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Verwoert M, Ottenhoff MC, Goulis S, Colon AJ, Wagner L, Tousseyn S, van Dijk JP, Kubben PL, Herff C. Dataset of Speech Production in intracranial.Electroencephalography. Sci Data 2022; 9:434. [PMID: 35869138 PMCID: PMC9307753 DOI: 10.1038/s41597-022-01542-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/08/2022] [Indexed: 11/28/2022] Open
Abstract
Speech production is an intricate process involving a large number of muscles and cognitive processes. The neural processes underlying speech production are not completely understood. As speech is a uniquely human ability, it can not be investigated in animal models. High-fidelity human data can only be obtained in clinical settings and is therefore not easily available to all researchers. Here, we provide a dataset of 10 participants reading out individual words while we measured intracranial EEG from a total of 1103 electrodes. The data, with its high temporal resolution and coverage of a large variety of cortical and sub-cortical brain regions, can help in understanding the speech production process better. Simultaneously, the data can be used to test speech decoding and synthesis approaches from neural data to develop speech Brain-Computer Interfaces and speech neuroprostheses. Measurement(s) | Brain activity | Technology Type(s) | Stereotactic electroencephalography | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Environment | Epilepsy monitoring center | Sample Characteristic - Location | The Netherlands |
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Ottenhoff MC, Goulis S, Wagner L, Tousseyn S, Colon A, Kubben P, Herff C. Continuously Decoding Grasping Movements using Stereotactic Depth Electrodes. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:6098-6101. [PMID: 34892508 DOI: 10.1109/embc46164.2021.9629639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Brain-Computer Interfaces (BCIs) that decode a patient's movement intention to control a prosthetic device could restore some independence to paralyzed patients. An important step on the road towards naturalistic prosthetic control is to decode movement continuously with low-latency. BCIs based on intracortical micro-arrays provide continuous control of robotic arms, but require a minor craniotomy. Surface recordings of neural activity using EEG have made great advances over the last years, but suffer from high noise levels and large intra-session variance. Here, we investigate the use of minimally invasive recordings using stereotactically implanted EEG (sEEG). These electrodes provide a sparse sampling across many brain regions. So far, promising decoding results have been presented using data measured from the subthalamic nucleus or trial-to-trial based methods using depth electrodes. In this work, we demonstrate that grasping movements can continuously be decoded using sEEG electrodes, as well. Beta and high-gamma activity was extracted from eight participants performing a grasping task. We demonstrate above chance level decoding of movement vs rest and left vs right, from both frequency bands with accuracies up to 0.94 AUC. The vastly different electrode locations between participants lead to large variability. In the future, we hope that sEEG recordings will provide additional information for the decoding process in neuroprostheses.
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Ottenhoff MC, Ramos LA, Potters W, Janssen MLF, Hubers D, Hu S, Fridgeirsson EA, Piña-Fuentes D, Thomas R, van der Horst ICC, Herff C, Kubben P, Elbers PWG, Marquering HA, Welling M, Simsek S, de Kruif MD, Dormans T, Fleuren LM, Schinkel M, Noordzij PG, van den Bergh JP, Wyers CE, Buis DTB, Wiersinga WJ, van den Hout EHC, Reidinga AC, Rusch D, Sigaloff KCE, Douma RA, de Haan L, Gritters van den Oever NC, Rennenberg RJMW, van Wingen GA, Aries MJH, Beudel M. Predicting mortality of individual patients with COVID-19: a multicentre Dutch cohort. BMJ Open 2021; 11:e047347. [PMID: 34281922 PMCID: PMC8290951 DOI: 10.1136/bmjopen-2020-047347] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 06/16/2021] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE Develop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital. DESIGN Retrospective cohort study. SETTING A multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020. PARTICIPANTS SARS-CoV-2 positive patients (age ≥18) admitted to the hospital. MAIN OUTCOME MEASURES 21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis. RESULTS 2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81). CONCLUSION Both models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.
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Affiliation(s)
- Maarten C Ottenhoff
- Department of Neurosurgery, Maastricht University, Maastricht, The Netherlands
| | - Lucas A Ramos
- Department of Biomedical Engineering and Physics/Department of Epidemiology & Data Science, Amsterdam University Medical Centres, Duivendrecht, The Netherlands
| | - Wouter Potters
- Department of Neurology, Amsterdam University Medical Centres, Duivendrecht, The Netherlands
| | - Marcus L F Janssen
- Department of Clinical Neurophysiology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Deborah Hubers
- Department of Intensive Care, Maastricht Universitair Medisch Centrum+, Maastricht, The Netherlands
| | - Shi Hu
- Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
| | - Egill A Fridgeirsson
- Department of Psychiatry, Amsterdam University Medical Centres, Duivendrecht, The Netherlands
| | - Dan Piña-Fuentes
- Department of Neurology, Amsterdam University Medical Centres, Duivendrecht, The Netherlands
| | - Rajat Thomas
- Department of Psychiatry, Amsterdam University Medical Centres, Duivendrecht, The Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht Universitair Medisch Centrum+, Maastricht, The Netherlands
| | - Christian Herff
- Department of Neurosurgery, Maastricht University, Maastricht, The Netherlands
| | - Pieter Kubben
- Department of Neurosurgery, Maastricht Universitair Medisch Centrum+, Maastricht, The Netherlands
| | - Paul W G Elbers
- Department of Intensive Care, Amsterdam UMC - Locatie VUMC, Amsterdam, The Netherlands
| | - Henk A Marquering
- Department of Biomedical Engineering and Physics/Department of Epidemiology & Data Science, Amsterdam University Medical Centres, Duivendrecht, The Netherlands
| | - Max Welling
- Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
| | - Suat Simsek
- Department of Internal Medicine, Noordwest Ziekenhuisgroep, Alkmaar, The Netherlands
- Department of Internal Medicine, Section of Endocrinology, Amsterdam UMC - Locatie VUMC, Amsterdam, The Netherlands
| | - Martijn D de Kruif
- Department of Pulmonary Medicine, Zuyderland Medical Centre Heerlen, Heerlen, The Netherlands
| | - Tom Dormans
- Vascular Medicine, Amsterdam University Medical Centres, Duivendrecht, The Netherlands
| | - Lucas M Fleuren
- Department of Intensive Care, Amsterdam University Medical Centres, Duivendrecht, Noord-Holland, The Netherlands
| | - Michiel Schinkel
- Center for Experimental and Molecular Medicine (C.E.M.M.), Amsterdam University Medical Centres, Duivendrecht, The Netherlands
| | - Peter G Noordzij
- Department of Anesthesiology and Intensive Care, Sint Antonius Hospital, Nieuwegein, The Netherlands
| | | | - Caroline E Wyers
- Department of Internal Medicine, VieCuri Medical Centre, Venlo, The Netherlands
| | - David T B Buis
- Department of Internal Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, The Netherlands
| | - W Joost Wiersinga
- Department of Internal Medicine, Amsterdam University Medical Centres, Duivendrecht, The Netherlands
- Center for Experimental and Molecular Medicine (C.E.M.M.), Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Ella H C van den Hout
- Department of Internal Medicine, Noordwest Ziekenhuisgroep, Alkmaar, The Netherlands
| | - Auke C Reidinga
- Department of Intensive Care, Martini Ziekenhuis, Groningen, The Netherlands
| | - Daisy Rusch
- Research, Martini Ziekenhuis, Groningen, The Netherlands
| | - Kim C E Sigaloff
- Department of Internal Medicine, Amsterdam University Medical Centres, Duivendrecht, The Netherlands
| | - Renee A Douma
- Department of Internal Medicine, Flevoziekenhuis, Almere, Flevoland, The Netherlands
| | - Lianne de Haan
- Department of Internal Medicine, Flevoziekenhuis, Almere, Flevoland, The Netherlands
| | | | - Roger J M W Rennenberg
- Department of Internal Medicine, Maastricht Universitair Medisch Centrum+, Maastricht, The Netherlands
| | - Guido A van Wingen
- Department of Psychiatry, University of Amsterdam, Amsterdam, The Netherlands
| | - Marcel J H Aries
- Department of Intensive Care, Maastricht Universitair Medisch Centrum+, Maastricht, The Netherlands
| | - Martijn Beudel
- Department of Neurology, Amsterdam University Medical Centres, Duivendrecht, The Netherlands
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