed-loop stimulation and recording in non-human primates. Nat Biomed Eng 2018; 3:15-26. [PMID: 30932068 DOI: 10.1038/s41551-018-0323-x] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 10/30/2018] [Indexed: 11/08/2022]
Abstract
Closed-loop neuromodulation systems aim to treat a variety of neurological conditions by delivering and adjusting therapeutic electrical stimulation in response to a patient's neural state, recorded in real time. Existing systems are limited by low channel counts, lack of algorithmic flexibility, and the distortion of recorded signals by large and persistent stimulation artefacts. Here, we describe an artefact-free wireless neuromodulation device that enables research applications requiring high-throughput data streaming, low-latency biosignal processing, and simultaneous sensing and stimulation. The device is a miniaturized neural interface capable of closed-loop recording and stimulation on 128 channels, with on-board processing to fully cancel stimulation artefacts. In addition, it can detect neural biomarkers and automatically adjust stimulation parameters in closed-loop mode. In a behaving non-human primate, the device enabled long-term recordings of local field potentials and the real-time cancellation of stimulation artefacts, as well as closed-loop stimulation to disrupt movement preparatory activity during a delayed-reach task. The neuromodulation device may help advance neuroscientific discovery and preclinical investigations of stimulation-based therapeutic interventions.
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Culaclii S, Kim B, Lo YK, Li L, Liu W. Online Artifact Cancelation in Same-Electrode Neural Stimulation and Recording Using a Combined Hardware and Software Architecture. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2018; 12:601-613. [PMID: 29877823 PMCID: PMC6299268 DOI: 10.1109/tbcas.2018.2816464] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Advancing studies of neural network dynamics and developments of closed-loop neural interfaces requires the ability to simultaneously stimulate and record the neural cells. Recording adjacent to or at the stimulation site produces artifact signals that are orders of magnitude larger than the neural responses of interest. These signals often saturate the recording amplifier causing distortion or loss of short-latency evoked responses. This paper proposes a method to cancel the artifact in simultaneous neural recording and stimulation on the same electrode. By combining a novel hardware architecture with concurrent software processing, the design achieves neural signal recovery in a wide range of conditions. The proposed system uniquely demonstrates same-electrode stimulation and recording, with neural signal recovery in presence of stimulation artifact 100 dB larger in magnitude than the underlying signals. The system is tested both in vitro and in vivo, during concurrent stimulation and recording on the same electrode. In vivo results in a rodent model are compared to recordings made by a commercial neural amplifier system connected in parallel.
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10
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Zhou A, Johnson BC, Muller R. Toward true closed-loop neuromodulation: artifact-free recording during stimulation. Curr Opin Neurobiol 2018; 50:119-127. [DOI: 10.1016/j.conb.2018.01.012] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 01/12/2018] [Accepted: 01/17/2018] [Indexed: 11/29/2022]
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O'Shea DJ, Shenoy KV. ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings. J Neural Eng 2018; 15:026020. [PMID: 29265009 PMCID: PMC5833982 DOI: 10.1088/1741-2552/aaa365] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Electrical stimulation is a widely used and effective tool in systems neuroscience, neural prosthetics, and clinical neurostimulation. However, electrical artifacts evoked by stimulation prevent the detection of spiking activity on nearby recording electrodes, which obscures the neural population response evoked by stimulation. We sought to develop a method to clean artifact-corrupted electrode signals recorded on multielectrode arrays in order to recover the underlying neural spiking activity. APPROACH We created an algorithm, which performs estimation and removal of array artifacts via sequential principal components regression (ERAASR). This approach leverages the similar structure of artifact transients, but not spiking activity, across simultaneously recorded channels on the array, across pulses within a train, and across trials. The ERAASR algorithm requires no special hardware, imposes no requirements on the shape of the artifact or the multielectrode array geometry, and comprises sequential application of straightforward linear methods with intuitive parameters. The approach should be readily applicable to most datasets where stimulation does not saturate the recording amplifier. MAIN RESULTS The effectiveness of the algorithm is demonstrated in macaque dorsal premotor cortex using acute linear multielectrode array recordings and single electrode stimulation. Large electrical artifacts appeared on all channels during stimulation. After application of ERAASR, the cleaned signals were quiescent on channels with no spontaneous spiking activity, whereas spontaneously active channels exhibited evoked spikes which closely resembled spontaneously occurring spiking waveforms. SIGNIFICANCE We hope that enabling simultaneous electrical stimulation and multielectrode array recording will help elucidate the causal links between neural activity and cognition and facilitate naturalistic sensory protheses.
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Affiliation(s)
- Daniel J O'Shea
- Neurosciences Program, Stanford University, Stanford, CA 94305, United States of America. Department of Electrical Engineering, Stanford University, Stanford, CA 94305, United States of America
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12
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Mena GE, Grosberg LE, Madugula S, Hottowy P, Litke A, Cunningham J, Chichilnisky EJ, Paninski L. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. PLoS Comput Biol 2017; 13:e1005842. [PMID: 29131818 PMCID: PMC5703587 DOI: 10.1371/journal.pcbi.1005842] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 11/27/2017] [Accepted: 10/20/2017] [Indexed: 11/18/2022] Open
Abstract
Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.
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Affiliation(s)
- Gonzalo E. Mena
- Statistics Department, Columbia University, New York, New York, United States of America
| | - Lauren E. Grosberg
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Sasidhar Madugula
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Paweł Hottowy
- Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland
| | - Alan Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, California, United States of America
| | - John Cunningham
- Statistics Department, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - E. J. Chichilnisky
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Liam Paninski
- Statistics Department, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
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13
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Application of Empirical Mode Decomposition Combined With Notch Filtering for Interpretation of Surface Electromyograms During Functional Electrical Stimulation. IEEE Trans Neural Syst Rehabil Eng 2017; 25:1268-1277. [DOI: 10.1109/tnsre.2016.2624763] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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14
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Liu J, Li S, Li X, Klein C, Rymer WZ, Zhou P. Suppression of stimulus artifact contaminating electrically evoked electromyography. NeuroRehabilitation 2014; 34:381-9. [PMID: 24419021 DOI: 10.3233/nre-131045] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Electrical stimulation of muscle or nerve is a very useful technique for understanding of muscle activity and its pathological changes for both diagnostic and therapeutic purposes. During electrical stimulation of a muscle, the recorded M wave is often contaminated by a stimulus artifact. The stimulus artifact must be removed for appropriate analysis and interpretation of M waves. OBJECTIVES The objective of this study was to develop a novel software based method to remove stimulus artifacts contaminating or superimposing with electrically evoked surface electromyography (EMG) or M wave signals. METHODS The multiple stage method uses a series of signal processing techniques, including highlighting and detection of stimulus artifacts using Savitzky-Golay filtering, estimation of the artifact contaminated region with Otsu thresholding, and reconstruction of such region using signal interpolation and smoothing. The developed method was tested using M wave signals recorded from biceps brachii muscles by a linear surface electrode array. To evaluate the performance, a series of semi-synthetic signals were constructed from clean M wave and stimulus artifact recordings with different degrees of overlap between them. RESULTS The effectiveness of the developed method was quantified by a significant increase in correlation coefficient and a significant decrease in root mean square error between the clean M wave and the reconstructed M wave, compared with those between the clean M wave and the originally contaminated signal. The validity of the developed method was also demonstrated when tested on each channel's M wave recording using a linear electrode array. CONCLUSIONS The developed method can suppress stimulus artifacts contaminating M wave recordings.
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Affiliation(s)
- Jie Liu
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA
| | - Sheng Li
- Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, Houston, TX, USA The Neurorehabilitation Research Laboratory, The Institute of Rehabilitation and Research (TIRR)-Memorial Hermann Hospital, Houston, TX, USA
| | - Xiaoyan Li
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA
| | - Cliff Klein
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA
| | - William Z Rymer
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
| | - Ping Zhou
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, China
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Pilkar RB, Yarossi M, Forrest G. Empirical mode decomposition as a tool to remove the function electrical stimulation artifact from surface electromyograms: preliminary investigation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:1847-50. [PMID: 23366272 DOI: 10.1109/embc.2012.6346311] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Rectification of surface EMGs during electrical stimulations (ES) is still a problem to be solved. The broad band frequency components of ES artifact overlap with the EMG spectrum, make this task challenging. In this study, we investigate the potential use of empirical mode decomposition (EMD) method to remove the stimulus artifact from surface EMGs collected during such applications. We hypothesize that the EMD algorithm provides a suitable platform for decomposing the EMG signal into physically meaningful intrinsic modes which can be used to isolate ES artifact. Basic EMD is tested on two signals - ES induced EMG and EMG of voluntary contractions added with simulated ES signal. The algorithm isolates the EMG from ES artifact with considerable success. Further, the EMD method along with the energy operator -TKEO gives even better representation of the EMG signal. However, some high frequency data was lost during reconstruction process. Hence, there is further need to investigate the relationship between the EMD parameters and stimulus artifact properties so that the algorithm can be optimized to reconstruct pure artifact free EMG signal with minimum lost of data.
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16
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Limnuson K, Lu H, Chiel HJ, Mohseni P. VLSI implementation of a template subtraction algorithm for real-time stimulus artifact rejection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2010:2939-42. [PMID: 21095990 DOI: 10.1109/iembs.2010.5626247] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this paper, we present very-large-scale integrated (VLSI) implementation of a template subtraction algorithm for stimulus artifact rejection (SAR) in real time with applicability to closed-loop neuroprostheses. The SAR algorithm is based upon an infinite impulse response (IIR) temporal filtering technique, which can be efficiently implemented in VLSI with reduced power consumption and silicon area. We demonstrate that initialization of the memory within the system architecture using the first recorded stimulus artifact significantly decreases system response time as compared to the case without memory initialization. Two sets of pre-recorded neural data from an Aplysia californica are used to simulate the functionality of the proposed VLSI architecture in AMS 0.35 microm complementary metal-oxide-semiconductor (CMOS) technology. Depending upon the reproducibility in the shape of stimulus artifacts in vivo, the system eliminates virtually all artifacts in real time and recovers the extracellular neural activity with microW-level power consumption from 1.5 V.
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Affiliation(s)
- Kanokwan Limnuson
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA
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