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Barth K, Schmitz C, Jochum T, Viventi J. Intan Technologies integrated circuits can produce analog-to-digital conversion artifacts that affect neural signal acquisition. J Neural Eng 2024; 21:10.1088/1741-2552/ad5762. [PMID: 38865993 PMCID: PMC11316496 DOI: 10.1088/1741-2552/ad5762] [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: 03/22/2024] [Accepted: 06/12/2024] [Indexed: 06/14/2024]
Abstract
Objective.Intan Technologies' integrated circuits (ICs) are valuable tools for neurophysiological data acquisition, providing signal amplification, filtering, and digitization from many channels (up to 64 channels/chip) at high sampling rates (up to 30 kSPS) within a compact package (⩽9× 7 mm). However, we found that the analog-to-digital converters (ADCs) in the Intan RHD2000 series ICs can produce artifacts in recorded signals. Here, we examine the effects of these ADC artifacts on neural signal quality and describe a method to detect them in recorded data.Approach.We identified two types of ADC artifacts produced by Intan ICs: 1) jumps, resulting from missing output codes, and 2) flatlines, resulting from overrepresented output codes. We identified ADC artifacts in neural recordings acquired with Intan RHD2000 ICs and tested the repeated performance of 17 ICsin vitro. With the on-chip digital-signal-processing disabled, we detected the ADC artifacts in each test recording by examining the distribution of unfiltered ADC output codes.Main Results.We found larger ADC artifacts in recordings using the Intan RHX data acquisition software versions 3.0-3.2, which did not run the necessary ADC calibration command when the inputs to the Intan recording controller were rescanned. This has been corrected in the Intan RHX software version 3.3. We found that the ADC calibration routine significantly reduced, but did not fully eliminate, the occurrence and size of ADC artifacts as compared with recordings acquired when the calibration routine was not run (p< 0.0001). When the ADC calibration routine was run, we found that the artifacts produced by each ADC were consistent over time, enabling us to sort ICs by performance.Significance.Our findings call attention to the importance of evaluating signal quality when acquiring electrophysiological data using Intan Technologies ICs and offer a method for detecting ADC artifacts in recorded data.
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Affiliation(s)
- Katrina Barth
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Cecilia Schmitz
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Thomas Jochum
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Jonathan Viventi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
- Duke Comprehensive Epilepsy Center, Duke University School of Medicine, Durham, NC, USA
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2
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Zhang Y, Liu L, Ding Y, Chen X, Monsoor T, Daida A, Oana S, Hussain S, Sankar R, Fallah A, Santana-Gomez C, Engel J, Staba RJ, Speier W, Zhang J, Nariai H, Roychowdhury V. PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application. J Neural Eng 2024; 21:036023. [PMID: 38722308 PMCID: PMC11135143 DOI: 10.1088/1741-2552/ad4916] [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: 10/18/2023] [Revised: 04/19/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024]
Abstract
Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Lawrence Liu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Yuanyi Ding
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Xin Chen
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Shaun Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Cesar Santana-Gomez
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, United States of America
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, United States of America
- Department of Neurobiology, University of California, Los Angeles, CA, United States of America
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, United States of America
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, United States of America
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States of America
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Jianguo Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, People’s Republic of China
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
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3
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Viana D, Walston ST, Masvidal-Codina E, Illa X, Rodríguez-Meana B, Del Valle J, Hayward A, Dodd A, Loret T, Prats-Alfonso E, de la Oliva N, Palma M, Del Corro E, Del Pilar Bernicola M, Rodríguez-Lucas E, Gener T, de la Cruz JM, Torres-Miranda M, Duvan FT, Ria N, Sperling J, Martí-Sánchez S, Spadaro MC, Hébert C, Savage S, Arbiol J, Guimerà-Brunet A, Puig MV, Yvert B, Navarro X, Kostarelos K, Garrido JA. Nanoporous graphene-based thin-film microelectrodes for in vivo high-resolution neural recording and stimulation. NATURE NANOTECHNOLOGY 2024; 19:514-523. [PMID: 38212522 PMCID: PMC11026161 DOI: 10.1038/s41565-023-01570-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 11/07/2023] [Indexed: 01/13/2024]
Abstract
One of the critical factors determining the performance of neural interfaces is the electrode material used to establish electrical communication with the neural tissue, which needs to meet strict electrical, electrochemical, mechanical, biological and microfabrication compatibility requirements. This work presents a nanoporous graphene-based thin-film technology and its engineering to form flexible neural interfaces. The developed technology allows the fabrication of small microelectrodes (25 µm diameter) while achieving low impedance (∼25 kΩ) and high charge injection (3-5 mC cm-2). In vivo brain recording performance assessed in rodents reveals high-fidelity recordings (signal-to-noise ratio >10 dB for local field potentials), while stimulation performance assessed with an intrafascicular implant demonstrates low current thresholds (<100 µA) and high selectivity (>0.8) for activating subsets of axons within the rat sciatic nerve innervating tibialis anterior and plantar interosseous muscles. Furthermore, the tissue biocompatibility of the devices was validated by chronic epicortical (12 week) and intraneural (8 week) implantation. This work describes a graphene-based thin-film microelectrode technology and demonstrates its potential for high-precision and high-resolution neural interfacing.
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Affiliation(s)
- Damià Viana
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Steven T Walston
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Eduard Masvidal-Codina
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Xavi Illa
- Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Campus UAB, Bellaterra, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - Bruno Rodríguez-Meana
- Institute of Neurosciences, Department of Cell Biology, Physiology and Immunology, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jaume Del Valle
- Institute of Neurosciences, Department of Cell Biology, Physiology and Immunology, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Universitat Autònoma de Barcelona, Barcelona, Spain
- Secció de Fisiologia, Department de Bioquímica i Fisiologia, Facultat de Farmàcia i Ciències de l'Alimentació, Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
| | - Andrew Hayward
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology, Medicine & Health, Manchester, UK
| | - Abbie Dodd
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology, Medicine & Health, Manchester, UK
| | - Thomas Loret
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology, Medicine & Health, Manchester, UK
| | - Elisabet Prats-Alfonso
- Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Campus UAB, Bellaterra, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - Natàlia de la Oliva
- Institute of Neurosciences, Department of Cell Biology, Physiology and Immunology, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Marie Palma
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Elena Del Corro
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - María Del Pilar Bernicola
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Elisa Rodríguez-Lucas
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
- Hospital del Mar Research Institute, Barcelona, Spain
| | - Thomas Gener
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
- Hospital del Mar Research Institute, Barcelona, Spain
| | - Jose Manuel de la Cruz
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Miguel Torres-Miranda
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Fikret Taygun Duvan
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Nicola Ria
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Justin Sperling
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Sara Martí-Sánchez
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Maria Chiara Spadaro
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Clément Hébert
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
| | - Sinead Savage
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology, Medicine & Health, Manchester, UK
| | - Jordi Arbiol
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Anton Guimerà-Brunet
- Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Campus UAB, Bellaterra, Spain
| | - M Victoria Puig
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain
- Hospital del Mar Research Institute, Barcelona, Spain
| | - Blaise Yvert
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Xavier Navarro
- Institute of Neurosciences, Department of Cell Biology, Physiology and Immunology, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Kostas Kostarelos
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain.
- Institute of Neurosciences, Department of Cell Biology, Physiology and Immunology, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Nanomedicine Lab, National Graphene Institute and Faculty of Biology, Medicine & Health, Manchester, UK.
| | - Jose A Garrido
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Barcelona, Spain.
- ICREA, Barcelona, Spain.
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4
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Frauscher B, Rossetti AO, Beniczky S. Recent advances in clinical electroencephalography. Curr Opin Neurol 2024; 37:134-140. [PMID: 38230652 DOI: 10.1097/wco.0000000000001246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
PURPOSE OF REVIEW Clinical electroencephalography (EEG) is a conservative medical field. This explains likely the significant gap between clinical practice and new research developments. This narrative review discusses possible causes of this discrepancy and how to circumvent them. More specifically, we summarize recent advances in three applications of clinical EEG: source imaging (ESI), high-frequency oscillations (HFOs) and EEG in critically ill patients. RECENT FINDINGS Recently published studies on ESI provide further evidence for the accuracy and clinical utility of this method in the multimodal presurgical evaluation of patients with drug-resistant focal epilepsy, and opened new possibilities for further improvement of the accuracy. HFOs have received much attention as a novel biomarker in epilepsy. However, recent studies questioned their clinical utility at the level of individual patients. We discuss the impediments, show up possible solutions and highlight the perspectives of future research in this field. EEG in the ICU has been one of the major driving forces in the development of clinical EEG. We review the achievements and the limitations in this field. SUMMARY This review will promote clinical implementation of recent advances in EEG, in the fields of ESI, HFOs and EEG in the intensive care.
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Affiliation(s)
- Birgit Frauscher
- Department of Neurology, Duke University Medical Center & Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
| | - Andrea O Rossetti
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund
- Aarhus University Hospital, Aarhus, Denmark
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5
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Londoño‐Ramírez H, Huang X, Cools J, Chrzanowska A, Brunner C, Ballini M, Hoffman L, Steudel S, Rolin C, Mora Lopez C, Genoe J, Haesler S. Multiplexed Surface Electrode Arrays Based on Metal Oxide Thin-Film Electronics for High-Resolution Cortical Mapping. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308507. [PMID: 38145348 PMCID: PMC10933637 DOI: 10.1002/advs.202308507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/15/2023] [Indexed: 12/26/2023]
Abstract
Electrode grids are used in neuroscience research and clinical practice to record electrical activity from the surface of the brain. However, existing passive electrocorticography (ECoG) technologies are unable to offer both high spatial resolution and wide cortical coverage, while ensuring a compact acquisition system. The electrode count and density are restricted by the fact that each electrode must be individually wired. This work presents an active micro-electrocorticography (µECoG) implant that tackles this limitation by incorporating metal oxide thin-film transistors (TFTs) into a flexible electrode array, allowing to address multiple electrodes through a single shared readout line. By combining the array with an incremental-ΔΣ readout integrated circuit (ROIC), the system is capable of recording from up to 256 electrodes virtually simultaneously, thanks to the implemented 16:1 time-division multiplexing scheme, offering lower noise levels than existing active µECoG arrays. In vivo validation is demonstrated acutely in mice by recording spontaneous activity and somatosensory evoked potentials over a cortical surface of ≈8×8 mm2 . The proposed neural interface overcomes the wiring bottleneck limiting ECoG arrays, holding promise as a powerful tool for improved mapping of the cerebral cortex and as an enabling technology for future brain-machine interfaces.
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Affiliation(s)
- Horacio Londoño‐Ramírez
- Department of Neuroscience, Leuven Brain InstituteKatholieke Universiteit (KU) LeuvenLeuven3001Belgium
- Neuroelectronics Research Flanders (NERF)Leuven3001Belgium
- imecLeuven3001Belgium
- Flanders Institute for Biotechnology (VIB)Gent9052Belgium
| | - Xiaohua Huang
- imecLeuven3001Belgium
- Department of Electrical Engineering (ESAT)Katholieke Universiteit (KU) LeuvenLeuven3001Belgium
| | - Jordi Cools
- Neuroelectronics Research Flanders (NERF)Leuven3001Belgium
- imecLeuven3001Belgium
- Flanders Institute for Biotechnology (VIB)Gent9052Belgium
- Present address:
Thermo Fisher Scientific3001LeuvenBelgium
| | - Anna Chrzanowska
- Neuroelectronics Research Flanders (NERF)Leuven3001Belgium
- Flanders Institute for Biotechnology (VIB)Gent9052Belgium
- Department of BiologyKatholieke Universiteit (KU) LeuvenLeuven3001Belgium
| | - Clément Brunner
- Department of Neuroscience, Leuven Brain InstituteKatholieke Universiteit (KU) LeuvenLeuven3001Belgium
- Neuroelectronics Research Flanders (NERF)Leuven3001Belgium
- Flanders Institute for Biotechnology (VIB)Gent9052Belgium
| | - Marco Ballini
- imecLeuven3001Belgium
- Present address:
Microphone Business Unit, TDK InvenSense20057MilanItaly
| | - Luis Hoffman
- Neuroelectronics Research Flanders (NERF)Leuven3001Belgium
- imecLeuven3001Belgium
- Present address:
Swave Photonics3001LeuvenBelgium
| | - Soeren Steudel
- imecLeuven3001Belgium
- Present address:
MICLEDI Microdisplays3001LeuvenBelgium
| | | | | | - Jan Genoe
- Department of Electrical Engineering (ESAT)Katholieke Universiteit (KU) LeuvenLeuven3001Belgium
| | - Sebastian Haesler
- Department of Neuroscience, Leuven Brain InstituteKatholieke Universiteit (KU) LeuvenLeuven3001Belgium
- Neuroelectronics Research Flanders (NERF)Leuven3001Belgium
- Flanders Institute for Biotechnology (VIB)Gent9052Belgium
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6
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Paulk AC, Salami P, Zelmann R, Cash SS. Electrode Development for Epilepsy Diagnosis and Treatment. Neurosurg Clin N Am 2024; 35:135-149. [PMID: 38000837 DOI: 10.1016/j.nec.2023.09.003] [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: 11/26/2023]
Abstract
Recording neural activity has been a critical aspect in the diagnosis and treatment of patients with epilepsy. For those with intractable epilepsy, intracranial neural monitoring has been of substantial importance. Clinically, however, methods for recording neural information have remained essentially unchanged for decades. Over the last decade or so, rapid advances in electrode technology have begun to change this landscape. New systems allow for the observation of neural activity with high spatial resolution and, in some cases, at the level of the activity of individual neurons. These new tools have contributed greatly to our understanding of brain function and dysfunction. Here, the authors review the primary technologies currently in use in humans. The authors discuss other possible systems, some of the challenges which come along with these devices, and how they will become incorporated into the clinical workflow. Ultimately, the expectation is that these new, high-density, high-spatial-resolution recording systems will become a valuable part of the clinical arsenal used in the diagnosis and surgical management of epilepsy.
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Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA.
| | - Pariya Salami
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
| | - Rina Zelmann
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
| | - Sydney S Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
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7
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Duraivel S, Rahimpour S, Chiang CH, Trumpis M, Wang C, Barth K, Harward SC, Lad SP, Friedman AH, Southwell DG, Sinha SR, Viventi J, Cogan GB. High-resolution neural recordings improve the accuracy of speech decoding. Nat Commun 2023; 14:6938. [PMID: 37932250 PMCID: PMC10628285 DOI: 10.1038/s41467-023-42555-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 10/13/2023] [Indexed: 11/08/2023] Open
Abstract
Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.
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Affiliation(s)
| | - Shervin Rahimpour
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, USA
- Department of Neurosurgery, Clinical Neuroscience Center, University of Utah, Salt Lake City, UT, USA
| | - Chia-Han Chiang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Michael Trumpis
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Charles Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Katrina Barth
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Stephen C Harward
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, USA
- Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC, USA
| | - Shivanand P Lad
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, USA
| | - Allan H Friedman
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, USA
| | - Derek G Southwell
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, USA
- Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC, USA
- Department of Neurobiology, Duke School of Medicine, Durham, NC, USA
| | - Saurabh R Sinha
- Penn Epilepsy Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jonathan Viventi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, USA.
- Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC, USA.
- Department of Neurobiology, Duke School of Medicine, Durham, NC, USA.
| | - Gregory B Cogan
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, USA.
- Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC, USA.
- Department of Neurology, Duke School of Medicine, Durham, NC, USA.
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA.
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.
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