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Hofmann UG, Stieglitz T. Why some BCI should still be called BMI. Nat Commun 2024; 15:6207. [PMID: 39043690 PMCID: PMC11266610 DOI: 10.1038/s41467-024-50603-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Accepted: 07/15/2024] [Indexed: 07/25/2024] Open
Affiliation(s)
- Ulrich G Hofmann
- Section for Neuroelectronic Systems, Department of Neurosurgery, Medical Center University of Freiburg and Medical Faculty, University of Freiburg, Freiburg, Germany.
- BrainLinks-BrainTools, Freiburg, Germany.
| | - Thomas Stieglitz
- BrainLinks-BrainTools, Freiburg, Germany.
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, University of Freiburg, Freiburg, Germany.
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Kılınç Bülbül D, Güçlü B. Predicting lever press in a vibrotactile yes/no detection task from S1 cortex of freely behaving rats by µECoG arrays. Somatosens Mot Res 2024:1-8. [PMID: 38812257 DOI: 10.1080/08990220.2024.2358522] [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: 03/04/2024] [Accepted: 05/18/2024] [Indexed: 05/31/2024]
Abstract
AIM OF THE STUDY Brain-computer interfaces (BCIs) may help patients with severe neurological deficits communicate with the external world. Based on microelectrocorticography (µECoG) data recorded from the primary somatosensory cortex (S1) of unrestrained behaving rats, this study attempts to decode lever presses in a psychophysical detection task by using machine learning algorithms. MATERIALS AND METHODS 16-channel Pt-Ir microelectrode arrays were implanted on the S1 of two rats, and µECoG was recorded during a vibrotactile yes/no detection task. For this task, the rats were trained to press the right lever when they detected the vibrotactile stimulus and the left lever when they did not. The multichannel µECoG data was analysed offline by time-frequency methods and its features were used for binary classification of the lever press at each trial. Several machine learning algorithms were tested as such. RESULTS The psychophysical sensitivities (A') were similar and low for both rats (0.58). Rat 2 (B'': -0.11) had higher bias for the right lever than Rat 1 (B'': - 0.01). The lever presses could be predicted with accuracies over 66% with all the tested algorithms, and the highest average accuracy (78%) was with the support vector machine. CONCLUSION According to the recent studies, sensory feedback increases the benefit of the BCIs. The current proof-of-concept study shows that lever presses can be decoded from the S1; therefore, this area may be utilised for a bidirectional BCI in the future.
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Affiliation(s)
| | - Burak Güçlü
- Institute of Biomedical Engineering, Boğaziçi University, İstanbul, Turkey
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3
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McNamara IN, Wellman SM, Li L, Eles JR, Savya S, Sohal HS, Angle MR, Kozai TDY. Electrode sharpness and insertion speed reduce tissue damage near high-density penetrating arrays. J Neural Eng 2024; 21:026030. [PMID: 38518365 DOI: 10.1088/1741-2552/ad36e1] [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: 11/22/2023] [Accepted: 03/22/2024] [Indexed: 03/24/2024]
Abstract
Objective. Over the past decade, neural electrodes have played a crucial role in bridging biological tissues with electronic and robotic devices. This study focuses on evaluating the optimal tip profile and insertion speed for effectively implanting Paradromics' high-density fine microwire arrays (FμA) prototypes into the primary visual cortex (V1) of mice and rats, addressing the challenges associated with the 'bed-of-nails' effect and tissue dimpling.Approach. Tissue response was assessed by investigating the impact of electrodes on the blood-brain barrier (BBB) and cellular damage, with a specific emphasis on tailored insertion strategies to minimize tissue disruption during electrode implantation.Main results.Electro-sharpened arrays demonstrated a marked reduction in cellular damage within 50μm of the electrode tip compared to blunt and angled arrays. Histological analysis revealed that slow insertion speeds led to greater BBB compromise than fast and pneumatic methods. Successful single-unit recordings validated the efficacy of the optimized electro-sharpened arrays in capturing neural activity.Significance.These findings underscore the critical role of tailored insertion strategies in minimizing tissue damage during electrode implantation, highlighting the suitability of electro-sharpened arrays for long-term implant applications. This research contributes to a deeper understanding of the complexities associated with high-channel-count microelectrode array implantation, emphasizing the importance of meticulous assessment and optimization of key parameters for effective integration and minimal tissue disruption. By elucidating the interplay between insertion parameters and tissue response, our study lays a strong foundation for the development of advanced implantable devices with a reduction in reactive gliosis and improved performance in neural recording applications.
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Affiliation(s)
- Ingrid N McNamara
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Steven M Wellman
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Lehong Li
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - James R Eles
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Sajishnu Savya
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
| | | | | | - Takashi D Y Kozai
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
- Center of the Basis of Neural Cognition, Pittsburgh, PA, United States of America
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
- NeuroTech Center, University of Pittsburgh Brain Institute, Pittsburgh, PA, United States of America
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4
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Ahmadipour P, Sani OG, Pesaran B, Shanechi MM. Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity. J Neural Eng 2024; 21:026001. [PMID: 38016450 PMCID: PMC10913727 DOI: 10.1088/1741-2552/ad1053] [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: 06/02/2023] [Revised: 10/23/2023] [Accepted: 11/28/2023] [Indexed: 11/30/2023]
Abstract
Objective.Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales.Approach.Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical SID method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and with spiking and local field potential population activity recorded during a naturalistic reach and grasp behavior.Main results.We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior.Significance.Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.
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Affiliation(s)
- Parima Ahmadipour
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Omid G Sani
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Bijan Pesaran
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Thomas Lord Department of Computer Science, Alfred E. Mann Department of Biomedical Engineering, and the Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America
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Brannigan JFM, Fry A, Opie NL, Campbell BCV, Mitchell PJ, Oxley TJ. Endovascular Brain-Computer Interfaces in Poststroke Paralysis. Stroke 2024; 55:474-483. [PMID: 38018832 DOI: 10.1161/strokeaha.123.037719] [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] [Indexed: 11/30/2023]
Abstract
Stroke is a leading cause of paralysis, most frequently affecting the upper limbs and vocal folds. Despite recent advances in care, stroke recovery invariably reaches a plateau, after which there are permanent neurological impairments. Implantable brain-computer interface devices offer the potential to bypass permanent neurological lesions. They function by (1) recording neural activity, (2) decoding the neural signal occurring in response to volitional motor intentions, and (3) generating digital control signals that may be used to control external devices. While brain-computer interface technology has the potential to revolutionize neurological care, clinical translation has been limited. Endovascular arrays present a novel form of minimally invasive brain-computer interface devices that have been deployed in human subjects during early feasibility studies. This article provides an overview of endovascular brain-computer interface devices and critically evaluates the patient with stroke as an implant candidate. Future opportunities are mapped, along with the challenges arising when decoding neural activity following infarction. Limitations arise when considering intracerebral hemorrhage and motor cortex lesions; however, future directions are outlined that aim to address these challenges.
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Affiliation(s)
- Jamie F M Brannigan
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom (J.F.M.B.)
| | - Adam Fry
- Synchron, Inc, New York, NY (A.F., N.L.O., T.J.O.)
| | - Nicholas L Opie
- Synchron, Inc, New York, NY (A.F., N.L.O., T.J.O.)
- Vascular Bionics Laboratory, Department of Medicine, The University of Melbourne, Victoria, Australia (N.L.O., T.J.O.)
| | - Bruce C V Campbell
- Department of Neurology (B.C.V.C.), The Royal Melbourne Hospital, The University of Melbourne, Parkville, Australia
- Melbourne Brain Centre (B.C.V.C.), The Royal Melbourne Hospital, The University of Melbourne, Parkville, Australia
| | - Peter J Mitchell
- Department of Radiology (P.J.M.), The Royal Melbourne Hospital, The University of Melbourne, Parkville, Australia
| | - Thomas J Oxley
- Synchron, Inc, New York, NY (A.F., N.L.O., T.J.O.)
- Vascular Bionics Laboratory, Department of Medicine, The University of Melbourne, Victoria, Australia (N.L.O., T.J.O.)
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Kancheva I, van der Salm SMA, Ramsey NF, Vansteensel MJ. Association between lesion location and sensorimotor rhythms in stroke - a systematic review with narrative synthesis. Neurol Sci 2023; 44:4263-4289. [PMID: 37606742 PMCID: PMC10641054 DOI: 10.1007/s10072-023-06982-8] [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: 11/02/2022] [Accepted: 07/26/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND Stroke causes alterations in the sensorimotor rhythms (SMRs) of the brain. However, little is known about the influence of lesion location on the SMRs. Understanding this relationship is relevant for the use of SMRs in assistive and rehabilitative therapies, such as Brain-Computer Interfaces (BCIs).. METHODS We reviewed current evidence on the association between stroke lesion location and SMRs through systematically searching PubMed and Embase and generated a narrative synthesis of findings. RESULTS We included 12 articles reporting on 161 patients. In resting-state studies, cortical and pontine damage were related to an overall decrease in alpha (∼8-12 Hz) and increase in delta (∼1-4 Hz) power. In movement paradigm studies, attenuated alpha and beta (∼15-25 Hz) event-related desynchronization (ERD) was shown in stroke patients during (attempted) paretic hand movement, compared to controls. Stronger reductions in alpha and beta ERD in the ipsilesional, compared to contralesional hemisphere, were observed for cortical lesions. Subcortical stroke was found to affect bilateral ERD and ERS, but results were highly variable. CONCLUSIONS Findings suggest a link between stroke lesion location and SMR alterations, but heterogeneity across studies and limited lesion location descriptions precluded a meta-analysis. SIGNIFICANCE Future research would benefit from more uniformly defined outcome measures, homogeneous methodologies, and improved lesion location reporting.
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Affiliation(s)
- Ivana Kancheva
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, P.O. Box 85060, 3508 AB, Utrecht, The Netherlands
| | - Sandra M A van der Salm
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, P.O. Box 85060, 3508 AB, Utrecht, The Netherlands
| | - Nick F Ramsey
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, P.O. Box 85060, 3508 AB, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, P.O. Box 85060, 3508 AB, Utrecht, The Netherlands.
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Canny E, Vansteensel MJ, van der Salm SMA, Müller-Putz GR, Berezutskaya J. Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome. J Neuroeng Rehabil 2023; 20:157. [PMID: 37980536 PMCID: PMC10656959 DOI: 10.1186/s12984-023-01272-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/23/2023] [Indexed: 11/20/2023] Open
Abstract
Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals.
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Affiliation(s)
- Evan Canny
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sandra M A van der Salm
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gernot R Müller-Putz
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
| | - Julia Berezutskaya
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
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Wang X, Santos VJ. Gaze-Based Shared Autonomy Framework With Real-Time Action Primitive Recognition for Robot Manipulators. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4306-4317. [PMID: 37906485 DOI: 10.1109/tnsre.2023.3328888] [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: 11/02/2023]
Abstract
Robots capable of robust, real-time recognition of human intent during manipulation tasks could be used to enhance human-robot collaboration for activities of daily living. Eye gaze-based control interfaces offer a non-invasive way to infer intent and reduce the cognitive burden on operators of complex robots. Eye gaze is traditionally used for "gaze triggering" (GT) in which staring at an object, or sequence of objects, triggers pre-programmed robotic movements. We propose an alternative approach: a neural network-based "action prediction" (AP) mode that extracts gaze-related features to recognize, and often predict, an operator's intended action primitives. We integrated the AP mode into a shared autonomy framework capable of 3D gaze reconstruction, real-time intent inference, object localization, obstacle avoidance, and dynamic trajectory planning. Using this framework, we conducted a user study to directly compare the performance of the GT and AP modes using traditional subjective performance metrics, such as Likert scales, as well as novel objective performance metrics, such as the delay of recognition. Statistical analyses suggested that the AP mode resulted in more seamless robotic movement than the state-of-the-art GT mode, and that participants generally preferred the AP mode.
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Crone N, Candrea D, Shah S, Luo S, Angrick M, Rabbani Q, Coogan C, Milsap G, Nathan K, Wester B, Anderson W, Rosenblatt K, Clawson L, Maragakis N, Vansteensel M, Tenore F, Ramsey N, Fifer M, Uchil A. A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling. RESEARCH SQUARE 2023:rs.3.rs-3158792. [PMID: 37841873 PMCID: PMC10571601 DOI: 10.21203/rs.3.rs-3158792/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Background Brain-computer interfaces (BCIs) can restore communication in movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command "click" decoders provide a basic yet highly functional capability. Methods We sought to test the performance and long-term stability of click-decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis (ALS). We trained the participant's click decoder using a small amount of training data (< 44 minutes across four days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating. Results Using this click decoder to navigate a switch-scanning spelling interface, the study participant was able to maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation interrupted testing with this fixed model, a new click decoder achieved comparable performance despite being trained with even less data (< 15 min, within one day). Conclusion These results demonstrate that a click decoder can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.
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Lim J, Wang PT, Bashford L, Kellis S, Shaw SJ, Gong H, Armacost M, Heydari P, Do AH, Andersen RA, Liu CY, Nenadic Z. Suppression of cortical electrostimulation artifacts using pre-whitening and null projection. J Neural Eng 2023; 20:056018. [PMID: 37666246 DOI: 10.1088/1741-2552/acf68b] [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: 04/25/2023] [Accepted: 09/04/2023] [Indexed: 09/06/2023]
Abstract
Objective.Invasive brain-computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods.Approach.We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects.Main results.In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%-80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement.Significance.PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.
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Affiliation(s)
- Jeffrey Lim
- Department of Biomedical Engineering, University of California Irvine (UCI), Irvine, CA 92697, United States of America
| | - Po T Wang
- Department of Biomedical Engineering, University of California Irvine (UCI), Irvine, CA 92697, United States of America
| | - Luke Bashford
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States of America
| | - Spencer Kellis
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States of America
- Department of Neurological Surgery, Keck School of Medicine of University of Southern California (USC), Los Angeles, CA 90033, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA 90033, United States of America
| | - Susan J Shaw
- Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, United States of America
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA 90033, United States of America
| | - Hui Gong
- Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, United States of America
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA 90033, United States of America
| | - Michelle Armacost
- Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, United States of America
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA 90033, United States of America
| | - Payam Heydari
- Department of Biomedical Engineering, University of California Irvine (UCI), Irvine, CA 92697, United States of America
- Department of Electrical Engineering and Computer Science, UCI, Irvine, CA 92697, United States of America
| | - An H Do
- Department of Neurology, UCI, Irvine, CA 92697, United States of America
| | - Richard A Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States of America
| | - Charles Y Liu
- Department of Neurological Surgery, Keck School of Medicine of University of Southern California (USC), Los Angeles, CA 90033, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA 90033, United States of America
- Department of Neurosurgery, Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, United States of America
| | - Zoran Nenadic
- Department of Biomedical Engineering, University of California Irvine (UCI), Irvine, CA 92697, United States of America
- Department of Electrical Engineering and Computer Science, UCI, Irvine, CA 92697, United States of America
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Natraj N, Seko S, Abiri R, Yan H, Graham Y, Tu-Chan A, Chang EF, Ganguly K. Flexible regulation of representations on a drifting manifold enables long-term stable complex neuroprosthetic control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.11.551770. [PMID: 37645922 PMCID: PMC10462094 DOI: 10.1101/2023.08.11.551770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
The nervous system needs to balance the stability of neural representations with plasticity. It is unclear what is the representational stability of simple actions, particularly those that are well-rehearsed in humans, and how it changes in new contexts. Using an electrocorticography brain-computer interface (BCI), we found that the mesoscale manifold and relative representational distances for a repertoire of simple imagined movements were remarkably stable. Interestingly, however, the manifold's absolute location demonstrated day-to-day drift. Strikingly, representational statistics, especially variance, could be flexibly regulated to increase discernability during BCI control without somatotopic changes. Discernability strengthened with practice and was specific to the BCI, demonstrating remarkable contextual specificity. Accounting for drift, and leveraging the flexibility of representations, allowed neuroprosthetic control of a robotic arm and hand for over 7 months without recalibration. Our study offers insight into how electrocorticography can both track representational statistics across long periods and allow long-term complex neuroprosthetic control.
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Affiliation(s)
- Nikhilesh Natraj
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
| | - Sarah Seko
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
| | - Reza Abiri
- Electrical, Computer and Biomedical Engineering, University of Rhode Island, Rhode Island, USA
| | - Hongyi Yan
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
| | - Yasmin Graham
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
| | - Adelyn Tu-Chan
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
| | - Edward F Chang
- Department of Neurological Surgery, Weill Institute for Neuroscience, University of California-San Francisco, San Francisco, California, USA
| | - Karunesh Ganguly
- Dept. of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
- UCSF - Veteran Affairs Medical Center, San Francisco, California, USA
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Ahmadipour P, Sani OG, Pesaran B, Shanechi MM. Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.26.542509. [PMID: 37398400 PMCID: PMC10312539 DOI: 10.1101/2023.05.26.542509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower computational cost while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest.
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Guan C, Aflalo T, Kadlec K, Gámez de Leon J, Rosario ER, Bari A, Pouratian N, Andersen RA. Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex. J Neural Eng 2023; 20:036020. [PMID: 37160127 PMCID: PMC10209510 DOI: 10.1088/1741-2552/acd3b1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/24/2023] [Accepted: 05/09/2023] [Indexed: 05/11/2023]
Abstract
Objective. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis.Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified attempted finger movements from neural firing rates using linear discriminant analysis with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain-machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands.Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). In MC and in one PPC array, a factorized code linked corresponding finger movements of the contralateral and ipsilateral hands.Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.
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Affiliation(s)
- Charles Guan
- California Institute of Technology, Pasadena, CA, United States of America
| | - Tyson Aflalo
- California Institute of Technology, Pasadena, CA, United States of America
- T&C Chen Brain-Machine Interface Center at Caltech, Pasadena, CA, United States of America
| | - Kelly Kadlec
- California Institute of Technology, Pasadena, CA, United States of America
| | | | - Emily R Rosario
- Casa Colina Hospital and Centers for Healthcare, Pomona, CA, United States of America
| | - Ausaf Bari
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States of America
| | - Nader Pouratian
- University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Richard A Andersen
- California Institute of Technology, Pasadena, CA, United States of America
- T&C Chen Brain-Machine Interface Center at Caltech, Pasadena, CA, United States of America
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14
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Song S, Fallegger F, Trouillet A, Kim K, Lacour SP. Deployment of an electrocorticography system with a soft robotic actuator. Sci Robot 2023; 8:eadd1002. [PMID: 37163609 DOI: 10.1126/scirobotics.add1002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epileptogenic regions of the brain and facilitate lesion resection surgery and increasingly explored in brain-machine interface applications. Current devices display limitations that require trade-offs among cortical surface coverage, spatial electrode resolution, aesthetic, and risk consequences and often limit the use of the mapping technology to the operating room. In this work, we report on a scalable technique for the fabrication of large-area soft robotic electrode arrays and their deployment on the cortex through a square-centimeter burr hole using a pressure-driven actuation mechanism called eversion. The deployable system consists of up to six prefolded soft legs, and it is placed subdurally on the cortex using an aqueous pressurized solution and secured to the pedestal on the rim of the small craniotomy. Each leg contains soft, microfabricated electrodes and strain sensors for real-time deployment monitoring. In a proof-of-concept acute surgery, a soft robotic electrode array was successfully deployed on the cortex of a minipig to record sensory cortical activity. This soft robotic neurotechnology opens promising avenues for minimally invasive cortical surgery and applications related to neurological disorders such as motor and sensory deficits.
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Affiliation(s)
- Sukho Song
- Laboratory for Soft Bioelectronic Interfaces, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland
- Laboratory of Sustainability Robotics, Swiss Federal Laboratories for Materials Science and Technology (Empa), 8600 Dübendorf, Switzerland
| | - Florian Fallegger
- Laboratory for Soft Bioelectronic Interfaces, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland
| | - Alix Trouillet
- Laboratory for Soft Bioelectronic Interfaces, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland
| | - Kyungjin Kim
- Laboratory for Soft Bioelectronic Interfaces, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Stéphanie P Lacour
- Laboratory for Soft Bioelectronic Interfaces, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland
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15
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Branco MP, Geukes SH, Aarnoutse EJ, Ramsey NF, Vansteensel MJ. Nine decades of electrocorticography: A comparison between epidural and subdural recordings. Eur J Neurosci 2023; 57:1260-1288. [PMID: 36843389 DOI: 10.1111/ejn.15941] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 02/10/2023] [Accepted: 02/18/2023] [Indexed: 02/28/2023]
Abstract
In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.
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Affiliation(s)
- Mariana P Branco
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Simon H Geukes
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
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16
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Rouhi S, Rahmani S, Shanesazzadeh F, Ahmadvand T, Namazi M, Fardmanesh M, Kiani S. Stimulation of spinal cord according to recorded theta hippocampal rhythm during rat move on treadmill. BIOMED ENG-BIOMED TE 2023:bmt-2022-0420. [PMID: 36872631 DOI: 10.1515/bmt-2022-0420] [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: 10/31/2022] [Accepted: 02/20/2023] [Indexed: 03/07/2023]
Abstract
OBJECTIVES Several studies have revealed that after spinal cord injury (SCI), in acute and sub-acute phase the spinal cord neurons below the injury are alive and could stimulate by use of electrical pulses. Spinal cord electrical stimulation could generate movement for paralyzed limbs and is a rehabilitation strategy for paralyzed patients. An innovative idea for controlling spinal cord electrical stimulation onset time is presented in current study. METHODS In our method, the time of applying electrical pulse on the spinal cord is according to rat behavioral movement and two movements behaviors are recognized only based on rat EEG theta rhythm on the treadmill line. Briefly, 5 rats were placed on the treadmill and the animals experienced zero or 12 m/min speeds. RESULTS These speeds were recognized based on EEG signals and off-line periodogram analysis. Finally, the electrical stimulation pulses had been applied to the spinal cord if the results of the EEG analysis had detected running behavior. CONCLUSIONS These findings may guide future research in utilizing theta rhythms for the recognition of animal motor behavior and designing electrical stimulation systems based on it.
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Affiliation(s)
- Shahin Rouhi
- Department of Developmental Biology, University of Science and Culture, ACECR, Tehran, Iran.,Department of Stem Cell and Developmental Biology, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.,Department of Brain and Cognitive Science, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Saeid Rahmani
- Institute for Research in Fundamental Science (IPM), Tehran, Iran
| | | | - Tala Ahmadvand
- School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Mahrokh Namazi
- School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Mehdi Fardmanesh
- School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Sahar Kiani
- Department of Stem Cell and Developmental Biology, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.,Department of Brain and Cognitive Science, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
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17
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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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18
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Towards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations. J Neurol 2023; 270:1323-1336. [PMID: 36450968 PMCID: PMC9971103 DOI: 10.1007/s00415-022-11464-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 12/05/2022]
Abstract
Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain-computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease, among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs.
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19
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Song CY, Hsieh HL, Pesaran B, Shanechi MM. Modeling and inference methods for switching regime-dependent dynamical systems with multiscale neural observations. J Neural Eng 2022; 19. [PMID: 36261030 DOI: 10.1088/1741-2552/ac9b94] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 10/19/2022] [Indexed: 01/11/2023]
Abstract
Objective.Realizing neurotechnologies that enable long-term neural recordings across multiple spatial-temporal scales during naturalistic behaviors requires new modeling and inference methods that can simultaneously address two challenges. First, the methods should aggregate information across all activity scales from multiple recording sources such as spiking and field potentials. Second, the methods should detect changes in the regimes of behavior and/or neural dynamics during naturalistic scenarios and long-term recordings. Prior regime detection methods are developed for a single scale of activity rather than multiscale activity, and prior multiscale methods have not considered regime switching and are for stationary cases.Approach.Here, we address both challenges by developing a switching multiscale dynamical system model and the associated filtering and smoothing methods. This model describes the encoding of an unobserved brain state in multiscale spike-field activity. It also allows for regime-switching dynamics using an unobserved regime state that dictates the dynamical and encoding parameters at every time-step. We also design the associated switching multiscale inference methods that estimate both the unobserved regime and brain states from simultaneous spike-field activity.Main results.We validate the methods in both extensive numerical simulations and prefrontal spike-field data recorded in a monkey performing saccades for fluid rewards. We show that these methods can successfully combine the spiking and field potential observations to simultaneously track the regime and brain states accurately. Doing so, these methods lead to better state estimation compared with single-scale switching methods or stationary multiscale methods. Also, for single-scale linear Gaussian observations, the new switching smoother can better generalize to diverse system settings compared to prior switching smoothers.Significance.These modeling and inference methods effectively incorporate both regime-detection and multiscale observations. As such, they could facilitate investigation of latent switching neural population dynamics and improve future brain-machine interfaces by enabling inference in naturalistic scenarios where regime-dependent multiscale activity and behavior arise.
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Affiliation(s)
- Christian Y Song
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Han-Lin Hsieh
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Bijan Pesaran
- Departments of Neurosurgery, Neuroscience, and Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America.,Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America.,Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America.,Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
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20
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Tonin L, Perdikis S, Kuzu TD, Pardo J, Orset B, Lee K, Aach M, Schildhauer TA, Martínez-Olivera R, Millán JDR. Learning to control a BMI-driven wheelchair for people with severe tetraplegia. iScience 2022; 25:105418. [PMID: 36590466 PMCID: PMC9801246 DOI: 10.1016/j.isci.2022.105418] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/14/2022] [Accepted: 10/18/2022] [Indexed: 11/19/2022] Open
Abstract
Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.
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Affiliation(s)
- Luca Tonin
- Department of Information Engineering, University of Padova, Padova, Italy,Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Serafeim Perdikis
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Taylan Deniz Kuzu
- Klinik für Neurochirurgie und Wirbelsäulenchirurgie, Universitätsklinikum Bergmannsheil Bochum, Ruhr-Universität Bochum, Bochum, Germany
| | - Jorge Pardo
- Klinik für Neurochirurgie und Wirbelsäulenchirurgie, Universitätsklinikum Bergmannsheil Bochum, Ruhr-Universität Bochum, Bochum, Germany
| | - Bastien Orset
- École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Kyuhwa Lee
- Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
| | - Mirko Aach
- Chirurgische Universitätsklinik und Poliklinik, Universitätsklinikum Bergmannsheil Bochum, Ruhr-Universität Bochum, Bochum, Germany
| | - Thomas Armin Schildhauer
- Chirurgische Universitätsklinik und Poliklinik, Universitätsklinikum Bergmannsheil Bochum, Ruhr-Universität Bochum, Bochum, Germany
| | - Ramón Martínez-Olivera
- Klinik für Neurochirurgie und Wirbelsäulenchirurgie, Universitätsklinikum Bergmannsheil Bochum, Ruhr-Universität Bochum, Bochum, Germany
| | - José del R. Millán
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA,Department of Neurology, The University of Texas at Austin, Austin, TX, USA,Corresponding author
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21
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Faes A, Hulle MMV. Finger movement and coactivation predicted from intracranial brain activity using extended block-term tensor regression. J Neural Eng 2022; 19. [PMID: 36240727 DOI: 10.1088/1741-2552/ac9a75] [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: 07/06/2022] [Accepted: 10/14/2022] [Indexed: 01/11/2023]
Abstract
Objective.We introduce extended Block-Term Tensor Regression (eBTTR), a novel regression method designed to account for the multilinear nature of human intracranial finger movement recordings.Approach.The proposed method relies on recursive Tucker decomposition combined with automatic component extraction.Main results.eBTTR outperforms state-of-the-art regression approaches, including multilinear and deep learning ones, in accurately predicting finger trajectories as well as unintentional finger coactivations.Significance.eBTTR rivals state-of-the-art approaches while being less computationally expensive which is an advantage when intracranial electrodes are implanted acutely, as part of the patient's presurgical workup, limiting time for decoder development and testing.
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Affiliation(s)
- A Faes
- Department of Neurosciences, Laboratory for Neuro- & Psychophysiology, KU Leuven-University of Leuven, B-3000 Leuven, Belgium
| | - M M Van Hulle
- Department of Neurosciences, Laboratory for Neuro- & Psychophysiology, KU Leuven-University of Leuven, B-3000 Leuven, Belgium
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22
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Readioff R, Siddiqui ZK, Stewart C, Fulbrook L, O’Connor RJ, Chadwick EK. Use and evaluation of assistive technologies for upper limb function in tetraplegia. J Spinal Cord Med 2022; 45:809-820. [PMID: 33606599 PMCID: PMC9662059 DOI: 10.1080/10790268.2021.1878342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
CONTEXT More than half of all spinal cord injuries (SCI) occur at the cervical level leading to loss of upper limb function, restricted activity and reduced independence. Several technologies have been developed to assist with upper limb functions in the SCI population. OBJECTIVE There is no clear clinical consensus on the effectiveness of the current assistive technologies for the cervical SCI population, hence this study reviews the literature in the years between 1999 and 2019. METHODS A systematic review was performed on the state-of-the-art assistive technology that supports and improves the function of impaired upper limbs in cervical SCI populations. Combinations of terms, covering assistive technology, SCI, and upper limb, were used in the search, which resulted in a total of 1770 articles. Data extractions were performed on the selected studies which involved summarizing details on the assistive technologies, characteristics of study participants, outcome measures, and improved upper limb functions when using the device. RESULTS A total of 24 articles were found and grouped into five categories, including neuroprostheses (invasive and non-invasive), orthotic devices, hybrid systems, robots, and arm supports. Only a few selected studies comprehensively reported characteristics of the participants. There was a wide range of outcome measures and all studies reported improvements in upper limb function with the devices. CONCLUSIONS This study highlighted that assistive technologies can improve functions of the upper limbs in SCI patients. It was challenging to draw generalizable conclusions because of factors, such as heterogeneity of recruited participants, a wide range of outcome measures, and the different technologies employed.
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Affiliation(s)
- Rosti Readioff
- School of Pharmacy and Bioengineering, Keele University, Stoke-on-Trent, UK,Correspondence to: Rosti Readioff, Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, LeedsLS2 9JT, UK. ; @Dr_Rosti
| | - Zaha Kamran Siddiqui
- Academic Department of Rehabilitation Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | - Caroline Stewart
- School of Pharmacy and Bioengineering, Keele University, Stoke-on-Trent, UK,The Orthotic Research and Locomotor Assessment Unit (ORLAU), the Robert Jones and Agnes Hunt Orthopaedic Hospital, NHS Foundation Trust, Oswestry, UK
| | - Louisa Fulbrook
- The Orthotic Research and Locomotor Assessment Unit (ORLAU), the Robert Jones and Agnes Hunt Orthopaedic Hospital, NHS Foundation Trust, Oswestry, UK
| | - Rory J. O’Connor
- Academic Department of Rehabilitation Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, UK
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23
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Lim J, Wang PT, Shaw SJ, Gong H, Armacost M, Liu CY, Do AH, Heydari P, Nenadic Z. Artifact propagation in subdural cortical electrostimulation: Characterization and modeling. Front Neurosci 2022; 16:1021097. [PMID: 36312030 PMCID: PMC9596776 DOI: 10.3389/fnins.2022.1021097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
Cortical stimulation via electrocorticography (ECoG) may be an effective method for inducing artificial sensation in bi-directional brain-computer interfaces (BD-BCIs). However, strong electrical artifacts caused by electrostimulation may significantly degrade or obscure neural information. A detailed understanding of stimulation artifact propagation through relevant tissues may improve existing artifact suppression techniques or inspire the development of novel artifact mitigation strategies. Our work thus seeks to comprehensively characterize and model the propagation of artifacts in subdural ECoG stimulation. To this end, we collected and analyzed data from eloquent cortex mapping procedures of four subjects with epilepsy who were implanted with subdural ECoG electrodes. From this data, we observed that artifacts exhibited phase-locking and ratcheting characteristics in the time domain across all subjects. In the frequency domain, stimulation caused broadband power increases, as well as power bursts at the fundamental stimulation frequency and its super-harmonics. The spatial distribution of artifacts followed the potential distribution of an electric dipole with a median goodness-of-fit of R2 = 0.80 across all subjects and stimulation channels. Artifacts as large as ±1,100 μV appeared anywhere from 4.43 to 38.34 mm from the stimulation channel. These temporal, spectral and spatial characteristics can be utilized to improve existing artifact suppression techniques, inspire new strategies for artifact mitigation, and aid in the development of novel cortical stimulation protocols. Taken together, these findings deepen our understanding of cortical electrostimulation and provide critical design specifications for future BD-BCI systems.
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Affiliation(s)
- Jeffrey Lim
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- *Correspondence: Jeffrey Lim
| | - Po T. Wang
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Susan J. Shaw
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
| | - Hui Gong
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
| | - Michelle Armacost
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
| | - Charles Y. Liu
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
| | - An H. Do
- Department of Neurology, University of California, Irvine, Irvine, CA, United States
| | - Payam Heydari
- 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
| | - 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
- Zoran Nenadic
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24
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Tringides CM, Mooney DJ. Materials for Implantable Surface Electrode Arrays: Current Status and Future Directions. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2107207. [PMID: 34716730 DOI: 10.1002/adma.202107207] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/26/2021] [Indexed: 06/13/2023]
Abstract
Surface electrode arrays are mainly fabricated from rigid or elastic materials, and precisely manipulated ductile metal films, which offer limited stretchability. However, the living tissues to which they are applied are nonlinear viscoelastic materials, which can undergo significant mechanical deformation in dynamic biological environments. Further, the same arrays and compositions are often repurposed for vastly different tissues rather than optimizing the materials and mechanical properties of the implant for the target application. By first characterizing the desired biological environment, and then designing a technology for a particular organ, surface electrode arrays may be more conformable, and offer better interfaces to tissues while causing less damage. Here, the various materials used in each component of a surface electrode array are first reviewed, and then electrically active implants in three specific biological systems, the nervous system, the muscular system, and skin, are described. Finally, the fabrication of next-generation surface arrays that overcome current limitations is discussed.
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Affiliation(s)
- Christina M Tringides
- Harvard Program in Biophysics, Harvard University, Cambridge, MA, 02138, USA
- Harvard-MIT Division in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, 02138, USA
| | - David J Mooney
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, 02138, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
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25
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Pandarinath C, Bensmaia SJ. The science and engineering behind sensitized brain-controlled bionic hands. Physiol Rev 2022; 102:551-604. [PMID: 34541898 PMCID: PMC8742729 DOI: 10.1152/physrev.00034.2020] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/13/2022] Open
Abstract
Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.
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Affiliation(s)
- Chethan Pandarinath
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
- Department of Neurosurgery, Emory University, Atlanta, Georgia
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, Illinois
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26
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Srisrisawang N, Müller-Putz GR. Applying Dimensionality Reduction Techniques in Source-Space Electroencephalography via Template and Magnetic Resonance Imaging-Derived Head Models to Continuously Decode Hand Trajectories. Front Hum Neurosci 2022; 16:830221. [PMID: 35399364 PMCID: PMC8988304 DOI: 10.3389/fnhum.2022.830221] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 02/28/2022] [Indexed: 11/13/2022] Open
Abstract
Several studies showed evidence supporting the possibility of hand trajectory decoding from low-frequency electroencephalography (EEG). However, the decoding in the source space via source localization is scarcely investigated. In this study, we tried to tackle the problem of collinearity due to the higher number of signals in the source space by two folds: first, we selected signals in predefined regions of interest (ROIs); second, we applied dimensionality reduction techniques to each ROI. The dimensionality reduction techniques were computing the mean (Mean), principal component analysis (PCA), and locality preserving projections (LPP). We also investigated the effect of decoding between utilizing a template head model and a subject-specific head model during the source localization. The results indicated that applying source-space decoding with PCA yielded slightly higher correlations and signal-to-noise (SNR) ratios than the sensor-space approach. We also observed slightly higher correlations and SNRs when applying the subject-specific head model than the template head model. However, the statistical tests revealed no significant differences between the source-space and sensor-space approaches and no significant differences between subject-specific and template head models. The decoder with Mean and PCA utilizes information mainly from precuneus and cuneus to decode the velocity kinematics similarly in the subject-specific and template head models.
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Affiliation(s)
| | - Gernot R. Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- BioTechMed Graz, Graz, Austria
- *Correspondence: Gernot R. Müller-Putz,
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27
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Moly A, Costecalde T, Martel F, Martin M, Larzabal C, Karakas S, Verney A, Charvet G, Chabardès S, Benabid AL, Aksenova T. An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic. J Neural Eng 2022; 19. [PMID: 35234665 DOI: 10.1088/1741-2552/ac59a0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 03/01/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The article aims at addressing 2 challenges to step motor BCI out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration. APPROACH Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time. It allows cross-session training with multiple recording conditions during closed loop BCI experiments. In the article, an adaptive tensor-based Recursive Exponentially Weighted Markov-Switching multi-Linear Model (REW-MSLM) decoder is proposed. REW-MSLM uses a Mixture of Expert (ME) architecture, mixing or switching independent decoders (experts) according to the probability estimated by a "gating" model. A Hidden Markov model approach is employed as gating model to improve the decoding robustness and to provide strong idle state support. The ME architecture fits the multi-limb paradigm associating an expert to a particular limb or action. MAIN RESULTS Asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) recorder is reported. The stable over a period of 6 months (without decoder recalibration) 8-dimensional alternative bimanual control of the exoskeleton and its virtual avatar is demonstrated. SIGNIFICANCE Based on the long-term (>36 months) chronic bilateral epidural ECoG recordings in a tetraplegic (ClinicalTrials.gov, NCT02550522), we addressed the poorly explored field of asynchronous bimanual BCI. The new decoder was designed to meet to several challenges: the high-dimensional control of a complex effector in experiments closer to real-world behaviour (point-to-point pursuit versus conventional center-out tasks), with the ability of the BCI system to act as a stand-alone device switching between idle and control states, and a stable performance over a long period of time without decoder recalibration.
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Affiliation(s)
- Alexandre Moly
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Thomas Costecalde
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Félix Martel
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Matthieu Martin
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des Martyrs, Grenoble, 38000, FRANCE
| | - Christelle Larzabal
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Serpil Karakas
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Alexandre Verney
- Université Paris-Saclay, Palaiseau, Palaiseau, Île-de-France, 91120, FRANCE
| | - Guillaume Charvet
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Stephan Chabardès
- CHU Grenoble Alpes, Boulevard de la Chantourne, La Tronche, Auvergne-Rhône-Alpes, 38700, FRANCE
| | - Alim-Louis Benabid
- Univ. Grenoble Alpes, CEA, LETI, Clinatec, 17, avenue des Martyrs, Grenoble, 38000, FRANCE
| | - Tatiana Aksenova
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
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28
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Huggins JE, Krusienski D, Vansteensel MJ, Valeriani D, Thelen A, Stavisky S, Norton JJS, Nijholt A, Müller-Putz G, Kosmyna N, Korczowski L, Kapeller C, Herff C, Halder S, Guger C, Grosse-Wentrup M, Gaunt R, Dusang AN, Clisson P, Chavarriaga R, Anderson CW, Allison BZ, Aksenova T, Aarnoutse E. Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier. BRAIN-COMPUTER INTERFACES 2022; 9:69-101. [PMID: 36908334 PMCID: PMC9997957 DOI: 10.1080/2326263x.2021.2009654] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/15/2021] [Indexed: 12/11/2022]
Abstract
The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.
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Affiliation(s)
- Jane E Huggins
- Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, United States 325 East Eisenhower, Room 3017; Ann Arbor, Michigan 48108-5744, 734-936-7177
| | - Dean Krusienski
- Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23219
| | - Mariska J Vansteensel
- UMC Utrecht Brain Center, Dept of Neurosurgery, University Medical Center Utrecht, The Netherlands
| | | | - Antonia Thelen
- eemagine Medical Imaging Solutions GmbH, Berlin, Germany
| | | | - James J S Norton
- National Center for Adaptive Neurotechnologies, US Department of Veterans Affairs, 113 Holland Ave, Albany, NY 12208
| | - Anton Nijholt
- Faculty EEMCS, University of Twente, Enschede, The Netherlands
| | - Gernot Müller-Putz
- Institute of Neural Engineering, GrazBCI Lab, Graz University of Technology, Stremayrgasse 16/4, 8010 Graz, Austria
| | - Nataliya Kosmyna
- Massachusetts Institute of Technology (MIT), Media Lab, E14-548, Cambridge, MA 02139, Unites States
| | | | | | - Christian Herff
- School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Christoph Guger
- g.tec medical engineering GmbH/Guger Technologies OG, Austria, Sierningstrasse 14, 4521 Schiedlberg, Austria, +43725122240-0
| | - Moritz Grosse-Wentrup
- Research Group Neuroinformatics, Faculty of Computer Science, Vienna Cognitive Science Hub, Data Science @ Uni Vienna University of Vienna
| | - Robert Gaunt
- Rehab Neural Engineering Labs, Department of Physical Medicine and Rehabilitation, Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA, 3520 5th Ave, Suite 300, Pittsburgh, PA 15213, 412-383-1426
| | - Aliceson Nicole Dusang
- Department of Electrical and Computer Engineering, School of Engineering, Brown University, Carney Institute for Brain Science, Brown University, Providence, RI
- Department of Veterans Affairs Medical Center, Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence, RI
- Center for Neurotechnology and Neurorecovery, Neurology, Massachusetts General Hospital, Boston, MA
| | | | - Ricardo Chavarriaga
- IEEE Standards Association Industry Connections group on neurotechnologies for brain-machine interface, Center for Artificial Intelligence, School of Engineering, ZHAW-Zurich University of Applied Sciences, Switzerland, Switzerland
| | - Charles W Anderson
- Department of Computer Science, Molecular, Cellular and Integrative Neurosience Program, Colorado State University, Fort Collins, CO 80523
| | - Brendan Z Allison
- Dept. of Cognitive Science, Mail Code 0515, University of California at San Diego, La Jolla, United States, 619-534-9754
| | - Tetiana Aksenova
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Erik Aarnoutse
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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29
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Tchoe Y, Bourhis AM, Cleary DR, Stedelin B, Lee J, Tonsfeldt KJ, Brown EC, Siler DA, Paulk AC, Yang JC, Oh H, Ro YG, Lee K, Russman SM, Ganji M, Galton I, Ben-Haim S, Raslan AM, Dayeh SA. Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics. Sci Transl Med 2022; 14:eabj1441. [PMID: 35044788 DOI: 10.1126/scitranslmed.abj1441] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Electrophysiological devices are critical for mapping eloquent and diseased brain regions and for therapeutic neuromodulation in clinical settings and are extensively used for research in brain-machine interfaces. However, the existing clinical and experimental devices are often limited in either spatial resolution or cortical coverage. Here, we developed scalable manufacturing processes with a dense electrical connection scheme to achieve reconfigurable thin-film, multithousand-channel neurophysiological recording grids using platinum nanorods (PtNRGrids). With PtNRGrids, we have achieved a multithousand-channel array of small (30 μm) contacts with low impedance, providing high spatial and temporal resolution over a large cortical area. We demonstrated that PtNRGrids can resolve submillimeter functional organization of the barrel cortex in anesthetized rats that captured the tissue structure. In the clinical setting, PtNRGrids resolved fine, complex temporal dynamics from the cortical surface in an awake human patient performing grasping tasks. In addition, the PtNRGrids identified the spatial spread and dynamics of epileptic discharges in a patient undergoing epilepsy surgery at 1-mm spatial resolution, including activity induced by direct electrical stimulation. Collectively, these findings demonstrated the power of the PtNRGrids to transform clinical mapping and research with brain-machine interfaces.
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Affiliation(s)
- Youngbin Tchoe
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrew M Bourhis
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel R Cleary
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.,Department of Neurological Surgery, University of California San Diego, La Jolla, CA 92093, USA
| | - Brittany Stedelin
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Jihwan Lee
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Karen J Tonsfeldt
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.,Department of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Science and Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Erik C Brown
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Dominic A Siler
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jimmy C Yang
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.,Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Hongseok Oh
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Yun Goo Ro
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Keundong Lee
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Samantha M Russman
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Mehran Ganji
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Ian Galton
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Sharona Ben-Haim
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.,Department of Neurological Surgery, University of California San Diego, La Jolla, CA 92093, USA
| | - Ahmed M Raslan
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Shadi A Dayeh
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.,Department of Neurological Surgery, University of California San Diego, La Jolla, CA 92093, USA.,Graduate Program of Materials Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
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30
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Mohammadi M, Knoche H, Thøgersen M, Bengtson SH, Gull MA, Bentsen B, Gaihede M, Severinsen KE, Andreasen Struijk LNS. Eyes-Free Tongue Gesture and Tongue Joystick Control of a Five DOF Upper-Limb Exoskeleton for Severely Disabled Individuals. Front Neurosci 2022; 15:739279. [PMID: 34975367 PMCID: PMC8718615 DOI: 10.3389/fnins.2021.739279] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 11/23/2021] [Indexed: 11/30/2022] Open
Abstract
Spinal cord injury can leave the affected individual severely disabled with a low level of independence and quality of life. Assistive upper-limb exoskeletons are one of the solutions that can enable an individual with tetraplegia (paralysis in both arms and legs) to perform simple activities of daily living by mobilizing the arm. Providing an efficient user interface that can provide full continuous control of such a device—safely and intuitively—with multiple degrees of freedom (DOFs) still remains a challenge. In this study, a control interface for an assistive upper-limb exoskeleton with five DOFs based on an intraoral tongue-computer interface (ITCI) for individuals with tetraplegia was proposed. Furthermore, we evaluated eyes-free use of the ITCI for the first time and compared two tongue-operated control methods, one based on tongue gestures and the other based on dynamic virtual buttons and a joystick-like control. Ten able-bodied participants tongue controlled the exoskeleton for a drinking task with and without visual feedback on a screen in three experimental sessions. As a baseline, the participants performed the drinking task with a standard gamepad. The results showed that it was possible to control the exoskeleton with the tongue even without visual feedback and to perform the drinking task at 65.1% of the speed of the gamepad. In a clinical case study, an individual with tetraplegia further succeeded to fully control the exoskeleton and perform the drinking task only 5.6% slower than the able-bodied group. This study demonstrated the first single-modal control interface that can enable individuals with complete tetraplegia to fully and continuously control a five-DOF upper limb exoskeleton and perform a drinking task after only 2 h of training. The interface was used both with and without visual feedback.
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Affiliation(s)
- Mostafa Mohammadi
- Neurorehabilitation Robotics and Engineering, Center for Rehabilitation Robotics, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Hendrik Knoche
- Human Machine Interaction, Department of Architecture, Design and Media Technology, Aalborg University, Aalborg, Denmark
| | - Mikkel Thøgersen
- Neurorehabilitation Robotics and Engineering, Center for Rehabilitation Robotics, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Stefan Hein Bengtson
- Human Machine Interaction, Department of Architecture, Design and Media Technology, Aalborg University, Aalborg, Denmark
| | - Muhammad Ahsan Gull
- Department of Materials and Production, Aalborg University, Aalborg, Denmark
| | - Bo Bentsen
- Neurorehabilitation Robotics and Engineering, Center for Rehabilitation Robotics, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Michael Gaihede
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | | | - Lotte N S Andreasen Struijk
- Neurorehabilitation Robotics and Engineering, Center for Rehabilitation Robotics, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
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31
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Formento E, Botros P, Carmena JM. Skilled independent control of individual motor units via a non-invasive neuromuscular-machine interface. J Neural Eng 2021; 18. [PMID: 34727532 DOI: 10.1088/1741-2552/ac35ac] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 11/02/2021] [Indexed: 11/11/2022]
Abstract
Objective.Brain-machine interfaces (BMIs) have the potential to augment human functions and restore independence in people with disabilities, yet a compromise between non-invasiveness and performance limits their relevance.Approach.Here, we hypothesized that a non-invasive neuromuscular-machine interface providing real-time neurofeedback of individual motor units within a muscle could enable independent motor unit control to an extent suitable for high-performance BMI applications.Main results.Over 6 days of training, eight participants progressively learned to skillfully and independently control three biceps brachii motor units to complete a 2D center-out task. We show that neurofeedback enabled motor unit activity that largely violated recruitment constraints observed during ramp-and-hold isometric contractions thought to limit individual motor unit controllability. Finally, participants demonstrated the suitability of individual motor units for powering general applications through a spelling task.Significance.These results illustrate the flexibility of the sensorimotor system and highlight individual motor units as a promising source of control for BMI applications.
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Affiliation(s)
- Emanuele Formento
- Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94720, United States of America
| | - Paul Botros
- Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94720, United States of America
| | - Jose M Carmena
- Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94720, United States of America.,Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720, United States of America
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32
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Cajigas I, Davis KC, Meschede-Krasa B, Prins NW, Gallo S, Naeem JA, Palermo A, Wilson A, Guerra S, Parks BA, Zimmerman L, Gant K, Levi AD, Dietrich WD, Fisher L, Vanni S, Tauber JM, Garwood IC, Abel JH, Brown EN, Ivan ME, Prasad A, Jagid J. Implantable brain-computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury. Brain Commun 2021; 3:fcab248. [PMID: 34870202 PMCID: PMC8637800 DOI: 10.1093/braincomms/fcab248] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/27/2021] [Accepted: 08/19/2021] [Indexed: 11/12/2022] Open
Abstract
Loss of hand function after cervical spinal cord injury severely impairs functional independence. We describe a method for restoring volitional control of hand grasp in one 21-year-old male subject with complete cervical quadriplegia (C5 American Spinal Injury Association Impairment Scale A) using a portable fully implanted brain-computer interface within the home environment. The brain-computer interface consists of subdural surface electrodes placed over the dominant-hand motor cortex and connects to a transmitter implanted subcutaneously below the clavicle, which allows continuous reading of the electrocorticographic activity. Movement-intent was used to trigger functional electrical stimulation of the dominant hand during an initial 29-weeks laboratory study and subsequently via a mechanical hand orthosis during in-home use. Movement-intent information could be decoded consistently throughout the 29-weeks in-laboratory study with a mean accuracy of 89.0% (range 78-93.3%). Improvements were observed in both the speed and accuracy of various upper extremity tasks, including lifting small objects and transferring objects to specific targets. At-home decoding accuracy during open-loop trials reached an accuracy of 91.3% (range 80-98.95%) and an accuracy of 88.3% (range 77.6-95.5%) during closed-loop trials. Importantly, the temporal stability of both the functional outcomes and decoder metrics were not explored in this study. A fully implanted brain-computer interface can be safely used to reliably decode movement-intent from motor cortex, allowing for accurate volitional control of hand grasp.
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Affiliation(s)
- Iahn Cajigas
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
| | - Kevin C Davis
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Benyamin Meschede-Krasa
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Noeline W Prins
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
- Department of Electrical and Information Engineering, Faculty of Engineering, University of Ruhuna, Hapugala, Galle 80000, Sri Lanka
| | - Sebastian Gallo
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Jasim Ahmad Naeem
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Anne Palermo
- Department of Physical Therapy, University of Miami, Miami, FL 33146, USA
| | - Audrey Wilson
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - Santiago Guerra
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Brandon A Parks
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Lauren Zimmerman
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
| | - Katie Gant
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - Allan D Levi
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - W Dalton Dietrich
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - Letitia Fisher
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - Steven Vanni
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - John Michael Tauber
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Indie C Garwood
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - John H Abel
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Emery N Brown
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Michael E Ivan
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
| | - Abhishek Prasad
- Department of Biomedical Engineering, University of Miami, Miami, FL 33146, USA
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
| | - Jonathan Jagid
- Department of Neurological Surgery, University of Miami, Miami, FL 33136, USA
- Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA
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Chandrasekaran S, Fifer M, Bickel S, Osborn L, Herrero J, Christie B, Xu J, Murphy RKJ, Singh S, Glasser MF, Collinger JL, Gaunt R, Mehta AD, Schwartz A, Bouton CE. Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications. Bioelectron Med 2021; 7:14. [PMID: 34548098 PMCID: PMC8456563 DOI: 10.1186/s42234-021-00076-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 08/29/2021] [Indexed: 11/10/2022] Open
Abstract
Almost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.
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Affiliation(s)
- Santosh Chandrasekaran
- Neural Bypass and Brain Computer Interface Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Matthew Fifer
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Stephan Bickel
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Luke Osborn
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Jose Herrero
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Breanne Christie
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Junqian Xu
- Departments of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, USA
| | - Rory K J Murphy
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Sandeep Singh
- Good Shepherd Rehabilitation Hospital, Allentown, PA, USA
| | - Matthew F Glasser
- Departments of Radiology and Neuroscience, Washington University in St Louis, Saint Louis, MO, USA
| | - Jennifer L Collinger
- Rehabilitation Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert Gaunt
- Rehabilitation Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashesh D Mehta
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Andrew Schwartz
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chad E Bouton
- Neural Bypass and Brain Computer Interface Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA.
- Department of Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA.
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Larzabal C, Bonnet S, Costecalde T, Auboiroux V, Charvet G, Chabardes S, Aksenova T, Sauter-Starace F. Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients. J Neural Eng 2021; 18. [PMID: 34425566 DOI: 10.1088/1741-2552/ac2003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 08/23/2021] [Indexed: 11/12/2022]
Abstract
Objective.The evaluation of the long-term stability of ElectroCorticoGram (ECoG) signals is an important scientific question as new implantable recording devices can be used for medical purposes such as Brain-Computer Interface (BCI) or brain monitoring.Approach.The long-term clinical validation of wireless implantable multi-channel acquisition system for generic interface with neurons (WIMAGINE), a wireless 64-channel epidural ECoG recorder was investigated. The WIMAGINE device was implanted in two quadriplegic patients within the context of a BCI protocol. This study focused on the ECoG signal stability in two patients bilaterally implanted in June 2017 (P1) and in November 2019 (P2).Methods. The ECoG signal was recorded at rest prior to each BCI session resulting in a 32 month and in a 14 month follow-up for P1 and P2 respectively. State-of-the-art signal evaluation metrics such as root mean square (RMS), the band power (BP), the signal to noise ratio (SNR), the effective bandwidth (EBW) and the spectral edge frequency (SEF) were used to evaluate stability of signal over the implantation time course. The time-frequency maps obtained from task-related motor activations were also studied to investigate the long-term selectivity of the electrodes.Mainresults.Based on temporal linear regressions, we report a limited decrease of the signal average level (RMS), spectral distribution (BP) and SNR, and a remarkable steadiness of the EBW and SEF. Time-frequency maps obtained during motor imagery, showed a high level of discrimination 1 month after surgery and also after 2 years.Conclusions.The WIMAGINE epidural device showed high stability over time. The signal evaluation metrics of two quadriplegic patients during 32 months and 14 months respectively provide strong evidence that this wireless implant is well-suited for long-term ECoG recording.Significance.These findings are relevant for the future of implantable BCIs, and could benefit other patients with spinal cord injury, amyotrophic lateral sclerosis, neuromuscular diseases or drug-resistant epilepsy.
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Affiliation(s)
| | - Stéphane Bonnet
- University Grenoble Alpes, CEA, LETI, DTBS, Grenoble 38000, France
| | - Thomas Costecalde
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Vincent Auboiroux
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Guillaume Charvet
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Stéphan Chabardes
- University Grenoble Alpes, Grenoble University Hospital, Grenoble 38000, France
| | - Tetiana Aksenova
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
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Mylavarapu R, Prins NW, Pohlmeyer EA, Shoup AM, Debnath S, Geng S, Sanchez JC, Schwartz O, Prasad A. Chronic recordings from the marmoset motor cortex reveals modulation of neural firing and local field potentials overlap with macaques. J Neural Eng 2021; 18. [PMID: 34225263 DOI: 10.1088/1741-2552/ac115c] [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: 03/02/2021] [Accepted: 07/05/2021] [Indexed: 11/11/2022]
Abstract
Objective.The common marmoset has been increasingly used in neural interfacing studies due to its smaller size, easier handling, and faster breeding compared to Old World non-human primate (NHP) species. While assessment of cortical anatomy in marmosets has shown strikingly similar layout to macaques, comprehensive assessment of electrophysiological properties underlying forelimb reaching movements in this bridge species does not exist. The objective of this study is to characterize electrophysiological properties of signals recorded from the marmoset primary motor cortex (M1) during a reach task and compare with larger NHP models such that this smaller NHP model can be used in behavioral neural interfacing studies.Approach and main results.Neuronal firing rates and local field potentials (LFPs) were chronically recorded from M1 in three adult, male marmosets. Firing rates, mu + beta and high gamma frequency bands of LFPs were evaluated for modulation with respect to movement. Firing rate and regularity of neurons of the marmoset M1 were similar to that reported in macaques with a subset of neurons showing selectivity to movement direction. Movement phases (rest vs move) was classified from both neural spiking and LFPs. Microelectrode arrays provide the ability to sample small regions of the motor cortex to drive brain-machine interfaces (BMIs). The results demonstrate that marmosets are a robust bridge species for behavioral neuroscience studies with motor cortical electrophysiological signals recorded from microelectrode arrays that are similar to Old World NHPs.Significance. As marmosets represent an interesting step between rodent and macaque models, successful demonstration that neuron modulation in marmoset motor cortex is analogous to reports in macaques illustrates the utility of marmosets as a viable species for BMI studies.
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Affiliation(s)
- Ramanamurthy Mylavarapu
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL United States of America
| | - Noeline W Prins
- Department of Electrical and Information Engineering, University of Ruhuna, Galle, Sri Lanka
| | - Eric A Pohlmeyer
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, United States of America
| | - Alden M Shoup
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL United States of America
| | - Shubham Debnath
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, United States of America
| | - Shijia Geng
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL, United States of America
| | | | - Odelia Schwartz
- Department of Computer Science, University of Miami, Coral Gables, FL, United States of America
| | - Abhishek Prasad
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL United States of America.,The Miami Project to Cure Paralysis, University of Miami, Miami, FL United States of America
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Jafar MR, Nagesh DS. Literature review on assistive devices available for quadriplegic people: Indian context. Disabil Rehabil Assist Technol 2021; 18:1-13. [PMID: 34176416 DOI: 10.1080/17483107.2021.1938708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/01/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE This literature review aims to find the current state of the art in self-help devices (SHD) available for people with quadriplegia. MATERIALS AND METHODS We searched original articles, technical and case studies, conference articles, and literature reviews published between 2014 to 2019 with the keywords ("Self-help devices" OR "Assistive Devices" OR "Assistive Product" OR "Assistive Technology") AND "Quadriplegia" in Science Direct, Pubmed, IEEE Xplore digital library and Web of Science. RESULTS Total 222 articles were found. After removing duplicates and screening these articles based on their title and abstracts 80 articles remained. After this, we reviewed the full text, and articles unrelated to SHD development or about the patients who require mechanical ventilation or where the upper limb is functional (C2 or above and T2 or below injuries) were discarded. After the exclusion of articles using the above-mentioned criterion 75 articles were used for further review. CONCLUSION The abandonment rate of SHD currently available in the literature is very high. The major requirement of the people was independence and improved quality of life. The situation in India is very bad as compared to the developed countries. The people with spinal cord injury in India are uneducated and very poor, with an average income of 3000 ₹ (41$). They require SHDs and training specially designed for them, keeping their needs in mind.Implications for rehabilitationPeople with quadriplegia are totally dependent on caregivers. Assistive devices not only help these people to do day-to-day tasks but also provides them self-confidence.Even though there are a lot of self-help devices currently available, still they are not able to fulfil the requirements of people with quadriplegia, hence there is a very high abandonment rate of such devices.This study provides an evidence that developing devices after understanding the functional and non-functional requirements of these subjects will decrease the abandonment rate and increase the effectiveness of the device.The results of this study can be used for planning and developing assistive devices which are more focussed on fulfilling the requirements of people with quadriplegia.
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Affiliation(s)
- Mohd Rizwan Jafar
- Department of Mechanical Engineering, Delhi Technological University, Delhi, India
| | - D S Nagesh
- Department of Mechanical Engineering, Delhi Technological University, Delhi, India
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Simeral JD, Hosman T, Saab J, Flesher SN, Vilela M, Franco B, Kelemen J, Brandman DM, Ciancibello JG, Rezaii PG, Eskandar EN, Rosler DM, Shenoy KV, Henderson JM, Nurmikko AV, Hochberg LR. Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia. IEEE Trans Biomed Eng 2021; 68:2313-2325. [PMID: 33784612 PMCID: PMC8218873 DOI: 10.1109/tbme.2021.3069119] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-and-click control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI. METHODS Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial completed on-screen item selection tasks to assess iBCI-enabled cursor control. RESULTS Communication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home. SIGNIFICANCE Wireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to high-performance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment.
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Affiliation(s)
| | - Thomas Hosman
- CfNN and the School of Engineering, Brown University
| | - Jad Saab
- CfNN and the School of Engineering, Brown University. He is now with Insight Data Science, New York City, NY
| | - Sharlene N. Flesher
- Department of Electrical Engineering, Department of Neurosurgery, and Howard Hughes Medical Institute, Stanford University. She is now with Apple Inc., Cupertino, CA
| | - Marco Vilela
- School of Engineering, Brown University. He is now with Takeda, Cambridge, MA
| | - Brian Franco
- Department of Neurology, Massachusetts General Hospital, Boston, MA. He is now with NeuroPace Inc., Mountain View, CA
| | - Jessica Kelemen
- Department of Neurology, Massachusetts General Hospital, Boston
| | - David M. Brandman
- School of Engineering, Brown University. He is now with the Department of Neurosurgery, Emory University, Atlanta, GA
| | - John G. Ciancibello
- School of Engineering, Brown University. He is now with the Center for Bioelectronic Medicine at the Feinstein Institute for Medical Research, Manhasset, NY
| | - Paymon G. Rezaii
- Department of Neurosurgery, Stanford University. He is now with the School of Medicine, Tulane University
| | - Emad N. Eskandar
- Department of Neurosurgery, Massachusetts General Hospital. He is now with the Department of Neurosurgery, Albert Einstein College of Medicine, Montefiore Medical Center, NY
| | - David M. Rosler
- CfNN and the School of Engineering and the Robert J. & Nancy D. Carney Institute for Brain Science, Brown University, and also with the Department of Neurology, Massachusetts General Hospital
| | - Krishna V. Shenoy
- Departments of Electrical Engineering, Bioengineering and Neurobiology, Wu Tsai Neurosciences Institute, and the Bio-X Institute, Stanford, and also with the Howard Hughes Medical Institute, Stanford University
| | - Jaimie M. Henderson
- Department of Neurosurgery and Wu Tsai Neurosciences Institute and the Bio-X Institute, Stanford University
| | - Arto V. Nurmikko
- School of Engineering, Department of Physics, and the Robert J. & Nancy D. Carney Institute for Brain Science, Brown University
| | - Leigh R. Hochberg
- CfNN, and the School of Engineering and the Robert J. & Nancy D. Carney Institute for Brain Science, Brown University, and the Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School
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Robinson N, Chouhan T, Mihelj E, Kratka P, Debraine F, Wenderoth N, Guan C, Lehner R. Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot. Front Hum Neurosci 2021; 15:648275. [PMID: 34211380 PMCID: PMC8239283 DOI: 10.3389/fnhum.2021.648275] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.
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Affiliation(s)
- Neethu Robinson
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Tushar Chouhan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.,Future Health Technologies, Singapore-ETH Centre, Singapore, Singapore
| | - Ernest Mihelj
- Neural Control of Movement Lab, Department of Health Science and Technology, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Paulina Kratka
- Neural Control of Movement Lab, Department of Health Science and Technology, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Frédéric Debraine
- Neural Control of Movement Lab, Department of Health Science and Technology, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Nicole Wenderoth
- Future Health Technologies, Singapore-ETH Centre, Singapore, Singapore.,Neural Control of Movement Lab, Department of Health Science and Technology, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.,Future Health Technologies, Singapore-ETH Centre, Singapore, Singapore
| | - Rea Lehner
- Future Health Technologies, Singapore-ETH Centre, Singapore, Singapore.,Neural Control of Movement Lab, Department of Health Science and Technology, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
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Cajigas I, Vedantam A. Brain-Computer Interface, Neuromodulation, and Neurorehabilitation Strategies for Spinal Cord Injury. Neurosurg Clin N Am 2021; 32:407-417. [PMID: 34053728 DOI: 10.1016/j.nec.2021.03.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
As neural bypass interfacing, neuromodulation, and neurorehabilitation continue to evolve, there is growing recognition that combination therapies may achieve superior results. This article briefly introduces these broad areas of active research and lays out some of the current evidence for their use for patients with spinal cord injury.
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Affiliation(s)
- Iahn Cajigas
- Department of Neurosurgery, University of Miami, 1095 Northwest 14th Terrace (D4-6), Miami, FL 33136, USA.
| | - Aditya Vedantam
- Department of Neurosurgery, University of Miami, 1095 Northwest 14th Terrace (D4-6), Miami, FL 33136, USA
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40
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Larzabal C, Auboiroux V, Karakas S, Charvet G, Benabid AL, Chabardès S, Costecalde T, Bonnet S. The Riemannian Spatial Pattern method: mapping and clustering movement imagery using Riemannian geometry. J Neural Eng 2021; 18. [PMID: 33770779 DOI: 10.1088/1741-2552/abf291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 03/26/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Over the last decade, Riemannian geometry has shown promising results for motor imagery classification. However, extracting the underlying spatial features is not as straightforward as for applying Common Spatial Pattern (CSP) filtering prior to classification. In this article, we propose a simple way to extract the spatial patterns obtained from Riemannian classification: the Riemannian Spatial Pattern (RSP) method, which is based on the backward channel selection procedure. APPROACH The RSP method was compared to the CSP approach on ECoG data obtained from a quadriplegic patient while performing imagined movements of arm articulations and fingers. MAIN RESULTS Similar results were found between the RSP and CSP methods for mapping each motor imagery task with activations following the classical somatotopic organization. Clustering obtained by pairwise comparisons of imagined motor movements however, revealed higher differentiation for the RSP method compared to the CSP approach. Importantly, the RSP approach could provide a precise comparison of the imagined finger flexions which added supplementary information to the mapping results. SIGNIFICANCE Our new RSP method illustrates the interest of the Riemannian framework in the spatial domain and as such offers new avenues for the neuroimaging community. This study is part of an ongoing clinical trial registered with ClinicalTrials.gov, NCT02550522.
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Affiliation(s)
| | - Vincent Auboiroux
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Serpil Karakas
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Guillaume Charvet
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Alim-Louis Benabid
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | | | - Thomas Costecalde
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Stephane Bonnet
- CEA de Grenoble, DTBS, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
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Kaiju T, Inoue M, Hirata M, Suzuki T. High-density mapping of primate digit representations with a 1152-channel µECoG array. J Neural Eng 2021; 18. [PMID: 33530064 DOI: 10.1088/1741-2552/abe245] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 02/02/2021] [Indexed: 12/11/2022]
Abstract
Objective.Advances in brain-machine interfaces (BMIs) are expected to support patients with movement disorders. Electrocorticogram (ECoG) measures electrophysiological activities over a large area using a low-invasive flexible sheet placed on the cortex. ECoG has been considered as a feasible signal source of the clinical BMI device. To capture neural activities more precisely, the feasibility of higher-density arrays has been investigated. However, currently, the number of electrodes is limited to approximately 300 due to wiring difficulties, device size, and system costs.Approach.We developed a high-density recording system with a large coverage (14 × 7 mm2) and using 1152 electrodes by directly integrating dedicated flexible arrays with the neural-recording application-specific integrated circuits and their interposers.Main results.Comparative experiments with a 128-channel array demonstrated that the proposed device could delineate the entire digit representation of a nonhuman primate. Subsampling analysis revealed that higher-amplitude signals can be measured using higher-density arrays.Significance.We expect that the proposed system that simultaneously establishes large-scale sampling, high temporal-precision of electrophysiology, and high spatial resolution comparable to optical imaging will be suitable for next-generation brain-sensing technology.
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Affiliation(s)
- Taro Kaiju
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Osaka, Japan
| | - Masato Inoue
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Osaka, Japan.,Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Masayuki Hirata
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Osaka, Japan.,Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Osaka, Japan
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Alchalabi B, Faubert J, Labbé D. A multi-modal modified feedback self-paced BCI to control the gait of an avatar. J Neural Eng 2021; 18. [PMID: 33711832 DOI: 10.1088/1741-2552/abee51] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 03/12/2021] [Indexed: 11/12/2022]
Abstract
Brain-computer interfaces (BCI) have been used to control the gait of a virtual self-avatar with a proposed application in the field of gait rehabilitation. OBJECTIVE to develop a high performance multi-modal BCI to control single steps and forward walking of an immersive virtual reality avatar. This system will overcome the limitation of existing systems. APPROACH This system used MI of these actions, in cue-paced and self-paced modes. Twenty healthy participants participated in this 4 sessions study across 4 different days. They were cued to imagine a single step forward with their right or left foot, or to imagine walking forward. They were instructed to reach a target by using the MI of multiple steps (self-paced switch-control mode) or by maintaining MI of forward walking (continuous-control mode). The movement of the avatar was controlled by two calibrated RLDA classifiers that used the µ power spectral density (PSD) over the foot area of the motor cortex as a feature. The classifiers were retrained after every session. For a subset of the trials, positive modified feedback was presented to half of the participants. MAIN RESULTS All participants were able to operate the BCI. Their average offline performance, after retraining the classifiers was 86.0 ± 6.1%, showing that the recalibration of the classifiers enhanced the offline performance of the BCI (p < 0.01). The average online performance was 85.9 ± 8.4% showing that modified feedback enhanced BCI performance (p =0.001). The average performance was 83% at self-paced switch control and 92% at continuous control mode. SIGNIFICANCE This study reports on the first novel integration of different design approaches, different control modes and different performance enhancement techniques, all in parallel in one single high performance and multi-modal BCI system, to control single steps and forward walking of an immersive virtual reality avatar.
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Affiliation(s)
- Bilal Alchalabi
- biomedical engineering, University of Montreal, 2900 Boulevard Edouard mon Petit, Montreal, Quebec, H3C 3J7, CANADA
| | - Jocelyn Faubert
- Université de Montréal, 3744 Rue Jean Brillant, Montreal, Quebec, H3T 1P1, CANADA
| | - David Labbé
- École de technologie supérieure, 1100 Rue Notre-Dame ouest, Montreal, Quebec, H3C 1K3, CANADA
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Peterson SM, Steine-Hanson Z, Davis N, Rao RPN, Brunton BW. Generalized neural decoders for transfer learning across participants and recording modalities. J Neural Eng 2021; 18. [PMID: 33418552 DOI: 10.1088/1741-2552/abda0b] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/08/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants. APPROACH We introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant. MAIN RESULTS HTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet's trained weights and demonstrate its ability to extract physiologically-relevant features. SIGNIFICANCE By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.
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Affiliation(s)
- Steven M Peterson
- Biology, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Zoe Steine-Hanson
- Computer Science and Engineering, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Nathan Davis
- Computer Science and Engineering, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Rajesh P N Rao
- Computer Science and Engineering, University of Washington, 185 E Stevens Way NE, Seattle, Washington, 98195, UNITED STATES
| | - Bingni W Brunton
- Biology, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
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Yan T, Kameda S, Suzuki K, Kaiju T, Inoue M, Suzuki T, Hirata M. Minimal Tissue Reaction after Chronic Subdural Electrode Implantation for Fully Implantable Brain-Machine Interfaces. SENSORS 2020; 21:s21010178. [PMID: 33383864 PMCID: PMC7795822 DOI: 10.3390/s21010178] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 12/12/2020] [Accepted: 12/25/2020] [Indexed: 11/16/2022]
Abstract
There is a growing interest in the use of electrocorticographic (ECoG) signals in brain–machine interfaces (BMIs). However, there is still a lack of studies involving the long-term evaluation of the tissue response related to electrode implantation. Here, we investigated biocompatibility, including chronic tissue response to subdural electrodes and a fully implantable wireless BMI device. We implanted a half-sized fully implantable device with subdural electrodes in six beagles for 6 months. Histological analysis of the surrounding tissues, including the dural membrane and cortices, was performed to evaluate the effects of chronic implantation. Our results showed no adverse events, including infectious signs, throughout the 6-month implantation period. Thick connective tissue proliferation was found in the surrounding tissues in the epidural space and subcutaneous space. Quantitative measures of subdural reactive tissues showed minimal encapsulation between the electrodes and the underlying cortex. Immunohistochemical evaluation showed no significant difference in the cell densities of neurons, astrocytes, and microglia between the implanted sites and contralateral sites. In conclusion, we established a beagle model to evaluate cortical implantable devices. We confirmed that a fully implantable wireless device and subdural electrodes could be stably maintained with sufficient biocompatibility in vivo.
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Affiliation(s)
- Tianfang Yan
- Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; (T.Y.); (S.K.)
| | - Seiji Kameda
- Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; (T.Y.); (S.K.)
- Global Center for Medical Engineering and Informatics, Osaka University, Suita 565-0871, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Katsuyoshi Suzuki
- Ogino Memorial Laboratory, Nihon Kohden Corporation, Tokorozawa 359-0037, Japan;
| | - Taro Kaiju
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita 565-0871, Japan; (T.K.); (M.I.); (T.S.)
| | - Masato Inoue
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita 565-0871, Japan; (T.K.); (M.I.); (T.S.)
| | - Takafumi Suzuki
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita 565-0871, Japan; (T.K.); (M.I.); (T.S.)
| | - Masayuki Hirata
- Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; (T.Y.); (S.K.)
- Global Center for Medical Engineering and Informatics, Osaka University, Suita 565-0871, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Correspondence:
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Wang X, Haji Fathaliyan A, Santos VJ. Toward Shared Autonomy Control Schemes for Human-Robot Systems: Action Primitive Recognition Using Eye Gaze Features. Front Neurorobot 2020; 14:567571. [PMID: 33178006 PMCID: PMC7593660 DOI: 10.3389/fnbot.2020.567571] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 08/13/2020] [Indexed: 11/13/2022] Open
Abstract
The functional independence of individuals with upper limb impairment could be enhanced by teleoperated robots that can assist with activities of daily living. However, robot control is not always intuitive for the operator. In this work, eye gaze was leveraged as a natural way to infer human intent and advance action recognition for shared autonomy control schemes. We introduced a classifier structure for recognizing low-level action primitives that incorporates novel three-dimensional gaze-related features. We defined an action primitive as a triplet comprised of a verb, target object, and hand object. A recurrent neural network was trained to recognize a verb and target object, and was tested on three different activities. For a representative activity (making a powdered drink), the average recognition accuracy was 77% for the verb and 83% for the target object. Using a non-specific approach to classifying and indexing objects in the workspace, we observed a modest level of generalizability of the action primitive classifier across activities, including those for which the classifier was not trained. The novel input features of gaze object angle and its rate of change were especially useful for accurately recognizing action primitives and reducing the observational latency of the classifier.
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Affiliation(s)
| | | | - Veronica J. Santos
- Biomechatronics Laboratory, Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA, United States
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Wang M, Li G, Jiang S, Wei Z, Hu J, Chen L, Zhang D. Enhancing gesture decoding performance using signals from posterior parietal cortex: a stereo-electroencephalograhy (SEEG) study. J Neural Eng 2020; 17:046043. [DOI: 10.1088/1741-2552/ab9987] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Plug-and-play control of a brain-computer interface through neural map stabilization. Nat Biotechnol 2020; 39:326-335. [PMID: 32895549 DOI: 10.1038/s41587-020-0662-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/04/2020] [Indexed: 11/08/2022]
Abstract
Brain-computer interfaces (BCIs) enable control of assistive devices in individuals with severe motor impairments. A limitation of BCIs that has hindered real-world adoption is poor long-term reliability and lengthy daily recalibration times. To develop methods that allow stable performance without recalibration, we used a 128-channel chronic electrocorticography (ECoG) implant in a paralyzed individual, which allowed stable monitoring of signals. We show that long-term closed-loop decoder adaptation, in which decoder weights are carried across sessions over multiple days, results in consolidation of a neural map and 'plug-and-play' control. In contrast, daily reinitialization led to degradation of performance with variable relearning. Consolidation also allowed the addition of control features over days, that is, long-term stacking of dimensions. Our results offer an approach for reliable, stable BCI control by leveraging the stability of ECoG interfaces and neural plasticity.
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Intra-cortical brain-machine interfaces for controlling upper-limb powered muscle and robotic systems in spinal cord injury. Clin Neurol Neurosurg 2020; 196:106069. [DOI: 10.1016/j.clineuro.2020.106069] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 12/20/2022]
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Marcoleta JP, Nogueira W, Doll T. Distributed mixed signal demultiplexer for electrocorticography electrodes. Biomed Phys Eng Express 2020; 6:055006. [PMID: 33444237 DOI: 10.1088/2057-1976/ab9fed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This work presents a novel architecture, exemplified for electrophysiological applications like ECoG that can be used to detect Epilepsy. The new ECoG is based on a mixed analog-digital architecture (Pulse Amplitude Modulation PAM), that allows the use of thousands of electrodes for recording. Whilst the increased number of electrodes helps to refine the spatial resolution of the medical application, the transmission of the signals from the electrodes to an external analysing device appears to be a bottleneck. To overcoming this, our work presents a hardware architecture and corresponding protocol for a mixed architecture that improves the information density between channels and their signal-to-noise ratio. This is shown by the correlation between the input and the transmitted signals in comparison to a classical digital transmission (Pulse Code Modulation PCM) system. We show in this work that it is possible to transmit the signals of 10 channels with a analog-digital architecture with the same quality of a full digital architecture.
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Affiliation(s)
- Juan Pablo Marcoleta
- Medical University Hannover, Cluster of Excellence 'Hearing4all', Hannover, Germany
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50
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Lim J, Wang PT, Shaw SJ, Armacost M, Gong H, Liu CY, Do AH, Heydari P, Nenadic Z. Pre-whitening and Null Projection as an Artifact Suppression Method for Electrocorticography Stimulation in Bi-Directional Brain Computer Interfaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3493-3496. [PMID: 33018756 DOI: 10.1109/embc44109.2020.9175760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Electrocorticography (ECoG)-based bi-directional (BD) brain-computer interfaces (BCIs) are a forthcoming technology promising to help restore function to those with motor and sensory deficits. A major problem with this paradigm is that the cortical stimulation necessary to elicit artificial sensation creates strong electrical artifacts that can disrupt BCI operation by saturating recording amplifiers or obscuring useful neural signal. Even with state-of-the-art hardware artifact suppression methods, robust signal processing techniques are still required to suppress residual artifacts that are present at the digital back-end. Herein we demonstrate the effectiveness of a pre-whitening and null projection artifact suppression method using ECoG data recorded during a clinical neurostimulation procedure. Our method achieved a maximum artifact suppression of 21.49 dB and significantly increased the number of artifact-free frequencies in the frequency domain. This performance surpasses that of a more traditional independent component analysis methodology, while retaining a reduced complexity and increased computational efficiency.
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