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Zhong Y, Yao L, Pan G, Wang Y. Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD. IEEE Trans Neural Syst Rehabil Eng 2024; 32:662-671. [PMID: 38271166 DOI: 10.1109/tnsre.2024.3358491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings.
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Vidaurre C, Irastorza-Landa N, Sarasola-Sanz A, Insausti-Delgado A, Ray AM, Bibián C, Helmhold F, Mahmoud WJ, Ortego-Isasa I, López-Larraz E, Lozano Peiteado H, Ramos-Murguialday A. Challenges of neural interfaces for stroke motor rehabilitation. Front Hum Neurosci 2023; 17:1070404. [PMID: 37789905 PMCID: PMC10543821 DOI: 10.3389/fnhum.2023.1070404] [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: 10/14/2022] [Accepted: 08/28/2023] [Indexed: 10/05/2023] Open
Abstract
More than 85% of stroke survivors suffer from different degrees of disability for the rest of their lives. They will require support that can vary from occasional to full time assistance. These conditions are also associated to an enormous economic impact for their families and health care systems. Current rehabilitation treatments have limited efficacy and their long-term effect is controversial. Here we review different challenges related to the design and development of neural interfaces for rehabilitative purposes. We analyze current bibliographic evidence of the effect of neuro-feedback in functional motor rehabilitation of stroke patients. We highlight the potential of these systems to reconnect brain and muscles. We also describe all aspects that should be taken into account to restore motor control. Our aim with this work is to help researchers designing interfaces that demonstrate and validate neuromodulation strategies to enforce a contingent and functional neural linkage between the central and the peripheral nervous system. We thus give clues to design systems that can improve or/and re-activate neuroplastic mechanisms and open a new recovery window for stroke patients.
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Affiliation(s)
- Carmen Vidaurre
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
- Ikerbasque Science Foundation, Bilbao, Spain
| | | | | | | | - Andreas M. Ray
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Carlos Bibián
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Florian Helmhold
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Wala J. Mahmoud
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Iñaki Ortego-Isasa
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
| | - Eduardo López-Larraz
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Bitbrain, Zaragoza, Spain
| | | | - Ander Ramos-Murguialday
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
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Bahador N, Saha J, Rezaei MR, Utpal S, Ghahremani A, Chen R, Lankarany M. Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering. Bioengineering (Basel) 2023; 10:719. [PMID: 37370650 DOI: 10.3390/bioengineering10060719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.
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Affiliation(s)
- Nooshin Bahador
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
| | - Josh Saha
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Department of Electrical and Computer Engineering, University of Waterloo, Toronto, ON N2L 3G1, Canada
| | - Mohammad R Rezaei
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
| | - Saha Utpal
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
| | - Ayda Ghahremani
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Robert Chen
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON M5S 2E8, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network (UHN), Toronto, ON M5G 2A2, Canada
| | - Milad Lankarany
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network (UHN), Toronto, ON M5G 2A2, Canada
- Department of Physiology, University of Toronto, Toronto, ON M5S 2E8, Canada
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Cho W, Vidaurre C, An J, Birbaumer N, Ramos-Murguialday A. Cortical processing during robot and functional electrical stimulation. Front Syst Neurosci 2023; 17:1045396. [PMID: 37025164 PMCID: PMC10070684 DOI: 10.3389/fnsys.2023.1045396] [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: 09/15/2022] [Accepted: 02/28/2023] [Indexed: 04/08/2023] Open
Abstract
Introduction Like alpha rhythm, the somatosensory mu rhythm is suppressed in the presence of somatosensory inputs by implying cortical excitation. Sensorimotor rhythm (SMR) can be classified into two oscillatory frequency components: mu rhythm (8-13 Hz) and beta rhythm (14-25 Hz). The suppressed/enhanced SMR is a neural correlate of cortical activation related to efferent and afferent movement information. Therefore, it would be necessary to understand cortical information processing in diverse movement situations for clinical applications. Methods In this work, the EEG of 10 healthy volunteers was recorded while fingers were moved passively under different kinetic and kinematic conditions for proprioceptive stimulation. For the kinetics aspect, afferent brain activity (no simultaneous volition) was compared under two conditions of finger extension: (1) generated by an orthosis and (2) generated by the orthosis simultaneously combined and assisted with functional electrical stimulation (FES) applied at the forearm muscles related to finger extension. For the kinematic aspect, the finger extension was divided into two phases: (1) dynamic extension and (2) static extension (holding the extended position). Results In the kinematic aspect, both mu and beta rhythms were more suppressed during a dynamic than a static condition. However, only the mu rhythm showed a significant difference between kinetic conditions (with and without FES) affected by attention to proprioception after transitioning from dynamic to static state, but the beta rhythm was not. Discussion Our results indicate that mu rhythm was influenced considerably by muscle kinetics during finger movement produced by external devices, which has relevant implications for the design of neuromodulation and neurorehabilitation interventions.
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Affiliation(s)
- Woosang Cho
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
- *Correspondence: Woosang Cho,
| | - Carmen Vidaurre
- TECNALIA, Basque Research and Technology Alliance, Neurotechnology Laboratory, San Sebastián, Spain
- Ikerbasque-Basque Foundation for Science, Bilbao, Spain
| | - Jinung An
- Interdisciplinary Studies, Graduate School, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- San Camillo Hospital, Institute for Hospitalization and Scientific Care, Venice Lido, Italy
| | - Ander Ramos-Murguialday
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- TECNALIA, Basque Research and Technology Alliance, Neurotechnology Laboratory, San Sebastián, Spain
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Sadeghi Najafabadi M, Chen L, Dutta K, Norris A, Feng B, Schnupp JWH, Rosskothen-Kuhl N, Read HL, Escabí MA. Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces. Front Neurosci 2020; 14:709. [PMID: 32765212 PMCID: PMC7379342 DOI: 10.3389/fnins.2020.00709] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 06/11/2020] [Indexed: 11/13/2022] Open
Abstract
Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single- to multi-site stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, including in vitro sciatic nerve stimulation, bilateral cochlear implant stimulation, and multi-channel stimulation and recording between the auditory midbrain and cortex. We demonstrate a vast enhancement in the recording quality with a typical artifact reduction of 25-40 dB. The method is efficient and can be scaled to arbitrary number of stimulus and recording sites, making it ideal for applications in large-scale arrays, closed-loop implants, and high-resolution multi-channel brain-machine interfaces.
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Affiliation(s)
- Mina Sadeghi Najafabadi
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States
| | - Longtu Chen
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Kelsey Dutta
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States
| | - Ashley Norris
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Bin Feng
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Jan W. H. Schnupp
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
| | - Nicole Rosskothen-Kuhl
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong
- Neurobiological Research Laboratory, Section for Clinical and Experimental Otology, University Medical Center Freiburg, Freiburg im Breisgau, Germany
| | - Heather L. Read
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
- Department of Psychology, University of Connecticut, Storrs, CT, United States
- The Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, United States
| | - Monty A. Escabí
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
- Department of Psychology, University of Connecticut, Storrs, CT, United States
- The Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, United States
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Petkos K, Guiho T, Degenaar P, Jackson A, Brown P, Denison T, Drakakis EM. A high-performance 4 nV (√Hz) -1 analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation. J Neural Eng 2019; 16:066003. [PMID: 31151118 PMCID: PMC6877351 DOI: 10.1088/1741-2552/ab2610] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
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 ([Formula: see text])-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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Affiliation(s)
- Konstantinos Petkos
- Department of Bioengineering, Imperial College London, London, United Kingdom. Center for Neurotechnology, Imperial College London, London, United Kingdom
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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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López-Larraz E, Figueiredo TC, Insausti-Delgado A, Ziemann U, Birbaumer N, Ramos-Murguialday A. Event-related desynchronization during movement attempt and execution in severely paralyzed stroke patients: An artifact removal relevance analysis. Neuroimage Clin 2018; 20:972-986. [PMID: 30312940 PMCID: PMC6180341 DOI: 10.1016/j.nicl.2018.09.035] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 07/27/2018] [Accepted: 09/25/2018] [Indexed: 02/04/2023]
Abstract
The electroencephalogram (EEG) constitutes a relevant tool to study neural dynamics and to develop brain-machine interfaces (BMI) for rehabilitation of patients with paralysis due to stroke. However, the EEG is easily contaminated by artifacts of physiological origin, which can pollute the measured cortical activity and bias the interpretations of such data. This is especially relevant when recording EEG of stroke patients while they try to move their paretic limbs, since they generate more artifacts due to compensatory activity. In this paper, we study how physiological artifacts (i.e., eye movements, motion artifacts, muscle artifacts and compensatory movements with the other limb) can affect EEG activity of stroke patients. Data from 31 severely paralyzed stroke patients performing/attempting grasping movements with their healthy/paralyzed hand were analyzed offline. We estimated the cortical activation as the event-related desynchronization (ERD) of sensorimotor rhythms and used it to detect the movements with a pseudo-online simulated BMI. Automated state-of-the-art methods (linear regression to remove ocular contaminations and statistical thresholding to reject the other types of artifacts) were used to minimize the influence of artifacts. The effect of artifact reduction was quantified in terms of ERD and BMI performance. The results reveal a significant contamination affecting the EEG, being involuntary muscle activity the main source of artifacts. Artifact reduction helped extracting the oscillatory signatures of motor tasks, isolating relevant information from noise and revealing a more prominent ERD activity. Lower BMI performances were obtained when artifacts were eliminated from the training datasets. This suggests that artifacts produce an optimistic bias that improves theoretical accuracy but may result in a poor link between task-related oscillatory activity and BMI peripheral feedback. With a clinically relevant dataset of stroke patients, we evidence the need of appropriate methodologies to remove artifacts from EEG datasets to obtain accurate estimations of the motor brain activity.
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Affiliation(s)
- Eduardo López-Larraz
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany.
| | - Thiago C Figueiredo
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany
| | - Ainhoa Insausti-Delgado
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; International Max Planck Research School (IMPRS) for Cognitive and Systems Neuroscience, Tübingen, Germany; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Ulf Ziemann
- Department of Neurology & Stroke, and Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Wyss Institute for Bio- and Neuroengineering, Genève, Switzerland
| | - Ander Ramos-Murguialday
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Neural Engineering Laboratory, Health Department, TECNALIA, San Sebastián, Spain
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YOUNG D, WILLETT F, MEMBERG WD, MURPHY B, WALTER B, SWEET J, MILLER J, HOCHBERG LR, KIRSCH RF, AJIBOYE AB. Signal processing methods for reducing artifacts in microelectrode brain recordings caused by functional electrical stimulation. J Neural Eng 2018; 15:026014. [PMID: 29199642 PMCID: PMC5818316 DOI: 10.1088/1741-2552/aa9ee8] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Functional electrical stimulation (FES) is a promising technology for restoring movement to paralyzed limbs. Intracortical brain-computer interfaces (iBCIs) have enabled intuitive control over virtual and robotic movements, and more recently over upper extremity FES neuroprostheses. However, electrical stimulation of muscles creates artifacts in intracortical microelectrode recordings that could degrade iBCI performance. Here, we investigate methods for reducing the cortically recorded artifacts that result from peripheral electrical stimulation. APPROACH One participant in the BrainGate2 pilot clinical trial had two intracortical microelectrode arrays placed in the motor cortex, and thirty-six stimulating intramuscular electrodes placed in the muscles of the contralateral limb. We characterized intracortically recorded electrical artifacts during both intramuscular and surface stimulation. We compared the performance of three artifact reduction methods: blanking, common average reference (CAR) and linear regression reference (LRR), which creates channel-specific reference signals, composed of weighted sums of other channels. MAIN RESULTS Electrical artifacts resulting from surface stimulation were 175 × larger than baseline neural recordings (which were 110 µV peak-to-peak), while intramuscular stimulation artifacts were only 4 × larger. The artifact waveforms were highly consistent across electrodes within each array. Application of LRR reduced artifact magnitudes to less than 10 µV and largely preserved the original neural feature values used for decoding. Unmitigated stimulation artifacts decreased iBCI decoding performance, but performance was almost completely recovered using LRR, which outperformed CAR and blanking and extracted useful neural information during stimulation artifact periods. SIGNIFICANCE The LRR method was effective at reducing electrical artifacts resulting from both intramuscular and surface FES, and almost completely restored iBCI decoding performance (>90% recovery for surface stimulation and full recovery for intramuscular stimulation). The results demonstrate that FES-induced artifacts can be easily mitigated in FES + iBCI systems by using LRR for artifact reduction, and suggest that the LRR method may also be useful in other noise reduction applications.
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Affiliation(s)
- D. YOUNG
- Case Western Reserve Univ., Cleveland, OH
- FES Ctr. of Excellence, Rehab. R&D Service, Louis Stokes Cleveland Dept. of VA Med. Ctr., Cleveland, OH
| | - F. WILLETT
- Case Western Reserve Univ., Cleveland, OH
- FES Ctr. of Excellence, Rehab. R&D Service, Louis Stokes Cleveland Dept. of VA Med. Ctr., Cleveland, OH
- Dept of Neurosurgery, Stanford University, Stanford, CA
| | - W. D. MEMBERG
- Case Western Reserve Univ., Cleveland, OH
- FES Ctr. of Excellence, Rehab. R&D Service, Louis Stokes Cleveland Dept. of VA Med. Ctr., Cleveland, OH
| | - B. MURPHY
- Case Western Reserve Univ., Cleveland, OH
| | - B. WALTER
- Neurol., UH Cleveland Med. Ctr., Cleveland, OH
- Neurol., CWRU Sch. of Med., Cleveland, OH
| | - J. SWEET
- Neurosurg., UH Cleveland Med. Ctr., Cleveland, OH
- Neurolog. Surgery, CWRU Sch. of Med., Cleveland, OH
| | - J. MILLER
- Neurosurg., UH Cleveland Med. Ctr., Cleveland, OH
- Neurolog. Surgery, CWRU Sch. of Med., Cleveland, OH
| | - L. R. HOCHBERG
- Ctr. for Neurorestoration and Neurotechnology, Rehab. R&D Service, Dept. of VA Medical Center, Providence RI
- Sch. of Engin., Brown Univ., Providence, RI
- Neurol., Massachusetts Gen. Hosp., Boston, MA
- Neurol., Harvard Med. Sch., Boston, MA
- Inst. For Brain Sci., Brown Univ., Providence, RI
| | - R. F. KIRSCH
- Case Western Reserve Univ., Cleveland, OH
- FES Ctr. of Excellence, Rehab. R&D Service, Louis Stokes Cleveland Dept. of VA Med. Ctr., Cleveland, OH
| | - A. B. AJIBOYE
- Case Western Reserve Univ., Cleveland, OH
- FES Ctr. of Excellence, Rehab. R&D Service, Louis Stokes Cleveland Dept. of VA Med. Ctr., Cleveland, OH
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