72 results on '"Iturrate I"'
Search Results
2. P1363: OUTCOME OF ADULT HEMATOPOIETIC TRANSPLANT RECIPIENTS AFTER ADMISSION TO INTENSIVE CARE UNIT (ICU): SINGLE CENTRE EXPERIENCE.
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Puchol, A., primary, Mayor, C., additional, Díaz, S., additional, Iturrate, I., additional, Figuera, Á., additional, Alegre, A., additional, and Aguado, B., additional
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- 2022
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3. Towards robot cell matrices for agile production – SDU Robotics' assembly cell at the WRC 2018
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Schlette, C., primary, Buch, A. G., additional, Hagelskjær, F., additional, Iturrate, I., additional, Kraft, D., additional, Kramberger, A., additional, Lindvig, A. P., additional, Mathiesen, S., additional, Petersen, H. G., additional, Rasmussen, M. H., additional, Savarimuthu, T. R., additional, Sloth, C., additional, Sørensen, L. C., additional, and Thulesen, T. N., additional
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- 2019
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4. Towards robot cell matrices for agile production – SDU Robotics' assembly cell at the WRC 2018.
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Schlette, C., Buch, A. G., Hagelskjær, F., Iturrate, I., Kraft, D., Kramberger, A., Lindvig, A. P., Mathiesen, S., Petersen, H. G., Rasmussen, M. H., Savarimuthu, T. R., Sloth, C., Sørensen, L. C., and Thulesen, T. N.
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ROBOT programming ,ROBOTIC assembly ,ROBOT design & construction ,ROBOT control systems ,RESEARCH & development projects ,ROBOTS ,COMPUTER vision ,COMPUTER programming - Abstract
To support shifting to high mix/low volume production, manufacturers in high wage countries aim for robotizing their production operations – with a special focus on the late production phases, where robotic assembly cells are then confronted with any complexities resulting from part and product varieties. The 'World Robot Challenge 2018' (WRC 2018) emulated such high mix/low volume production scenarios in a competition taking place in Tokyo, Japan. As part of our activities in SDU's newly founded I4.0 Lab, we integrated and advanced our experiences and developments from our various R & D projects in a novel robotic assembly cell design to compete in the WRC 2018. This article describes the system architecture as well as main aspects of its implementation regarding robot control, robot programming and computer vision and how they contributed to winning the challenge. Due to the application of collaborative robots, the cell design allows for operation without fences. Hence, multiple copies of the cell can be arranged in a highly reconfigurable, highly adaptable matrix structure in which several production flows can be handled concurrently. This concept was demonstrated by the installation of a duplicate cell that allowed for parallel developments on two cells and prolonged development also after shipping the first cell to Japan. [ABSTRACT FROM AUTHOR]
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- 2020
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5. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke
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Biasiucci, A., primary, Leeb, R., additional, Iturrate, I., additional, Perdikis, S., additional, Al-Khodairy, A., additional, Corbet, T., additional, Schnider, A., additional, Schmidlin, T., additional, Zhang, H., additional, Bassolino, M., additional, Viceic, D., additional, Vuadens, P., additional, Guggisberg, A. G., additional, and Millán, J. d. R., additional
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- 2018
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6. Sensory Threshold Electrical Stimulation Enhances Classification Of Motor Imagery
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Corbet, Tiffany, Iturrate, I¤aki, Pereira, Michael, Perdikis, Serafeim, and Mill n, Jos Del R.
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Proceedings Of The 7Th Graz Brain-Computer Interface Conference 2017, From Vision To Reality, September 18-22, 2017 Graz University Of Technology, Austria
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- 2017
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7. Asynchronous Detection Of Error Potentials
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Llorente, Jason Omedes, Iturrate, I¤aki, and Montesano, Luis
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Proceedings of the 6th International Brain-Computer Interface Conference 2014
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- 2014
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8. Interactive Learning from Unlabeled Instructions
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Jonathan Grizou, Iturrate, I., Montesano, L., Oudeyer, P. -Y, Lopes, M., Grizou, Jonathan, École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Flowing Epigenetic Robots and Systems (Flowers), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Departamento de Informática e Ingeniería de Sistemas (DIIS), University of Zaragoza - Universidad de Zaragoza [Zaragoza], ERC EXPLORER 24007, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), and École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris)
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Adaptive interfaces ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Interactive Learning ,Brain-Computer Interaction ,Sequential Decision Making ,[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO] ,Planning under Uncertainty ,[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] ,[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] ,User Modeling - Abstract
International audience; Interactive learning deals with the problem of learning and solving tasks using human instructions. It is common in human-robot interaction, tutoring systems, and in human-computer interfaces such as brain-computer ones. In most cases, learning these tasks is possible because the signals are predefined or an ad-hoc calibration procedure allows to map signals to specific meanings. In this paper, we address the problem of simultaneously solving a task under human feedback and learning the associated meanings of the feedback signals. This has important practical application since the user can start controlling a device from scratch, without the need of an expert to define the meaning of signals or carrying out a calibration phase. The paper proposes an algorithm that simultaneously assign meanings to signals while solving a sequential task under the assumption that both, human and machine, share the same a priori on the possible instruction meanings and the possible tasks. Furthermore, we show using synthetic and real EEG data from a brain-computer interface that taking into account the uncertainty of the task and the signal is necessary for the machine to actively plan how to solve the task efficiently.
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- 2014
9. EEG-based decoding of error-related brain activity in a real-world driving task
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Zhang, H, primary, Chavarriaga, R, additional, Khaliliardali, Z, additional, Gheorghe, L, additional, Iturrate, I, additional, and Millán, J d R, additional
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- 2015
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10. Prediction of command delivery time for BCI
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Saeedi, S., primary, Chavarriaga, R., additional, Iturrate, I., additional, Millan, J. d. R., additional, and Carlson, T., additional
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- 2014
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11. Latency correction of event-related potentials between different experimental protocols
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Iturrate, I, primary, Chavarriaga, R, additional, Montesano, L, additional, Minguez, J, additional, and Millán, JdR, additional
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- 2014
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12. Task-dependent signal variations in EEG error-related potentials for brain–computer interfaces
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Iturrate, I, primary, Montesano, L, additional, and Minguez, J, additional
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- 2013
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13. Minimizing calibration time using inter-subject information of single-trial recognition of error potentials in brain-computer interfaces
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Iturrate, I., primary, Montesano, L., additional, Chavarriaga, R., additional, del R Millan, J., additional, and Minguez, J., additional
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- 2011
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14. EEG single-trial classification of visual, auditive and vibratory feedback potentials in Brain-Computer Interfaces
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Lopez-Larraz, E., primary, Creatura, M., additional, Iturrate, I., additional, Montesano, L., additional, and Minguez, J., additional
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- 2011
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15. Real-time recognition of feedback error-related potentials during a time-estimation task
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Lopez-Larraz, E, primary, Iturrate, I, additional, Montesano, L, additional, and Minguez, J, additional
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- 2010
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16. Robot reinforcement learning using EEG-based reward signals
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Iturrate, I, primary, Montesano, L, additional, and Minguez, J, additional
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- 2010
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17. A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation
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Iturrate, I., primary, Antelis, J.M., additional, Kubler, A., additional, and Minguez, J., additional
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- 2009
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18. Synchronous EEG brain-actuated wheelchair with automated navigation
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Iturrate, I., primary, Antelis, J., additional, and Minguez, J., additional
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- 2009
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19. Single trial recognition of error-related potentials during observation of robot operation.
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Iturrate, I., Montesano, L., and Minguez, J.
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- 2010
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20. Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control
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Iturrate, I., Chavarriaga, R., Montesano, L., Minguez, J., and Millán, J.D.R.
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Adult ,Male ,Neuroprosthetics ,Brain activity and meditation ,Computer science ,Movement ,Electroencephalography ,Article ,Young Adult ,Human–computer interaction ,Component (UML) ,medicine ,Humans ,Learning ,Adaptation (computer science) ,Brain–computer interface ,Multidisciplinary ,medicine.diagnostic_test ,business.industry ,Motor control ,Brain ,Cognition ,Brain-Computer Interfaces ,Female ,Artificial intelligence ,business ,Psychomotor Performance - Abstract
Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct and exploits the brain correlates of this assessment to learn suitable motor behaviours. Results show that, after a short user’s training period, this teaching BMI paradigm operated three different neuroprostheses and generalized across several targets. Our results further support that these error-related signals reflect a task-independent monitoring mechanism in the brain, making this teaching paradigm scalable. We anticipate this BMI approach to become a key component of any neuroprosthesis that mimics natural motor control as it enables continuous adaptation in the absence of explicit information about goals. Furthermore, our paradigm can seamlessly incorporate other cognitive signals and conventional neuroprosthetic approaches, invasive or non-invasive, to enlarge the range and complexity of tasks that can be accomplished.
21. Calibration-free BCI based control
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Jonathan Grizou, Iturrate, I., Montesano, L., Oudeyer, P. -Y, Lopes, M., Flowing Epigenetic Robots and Systems (Flowers), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Departamento de Informática e Ingeniería de Sistemas (DIIS), University of Zaragoza - Universidad de Zaragoza [Zaragoza], ERC EXPLORER 24007, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris), and Grizou, Jonathan
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Adaptive interfaces ,Brain-Computer Interface ,[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO] ,Sequential Decision Making ,[SCCO.COMP]Cognitive science/Computer science ,[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] ,General Medicine ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,[SCCO.COMP] Cognitive science/Computer science ,Brain-Computer Interaction ,Interactive Learning ,Planning under Uncertainty ,[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] ,User Modeling - Abstract
Recent works have explored the use of brain signals to directly control virtual and robotic agents in sequential tasks. So far in such brain-computer interfaces (BCI), an explicit calibration phase was required to build a decoder that translates raw electroencephalography (EEG) signals from the brain of each user into meaningful instructions. This paper proposes a method that removes the calibration phase, and allows a user to control an agent to solve a sequential task. The proposed method assumes a distribution of possible tasks, and infers the interpretation of EEG signals and the task by selecting the hypothesis which best explains the history of interaction. We introduce a measure of uncertainty on the task and on the EEG signal interpretation to act as an exploratory bonus for a planning strategy. This speeds up learning by guiding the system to regions that better disambiguate among task hypotheses. We report experiments where four users use BCI to control an agent on a virtual world to reach a target without any previous calibration process.
22. Latency correction of error-related potentials reduces BCI calibration time
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Iturrate, I¤aki, Chavarriaga, Ricardo, Montesano, Luis, Minguez, Javier, and Millan, Jose Del R.
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Calibration of brain-machine interfaces exploiting event-related potentials has to be performed for each experimental paradigm. Even if these signals have been used in previous experiments with different protocols. We show that use of signals from previous experiments can reduce the calibration time for single-trial classification of error-related potentials. Compensating latency variations across tasks yield up to a 50% reduction the training period in new experiments without decrease in online performance compared to the standard training.
23. Safe contact-based robot active search using Bayesian optimization and control barrier functions.
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Vinter-Hviid F, Sloth C, Savarimuthu TR, and Iturrate I
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In robotics, active exploration and learning in uncertain environments must take into account safety, as the robot may otherwise damage itself or its surroundings. This paper presents a method for safe active search using Bayesian optimization and control barrier functions. As robot paths undertaken during sampling are continuous, we consider an informative continuous expected improvement acquisition function. To safely bound the contact forces between the robot and its surroundings, we leverage exponential control barrier functions, utilizing the derivative of the force in the contact model to increase robustness to uncertainty in the contact boundary. Our approach is demonstrated on a fully autonomous robot for ultrasound scanning of rheumatoid arthritis (RA). Here, active search is a critical component of ensuring high image quality. Furthermore, bounded contact forces between the ultrasound probe and the patient ensure patient safety and better scan quality. To the best of our knowledge, our results are both the first demonstration of safe active search on a fully autonomous robot for ultrasound scanning of rheumatoid arthritis and the first experimental evaluation of bounding contact forces in the context of medical robotics using control barrier functions. The results show that when search time is limited to less than 60 s, informative continuous expected improvement leads to a 92% success, a 13% improvement compared to expected improvement. Meanwhile, exponential control barrier functions can limit the force applied by the robot to under 5 N, even in cases where the contact boundary is specified incorrectly by -1 or +4 mm., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Vinter-Hviid, Sloth, Savarimuthu and Iturrate.)
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- 2024
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24. Occurrence of SARS-CoV-2 viremia is associated with genetic variants of genes related to COVID-19 pathogenesis.
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Roy-Vallejo E, Fernández De Córdoba-Oñate S, Delgado-Wicke P, Triguero-Martínez A, Montes N, Carracedo-Rodríguez R, Zurita-Cruz N, Marcos-Jiménez A, Lamana A, Galván-Román JM, Villapalos García G, Zubiaur P, Ciudad M, Rabes L, Sanz M, Rodríguez C, Villa A, Rodríguez JÁ, Marcos C, Hernando J, Díaz-Fernández P, Abad F, de Los Santos I, Rodríguez Serrano DA, García-Vicuña R, Suárez Fernández C, P Gomariz R, Muñoz-Calleja C, Fernández-Ruiz E, González-Álvaro I, Cardeñoso L, Barrios A, Sanz J, Casado P, Gutiérrez Á, Bautista A, Hernández P, Ruiz Giménez N, Moyano B, Gil P, Jesús Delgado M, Parra P, Sánchez B, Sáez C, Fernández Rico M, Arévalo Román C, Castañeda S, Llorente I, G Tomero E, García Castañeda N, Uriarte M, Fontán García-Rodrigo L, Domingo García D, Alarcón Cavero T, Auxiliadora Semiglia Chong M, Gutiérrez Cobos A, Sánchez-Madrid F, Martín Gayo E, Sánchez-Cerrillo I, Martínez-Fleta P, López-Sanz C, Gabrie L, Del Campo Guerola L, Tejedor R, Ancochea J, García Castillo E, Ávalos E, Sánchez-Azofra A, Alonso T, Cisneros C, Valenzuela C, Javier García Pérez F, María Girón R, Aspa J, Marcos C, Del Perpetuo Socorro Churruca M, Zamora E, Martínez A, Barrio Mayo M, Henares Espi R, Méndez R, Arribas D, Chicot Llano M, González B, Quicios B, Patiño P, Trigueros M, Dominguez Peña C, Jiménez Jiménez D, Villamayor P, Canabal A, de la Cámara R, Ortiz J, and Iturrate I
- Abstract
Introduction: SARS-CoV-2 viral load has been related to COVID-19 severity. The main aim of this study was to evaluate the relationship between SARS-CoV-2 viremia and SNPs in genes previously studied by our group as predictors of COVID-19 severity., Materials and Methods: Retrospective observational study including 340 patients hospitalized for COVID-19 in the University Hospital La Princesa between March 2020 and December 2021, with at least one viremia determination. Positive viremia was considered when viral load was above the quantifiable threshold (20 copies/ml). A total of 38 SNPs were genotyped. To study their association with viremia a multivariate logistic regression was performed., Results: The mean age of the studied population was 64.5 years (SD 16.6), 60.9% patients were male and 79.4% white non-Hispanic. Only 126 patients (37.1%) had at least one positive viremia. After adjustment by confounders, the presence of the minor alleles of rs2071746 ( HMOX1 ; T/T genotype OR 9.9 p < 0.0001), rs78958998 (probably associated with SERPING1 expression; A/T genotype OR 2.3, p = 0.04 and T/T genotype OR 12.9, p < 0.0001), and rs713400 (eQTL for TMPRSS2 ; C/T + T/T genotype OR 1.86, p = 0.10) were associated with higher risk of viremia, whereas the minor alleles of rs11052877 ( CD69 ; A/G genotype OR 0.5, p = 0.04 and G/G genotype OR 0.3, p = 0.01), rs2660 ( OAS1 ; A/G genotype OR 0.6, p = 0.08), rs896 ( VIPR1 ; T/T genotype OR 0.4, p = 0.02) and rs33980500 ( TRAF3IP2 ; C/T + T/T genotype OR 0.3, p = 0.01) were associated with lower risk of viremia., Conclusion: Genetic variants in HMOX1 (rs2071746), SERPING1 (rs78958998), TMPRSS2 (rs713400), CD69 (rs11052877), TRAF3IP2 (rs33980500), OAS1 (rs2660) and VIPR1 (rs896) could explain heterogeneity in SARS-CoV-2 viremia in our population., Competing Interests: FA, has been consultant or investigator in clinical trials sponsored by the following pharmaceutical companies: Abbott, Alter, Aptatargets, Chemo, FAES, Farmalíder, Ferrer, Galenicum, GlaxoSmithKline, Gilead, Italfarmaco, Janssen-Cilag, Kern, Normon, Novartis, Servier, Teva and Zambon. IG-Á reports grants from Instituto de Salud Carlos III, during the course of the study; personal fees from Lilly and Sanofi; personal fees and non-financial support from BMS; personal fees and non-financial support from Abbvie; research support, personal fees and non-financial support from Roche Laboratories; research support from Gebro Pharma; non-financial support from MSD, Pfizer and Novartis, not related to the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Roy-Vallejo, Fernández de Córdoba-Oñate, Delgado-Wicke, Triguero-Martínez, Montes, Carracedo-Rodríguez, Zurita-Cruz, Marcos-Jiménez, Lamana, Galván-Román, Villapalos García, Zubiaur, Ciudad, Rabes, Sanz, Rodríguez, Villa, Rodríguez, Marcos, Hernando, Díaz-Fernández, Abad, de los Santos, Rodríguez Serrano, García-Vicuña, Suárez Fernández, P. Gomariz, Muñoz-Calleja, Fernández-Ruiz, González-Álvaro Cardeñoso and the PREDINMUN-COVID Group.)
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- 2023
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25. Relevant SARS-CoV-2 viremia is associated with COVID-19 severity: Prospective cohort study and validation cohort.
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Cardeñoso Domingo L, Roy Vallejo E, Zurita Cruz ND, Chicot Llano M, Ávalos Pérez-Urria E, Barrios A, Hernando Santos J, Ortiz J, Rodríguez García SC, Martín Ramírez A, Ciudad Sañudo M, Marcos C, García Castillo E, Fontán García-Rodrigo L, González B, Méndez R, Iturrate I, Sanz García A, Villa A, Sánchez Azofra A, Quicios B, Arribas D, Álvarez Rodríguez J, Patiño P, Trigueros M, Uriarte M, Triguero Martínez A, Arévalo C, Galván Román JM, García-Vicuña R, Ancochea J, Soriano JB, Canabal A, Muñoz Calleja C, De la Cámara R, Suarez Fernández C, González Álvaro I, and Rodríguez-Serrano DA
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- Adult, Hospitalization, Humans, Prospective Studies, Retrospective Studies, SARS-CoV-2, Viremia, COVID-19 diagnosis
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Early kinetics of SARS-CoV-2 viral load (VL) in plasma determined by quantitative reverse-transcription polymerase chain reaction (RT-PCR) was evaluated as a predictor of poor clinical outcome in a prospective study and assessed in a retrospective validation cohort. Prospective observational single-center study including consecutive adult patients hospitalized with COVID-19 between November 2020 and January 2021. Serial plasma samples were obtained until discharge. Quantitative RT-PCR was performed to assess SARS-CoV-2 VL. The main outcomes were in-hospital mortality, admission to the Intensive Care Unit (ICU), and their combination (Poor Outcome). Relevant viremia (RV), established in the prospective study, was assessed in a retrospective cohort including hospitalized COVID-19 patients from April 2021 to May 2022, in which plasma samples were collected according to clinical criteria. Prospective cohort: 57 patients were included. RV was defined as at least a twofold increase in VL within ≤2 days or a VL > 300 copies/ml, in the first week. Patients with RV (N = 14; 24.6%) were more likely to die than those without RV (35.7% vs. 0%), needed ICU admission (57% vs. 0%) or had Poor Outcome (71.4% vs. 0%), (p < 0.001 for the three variables). Retrospective cohort: 326 patients were included, 18.7% presented RV. Patients with RV compared with patients without RV had higher rates of ICU-admission (odds ratio [OR]: 5.6 [95% confidence interval [CI]: 2.1-15.1); p = 0.001), mortality (OR: 13.5 [95% CI: 6.3-28.7]; p < 0.0001) and Poor Outcome (OR: 11.2 [95% CI: 5.8-22]; p < 0.0001). Relevant SARS-CoV-2 viremia in the first week of hospitalization was associated with higher in-hospital mortality, ICU admission, and Poor Outcome. Findings observed in the prospective cohort were confirmed in a larger validation cohort., (© 2022 The Authors. Journal of Medical Virology published by Wiley Periodicals LLC.)
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- 2022
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26. Voluntary Motor Command Release Coincides with Restricted Sensorimotor Beta Rhythm Phases.
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Hussain SJ, Vollmer MK, Iturrate I, and Quentin R
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- Electroencephalography, Electromyography, Female, Fingers physiology, Humans, Male, Motor Activity physiology, Beta Rhythm physiology, Motor Cortex physiology, Psychomotor Performance physiology
- Abstract
Sensory perception and memory are enhanced during restricted phases of ongoing brain rhythms, but whether voluntary movement is constrained by brain rhythm phase is not known. Voluntary movement requires motor commands to be released from motor cortex (M1) and transmitted to spinal motoneurons and effector muscles. Here, we tested the hypothesis that motor commands are preferentially released from M1 during circumscribed phases of ongoing sensorimotor rhythms. Healthy humans of both sexes performed a self-paced finger movement task during electroencephalography (EEG) and electromyography (EMG) recordings. We first estimated the time of motor command release preceding each finger movement by subtracting individually measured corticomuscular transmission latencies from EMG-determined movement onset times. Then, we determined the phase of ipsilateral and contralateral sensorimotor mu (8-12 Hz) and beta (13-35 Hz) rhythms during release of each motor command. We report that motor commands were most often released between 120 and 140° along the contralateral beta cycle but were released uniformly along the contralateral mu cycle. Motor commands were also released uniformly along ipsilateral mu and beta cycles. Results demonstrate that motor command release coincides with restricted phases of the contralateral sensorimotor beta rhythm, suggesting that sensorimotor beta rhythm phase may sculpt the timing of voluntary human movement. SIGNIFICANCE STATEMENT Perceptual and cognitive function is optimal during specific brain rhythm phases. Although brain rhythm phase influences motor cortical neuronal activity and communication between the motor cortex and spinal cord, its role in voluntary movement is poorly understood. Here, we show that the motor commands needed to produce voluntary movements are preferentially released from the motor cortex during contralateral sensorimotor beta rhythm phases. Our findings are consistent with the notion that sensorimotor rhythm phase influences the timing of voluntary human movement., (Copyright © 2022 the authors.)
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- 2022
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27. SARS-CoV-2 Viremia Precedes an IL6 Response in Severe COVID-19 Patients: Results of a Longitudinal Prospective Cohort.
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Roy-Vallejo E, Cardeñoso L, Triguero-Martínez A, Chicot Llano M, Zurita N, Ávalos E, Barrios A, Hernando J, Ortiz J, Rodríguez-García SC, Ciudad Sañudo M, Marcos C, García Castillo E, Fontán García-Rodrigo L, González B, Méndez R, Iturrate I, Sanz-García A, Villa A, Sánchez-Azofra A, Quicios B, Arribas D, Álvarez Rodríguez J, Patiño P, Trigueros M, Uriarte M, Martín-Ramírez A, Arévalo Román C, Galván-Román JM, García-Vicuña R, Ancochea J, Muñoz-Calleja C, Fernández-Ruiz E, de la Cámara R, Suárez Fernández C, González-Álvaro I, and Rodríguez-Serrano DA
- Abstract
Background: Interleukin 6 (IL6) levels and SARS-CoV-2 viremia have been correlated with COVID-19 severity. The association over time between them has not been assessed in a prospective cohort. Our aim was to evaluate the relationship between SARS-CoV-2 viremia and time evolution of IL6 levels in a COVID-19 prospective cohort., Methods: Secondary analysis from a prospective cohort including COVID-19 hospitalized patients from Hospital Universitario La Princesa between November 2020 and January 2021. Serial plasma samples were collected from admission until discharge. Viral load was quantified by Real-Time Polymerase Chain Reaction and IL6 levels with an enzyme immunoassay. To represent the evolution over time of both variables we used the graphic command twoway of Stata., Results: A total of 57 patients were recruited, with median age of 63 years (IQR [53-81]), 61.4% male and 68.4% Caucasian. The peak of viremia appeared shortly after symptom onset in patients with persistent viremia (more than 1 sample with > 1.3 log10 copies/ml) and also in those with at least one IL6 > 30 pg/ml, followed by a progressive increase in IL6 around 10 days later. Persistent viremia in the first week of hospitalization was associated with higher levels of IL6. Both IL6 and SARS-CoV-2 viral load were higher in males, with a quicker increase with age., Conclusion: In those patients with worse outcomes, an early peak of SARS-CoV-2 viral load precedes an increase in IL6 levels. Monitoring SARS-CoV-2 viral load during the first week after symptom onset may be helpful to predict disease severity in COVID-19 patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Roy-Vallejo, Cardeñoso, Triguero-Martínez, Chicot Llano, Zurita, Ávalos, Barrios, Hernando, Ortiz, Rodríguez-García, Ciudad Sañudo, Marcos, García Castillo, Fontán García-Rodrigo, González, Méndez, Iturrate, Sanz-García, Villa, Sánchez-Azofra, Quicios, Arribas, Álvarez Rodríguez, Patiño, Trigueros, Uriarte, Martín-Ramírez, Arévalo Román, Galván-Román, García-Vicuña, Ancochea, Muñoz-Calleja, Fernández-Ruiz, de la Cámara, Suárez Fernández, González-Álvaro, Rodríguez-Serrano and the PREDINMUN-COVID Group.)
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- 2022
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28. Robotic Assembly of Timber Structures in a Human-Robot Collaboration Setup.
- Author
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Kramberger A, Kunic A, Iturrate I, Sloth C, Naboni R, and Schlette C
- Abstract
The construction sector is investigating wood as a highly sustainable material for fabrication of architectural elements. Several researchers in the field of construction are currently designing novel timber structures as well as novel solutions for fabricating such structures, i.e. robot technologies which allow for automation of a domain dominated by skilled craftsman. In this paper, we present a framework for closing the loop between the design and robotic assembly of timber structures. On one hand, we illustrate an extended automation process that incorporates learning by demonstration to learn and execute a complex assembly of an interlocking wooden joint. On the other hand, we describe a design case study that builds upon the specificity of this process, to achieve new designs of construction elements, which were previously only possible to be assembled by skilled craftsmen. The paper provides an overview of a process with different levels of focus, from the integration of a digital twin to timber joint design and the robotic assembly execution, to the development of a flexible robotic setup and novel assembly procedures for dealing with the complexity of the designed timber joints. We discuss synergistic results on both robotic and construction design innovation, with an outlook on future developments., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor declared a past co-authorship with one of the authors AKr., (Copyright © 2022 Kramberger, Kunic, Iturrate, Sloth, Naboni and Schlette.)
- Published
- 2022
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29. Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration.
- Author
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Iturrate I, Kramberger A, and Sloth C
- Abstract
This paper presents a framework for programming in-contact tasks using learning by demonstration. The framework is demonstrated on an industrial gluing task, showing that a high quality robot behavior can be programmed using a single demonstration. A unified controller structure is proposed for the demonstration and execution of in-contact tasks that eases the transition from admittance controller for demonstration to parallel force/position control for the execution. The proposed controller is adapted according to the geometry of the task constraints, which is estimated online during the demonstration. In addition, the controller gains are adapted to the human behavior during demonstration to improve the quality of the demonstration. The considered gluing task requires the robot to alternate between free motion and in-contact motion; hence, an approach for minimizing contact forces during the switching between the two situations is presented. We evaluate our proposed system in a series of experiments, where we show that we are able to estimate the geometry of a curved surface, that our adaptive controller for demonstration allows users to achieve higher accuracy in a shorter demonstration duration when compared to an off-the-shelf controller for teaching implemented on a collaborative robot, and that our execution controller is able to reduce impact forces and apply a constant process force while adapting to the surface geometry., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Iturrate, Kramberger and Sloth.)
- Published
- 2021
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30. Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states.
- Author
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Iwane F, Iturrate I, Chavarriaga R, and Millán JDR
- Subjects
- Brain, Electroencephalography, Feedback, Humans, Brain-Computer Interfaces
- Abstract
Objective. When humans perceive an erroneous action, an EEG error-related potential (ErrP) is elicited as a neural response. ErrPs have been largely investigated in discrete feedback protocols, where actions are executed at discrete steps, to enable seamless brain-computer interaction. However, there are only a few studies that investigate ErrPs in continuous feedback protocols. The objective of the present study is to better understand the differences between two types of ErrPs elicited during continuous feedback protocols, where errors may occur either at predicted or unpredicted states. We hypothesize that ErrPs of the unpredicted state is associated with longer latency as it requires higher cognitive workload to evaluate actions compared to the predicted states. Approach. Participants monitored the trajectory of an autonomous cursor that occasionally made erroneous actions on its way to the target in two conditions, namely, predicted or unpredicted states. After characterizing the ErrP waveform elicited by erroneous actions in the two conditions, we performed single-trial decoding of ErrPs in both synchronous (i.e. time-locked to the onset of the erroneous action) and asynchronous manner. Furthermore, we explored the possibility to transfer decoders built with data of one of the conditions to the other condition. Main results. As hypothesized, erroneous actions at unpredicted states gave rise to ErrPs with higher latency than erroneous actions at predicted states, a correlate of higher cognitive effort in the former condition. Moreover, ErrP decoders trained in a given condition successfully transferred to the other condition with a slight loss of classification performance. This was the case for synchronous as well as asynchronous ErrP decoding, showing the invariability of ErrPs across conditions. Significance. These results advance the characterization of ErrPs during continuous feedback protocols, enlarging the potential use of ErrPs during natural operation of brain-controlled devices as it is not necessary to have different decoders for each kind of erroneous conditions., (© 2021 IOP Publishing Ltd.)
- Published
- 2021
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31. Angioimmunoblastic T-cell lymphoma after acute myeloid leukemia: Alleged common pathogenesis. A case report and literature review.
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Iturrate I, Loscertales J, Fernández-Ruiz E, Muñoz P, López C, Del Campo L, Muñoz C, and Alegre A
- Abstract
The genomic landscape of AITL is characterized by mutation of epigenetic modifiers. This gene expression pattern resembles myeloid diseases and shows a potential role for hypomethylating agents as possible therapy for AITL., Competing Interests: The authors have no conflicts of interest to declare., (© 2020 The Authors. Clinical Case Reports published by John Wiley & Sons Ltd.)
- Published
- 2020
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32. Can COVID-19 cause severe neutropenia?
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López-Pereira P, Iturrate I, de La Cámara R, Cardeñoso L, Alegre A, and Aguado B
- Abstract
This is the first case of acquired severe neutropenia in the context of COVID-19 reported to date. This could illustrate another less frequent hematological disorder related to this novel viral infection., Competing Interests: The authors declare no conflicts of interest., (© 2020 The Authors. Clinical Case Reports published by John Wiley & Sons Ltd.)
- Published
- 2020
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33. Mechanisms of offline motor learning at a microscale of seconds in large-scale crowdsourced data.
- Author
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Bönstrup M, Iturrate I, Hebart MN, Censor N, and Cohen LG
- Abstract
Performance improvements during early human motor skill learning are suggested to be driven by short periods of rest during practice, at the scale of seconds. To reveal the unknown mechanisms behind these "micro-offline" gains, we leveraged the sampling power offered by online crowdsourcing (cumulative N over all experiments = 951). First, we replicated the original in-lab findings, demonstrating generalizability to subjects learning the task in their daily living environment ( N = 389). Second, we show that offline improvements during rest are equivalent when significantly shortening practice period duration, thus confirming that they are not a result of recovery from performance fatigue ( N = 118). Third, retroactive interference immediately after each practice period reduced the learning rate relative to interference after passage of time ( N = 373), indicating stabilization of the motor memory at a microscale of several seconds. Finally, we show that random termination of practice periods did not impact offline gains, ruling out a contribution of predictive motor slowing ( N = 71). Altogether, these results demonstrate that micro-offline gains indicate rapid, within-seconds consolidation accounting for early skill learning., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2020.)
- Published
- 2020
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34. Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging.
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Pereira M, Faivre N, Iturrate I, Wirthlin M, Serafini L, Martin S, Desvachez A, Blanke O, Van De Ville D, and Millán JDR
- Subjects
- Adult, Decision Making, Electroencephalography, Female, Humans, Male, Metacognition, Multimodal Imaging, Prefrontal Cortex diagnostic imaging, Young Adult, Judgment, Prefrontal Cortex physiology
- Abstract
The human capacity to compute the likelihood that a decision is correct-known as metacognition-has proven difficult to study in isolation as it usually cooccurs with decision making. Here, we isolated postdecisional from decisional contributions to metacognition by analyzing neural correlates of confidence with multimodal imaging. Healthy volunteers reported their confidence in the accuracy of decisions they made or decisions they observed. We found better metacognitive performance for committed vs. observed decisions, indicating that committing to a decision may improve confidence. Relying on concurrent electroencephalography and hemodynamic recordings, we found a common correlate of confidence following committed and observed decisions in the inferior frontal gyrus and a dissociation in the anterior prefrontal cortex and anterior insula. We discuss these results in light of decisional and postdecisional accounts of confidence and propose a computational model of confidence in which metacognitive performance naturally improves when evidence accumulation is constrained upon committing a decision., Competing Interests: The authors declare no competing interest.
- Published
- 2020
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35. The Confidence Database.
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Rahnev D, Desender K, Lee ALF, Adler WT, Aguilar-Lleyda D, Akdoğan B, Arbuzova P, Atlas LY, Balcı F, Bang JW, Bègue I, Birney DP, Brady TF, Calder-Travis J, Chetverikov A, Clark TK, Davranche K, Denison RN, Dildine TC, Double KS, Duyan YA, Faivre N, Fallow K, Filevich E, Gajdos T, Gallagher RM, de Gardelle V, Gherman S, Haddara N, Hainguerlot M, Hsu TY, Hu X, Iturrate I, Jaquiery M, Kantner J, Koculak M, Konishi M, Koß C, Kvam PD, Kwok SC, Lebreton M, Lempert KM, Ming Lo C, Luo L, Maniscalco B, Martin A, Massoni S, Matthews J, Mazancieux A, Merfeld DM, O'Hora D, Palser ER, Paulewicz B, Pereira M, Peters C, Philiastides MG, Pfuhl G, Prieto F, Rausch M, Recht S, Reyes G, Rouault M, Sackur J, Sadeghi S, Samaha J, Seow TXF, Shekhar M, Sherman MT, Siedlecka M, Skóra Z, Song C, Soto D, Sun S, van Boxtel JJA, Wang S, Weidemann CT, Weindel G, Wierzchoń M, Xu X, Ye Q, Yeon J, Zou F, and Zylberberg A
- Subjects
- Adult, Choice Behavior physiology, Datasets as Topic statistics & numerical data, Humans, Reaction Time physiology, Databases, Factual statistics & numerical data, Mental Processes physiology, Metacognition physiology, Psychometrics instrumentation, Psychometrics statistics & numerical data, Task Performance and Analysis
- Abstract
Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.
- Published
- 2020
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36. General principles of machine learning for brain-computer interfacing.
- Author
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Iturrate I, Chavarriaga R, and Millán JDR
- Subjects
- Algorithms, Electroencephalography methods, Humans, Brain physiology, Brain-Computer Interfaces, Machine Learning, Signal Processing, Computer-Assisted
- Abstract
Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair, typewriting applications, or games directly by modulating our brain activity. For this purpose, BCI systems rely on signal processing and machine learning algorithms to decode the brain activity. This chapter provides an overview of the main steps required to do such a process, including signal preprocessing, feature extraction and selection, and decoding. Given the large amount of possible methods that can be used for these processes, a comprehensive review of them is beyond the scope of this chapter, and it is focused instead on the general principles that should be taken into account, as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems., (© 2020 Elsevier B.V. All rights reserved.)
- Published
- 2020
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37. A Rapid Form of Offline Consolidation in Skill Learning.
- Author
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Bönstrup M, Iturrate I, Thompson R, Cruciani G, Censor N, and Cohen LG
- Subjects
- Adult, Female, Humans, Male, Learning, Memory Consolidation, Motor Skills, Psychomotor Performance
- Abstract
The brain strengthens memories through consolidation, defined as resistance to interference (stabilization) or performance improvements between the end of a practice session and the beginning of the next (offline gains) [1]. Typically, consolidation has been measured hours or days after the completion of training [2], but the same concept may apply to periods of rest that occur interspersed in a series of practice bouts within the same session. Here, we took an unprecedented close look at the within-seconds time course of early human procedural learning over alternating short periods of practice and rest that constitute a typical online training session. We found that performance did not markedly change over short periods of practice. On the other hand, performance improvements in between practice periods, when subjects were at rest, were significant and accounted for early procedural learning. These offline improvements were more prominent in early training trials when the learning curve was steep and no performance decrements during preceding practice periods were present. At the neural level, simultaneous magnetoencephalographic recordings showed an anatomically defined signature of this phenomenon. Beta-band brain oscillatory activity in a predominantly contralateral frontoparietal network predicted rest-period performance improvements. Consistent with its role in sensorimotor engagement [3], modulation of beta activity may reflect replay of task processes during rest periods. We report a rapid form of offline consolidation that substantially contributes to early skill learning and may extend the concept of consolidation to a time scale in the order of seconds, rather than the hours or days traditionally accepted., (Published by Elsevier Ltd.)
- Published
- 2019
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38. Differential contributions of subthalamic beta rhythms and 1/f broadband activity to motor symptoms in Parkinson's disease.
- Author
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Martin S, Iturrate I, Chavarriaga R, Leeb R, Sobolewski A, Li AM, Zaldivar J, Peciu-Florianu I, Pralong E, Castro-Jiménez M, Benninger D, Vingerhoets F, Knight RT, Bloch J, and Millán JDR
- Abstract
Excessive beta oscillatory activity in the subthalamic nucleus (STN) is linked to Parkinson's Disease (PD) motor symptoms. However, previous works have been inconsistent regarding the functional role of beta activity in untreated Parkinsonian states, questioning such role. We hypothesized that this inconsistency is due to the influence of electrophysiological broadband activity -a neurophysiological indicator of synaptic excitation/inhibition ratio- that could confound measurements of beta activity in STN recordings. Here we propose a data-driven, automatic and individualized mathematical model that disentangles beta activity and 1/f broadband activity in the STN power spectrum, and investigate the link between these individual components and motor symptoms in thirteen Parkinsonian patients. We show, using both modeled and actual data, how beta oscillatory activity significantly correlates with motor symptoms (bradykinesia and rigidity) only when broadband activity is not considered in the biomarker estimations, providing solid evidence that oscillatory beta activity does correlate with motor symptoms in untreated PD states as well as the significant impact of broadband activity. These findings emphasize the importance of data-driven models and the identification of better biomarkers for characterizing symptom severity and closed-loop applications., Competing Interests: The authors declare no competing interests.
- Published
- 2018
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39. Human EEG reveals distinct neural correlates of power and precision grasping types.
- Author
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Iturrate I, Chavarriaga R, Pereira M, Zhang H, Corbet T, Leeb R, and Millán JDR
- Subjects
- Adult, Brain-Computer Interfaces, Electromyography methods, Electrooculography methods, Female, Hand Strength physiology, Humans, Male, Young Adult, Electroencephalography methods, Functional Neuroimaging methods, Hand physiology, Motor Activity physiology, Motor Cortex physiology, Nerve Net physiology, Parietal Lobe physiology, Prefrontal Cortex physiology, Psychomotor Performance physiology
- Abstract
Hand grasping is a sophisticated motor task that has received much attention by the neuroscientific community, which demonstrated how grasping activates a network involving parietal, pre-motor and motor cortices using fMRI, ECoG, LFPs and spiking activity. Yet, there is a need for a more precise spatio-temporal analysis as it is still unclear how these brain activations over large cortical areas evolve at the sub-second level. In this study, we recorded ten human participants (1 female) performing visually-guided, self-paced reaching and grasping with precision or power grips. Following the results, we demonstrate the existence of neural correlates of grasping from broadband EEG in self-paced conditions and show how neural correlates of precision and power grasps differentially evolve as grasps unfold. 100 ms before the grasp is secured, bilateral parietal regions showed increasingly differential patterns. Afterwards, sustained differences between both grasps occurred over the bilateral motor and parietal regions, and medial pre-frontal cortex. Furthermore, these differences were sufficiently discriminable to allow single-trial decoding with 70% decoding performance. Functional connectivity revealed differences at the network level between grasps in fronto-parietal networks, in terms of upper-alpha cortical oscillatory power with a strong involvement of ipsilateral hemisphere. Our results supported the existence of fronto-parietal recurrent feedback loops, with stronger interactions for precision grips due to the finer motor control required for this grasping type., (Copyright © 2018 Elsevier Inc. All rights reserved.)
- Published
- 2018
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40. Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance.
- Author
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Corbet T, Iturrate I, Pereira M, Perdikis S, and Millán JDR
- Subjects
- Adult, Brain-Computer Interfaces, Cortical Synchronization physiology, Electroencephalography, Female, Humans, Male, Sensorimotor Cortex physiology, Axons physiology, Electric Stimulation methods, Feedback, Sensory physiology, Imagination physiology, Kinesthesis physiology, Motor Activity physiology, Motor Neurons physiology, Sensory Receptor Cells physiology, Sensory Thresholds physiology
- Abstract
Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable for many subjects. It has been suggested that the usage of somatosensory feedback would be more suitable than standardly used visual feedback to enhance MI brain patterns. However, somatosensory feedback should not interfere with the recorded MI brain pattern. In this study we propose a novel feedback modality to guide subjects during MI based on sensory threshold neuromuscular electrical stimulation (St-NMES). St-NMES depolarizes sensory and motor axons without eliciting any muscular contraction. We hypothesize that St-NMES does not induce detectable ERD brain patterns and fosters MI performance. Twelve novice subjects were included in a cross-over design study. We recorded their EEG, comparing St-NMES with visual feedback during MI or resting tasks. We found that St-NMES not only induced significantly larger desynchronization over sensorimotor areas (p<0.05) but also significantly enhanced MI brain connectivity patterns. Moreover, classification accuracy and stability were significantly higher with St-NMES. Importantly, St-NMES alone did not induce detectable artifacts, but rather the changes in the detected patterns were due to an increased MI performance. Our findings indicate that St-NMES is a promising feedback in order to foster MI performance and cold be used for BMI online applications., (Copyright © 2018 Elsevier Inc. All rights reserved.)
- Published
- 2018
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41. Decoding Inner Speech Using Electrocorticography: Progress and Challenges Toward a Speech Prosthesis.
- Author
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Martin S, Iturrate I, Millán JDR, Knight RT, and Pasley BN
- Abstract
Certain brain disorders resulting from brainstem infarcts, traumatic brain injury, cerebral palsy, stroke, and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders would benefit from a system that can infer internal speech directly from brain signals. In this review article, we describe the state of the art in decoding inner speech, ranging from early acoustic sound features, to higher order speech units. We focused on intracranial recordings, as this technique allows monitoring brain activity with high spatial, temporal, and spectral resolution, and therefore is a good candidate to investigate inner speech. Despite intense efforts, investigating how the human cortex encodes inner speech remains an elusive challenge, due to the lack of behavioral and observable measures. We emphasize various challenges commonly encountered when investigating inner speech decoding, and propose potential solutions in order to get closer to a natural speech assistive device.
- Published
- 2018
- Full Text
- View/download PDF
42. Corrigendum: Word pair classification during imagined speech using direct brain recordings.
- Author
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Martin S, Brunner P, Iturrate I, Millán JD, Schalk G, Knight RT, and Pasley BN
- Published
- 2017
- Full Text
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43. Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future.
- Author
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Huggins JE, Guger C, Ziat M, Zander TO, Taylor D, Tangermann M, Soria-Frisch A, Simeral J, Scherer R, Rupp R, Ruffini G, Robinson DKR, Ramsey NF, Nijholt A, Müller-Putz G, McFarland DJ, Mattia D, Lance BJ, Kindermans PJ, Iturrate I, Herff C, Gupta D, Do AH, Collinger JL, Chavarriaga R, Chase SM, Bleichner MG, Batista A, Anderson CW, and Aarnoutse EJ
- Abstract
The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.
- Published
- 2017
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44. Word pair classification during imagined speech using direct brain recordings.
- Author
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Martin S, Brunner P, Iturrate I, Millán Jdel R, Schalk G, Knight RT, and Pasley BN
- Subjects
- Acoustic Stimulation, Auditory Perception physiology, Discrimination, Psychological, Electrodes, Gamma Rhythm physiology, Humans, ROC Curve, Time Factors, Brain physiology, Brain Mapping, Electroencephalography, Imagination, Speech, Vocabulary
- Abstract
People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70-150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.
- Published
- 2016
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- View/download PDF
45. Analysis and asynchronous detection of gradually unfolding errors during monitoring tasks.
- Author
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Omedes J, Iturrate I, Minguez J, and Montesano L
- Subjects
- Adult, Attention physiology, Humans, Machine Learning, Male, Pattern Recognition, Automated methods, Reproducibility of Results, Sensitivity and Specificity, Brain physiology, Cognition physiology, Electroencephalography methods, Motion Perception physiology, Reaction Time physiology, Task Performance and Analysis
- Abstract
Human studies on cognitive control processes rely on tasks involving sudden-onset stimuli, which allow the analysis of these neural imprints to be time-locked and relative to the stimuli onset. Human perceptual decisions, however, comprise continuous processes where evidence accumulates until reaching a boundary. Surpassing the boundary leads to a decision where measured brain responses are associated to an internal, unknown onset. The lack of this onset for gradual stimuli hinders both the analyses of brain activity and the training of detectors. This paper studies electroencephalographic (EEG)-measurable signatures of human processing for sudden and gradual cognitive processes represented as a trajectory mismatch under a monitoring task. Time-locked potentials and brain-source analysis of the EEG of sudden mismatches revealed the typical components of event-related potentials and the involvement of brain structures related to cognitive control processing. For gradual mismatch events, time-locked analyses did not show any discernible EEG scalp pattern, despite related brain areas being, to a lesser extent, activated. However, and thanks to the use of non-linear pattern recognition algorithms, it is possible to train an asynchronous detector on sudden events and use it to detect gradual mismatches, as well as obtaining an estimate of their unknown onset. Post-hoc time-locked scalp and brain-source analyses revealed that the EEG patterns of detected gradual mismatches originated in brain areas related to cognitive control processing. This indicates that gradual events induce latency in the evaluation process but that similar brain mechanisms are present in sudden and gradual mismatch events. Furthermore, the proposed asynchronous detection model widens the scope of applications of brain-machine interfaces to other gradual processes.
- Published
- 2015
- Full Text
- View/download PDF
46. Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control.
- Author
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Iturrate I, Chavarriaga R, Montesano L, Minguez J, and Millán Jdel R
- Subjects
- Adult, Electroencephalography, Female, Humans, Learning, Male, Movement, Psychomotor Performance, Young Adult, Brain physiology, Brain-Computer Interfaces
- Abstract
Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct, and exploits the brain correlates of this assessment to learn suitable motor behaviours. Results show that, after a short user's training period, this teaching BMI paradigm operated three different neuroprostheses and generalized across several targets. Our results further support that these error-related signals reflect a task-independent monitoring mechanism in the brain, making this teaching paradigm scalable. We anticipate this BMI approach to become a key component of any neuroprosthesis that mimics natural motor control as it enables continuous adaptation in the absence of explicit information about goals. Furthermore, our paradigm can seamlessly incorporate other cognitive signals and conventional neuroprosthetic approaches, invasive or non-invasive, to enlarge the range and complexity of tasks that can be accomplished.
- Published
- 2015
- Full Text
- View/download PDF
47. Detecting intention to grasp during reaching movements from EEG.
- Author
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Randazzo L, Iturrate I, Chavarriaga R, Leeb R, and Del Millan JR
- Subjects
- Brain-Computer Interfaces, Electroencephalography, Hand Strength, Humans, Movement, Intention
- Abstract
Brain-computer interfaces (BCI) have been shown to be a promising tool in rehabilitation and assistive scenarios. Within these contexts, brain signals can be decoded and used as commands for a robotic device, allowing to translate user's intentions into motor actions in order to support the user's impaired neuro-muscular system. Recently, it has been suggested that slow cortical potentials (SCPs), negative deflections in the electroencephalographic (EEG) signals peaking around one second before the initiation of movements, might be of interest because they offer an accurate time resolution for the provided feedback. Many state-of-the-art studies exploiting SCPs have focused on decoding intention of movements related to walking and arm reaching, but up to now few studies have focused on decoding the intention to grasp, which is of fundamental importance in upper-limb tasks. In this work, we present a technique that exploits EEG to decode grasping correlates during reaching movements. Results obtained with four subjects show the existence of SCPs prior to the execution of grasping movements and how they can be used to classify, with accuracy rates greater than 70% across all subjects, the intention to grasp. Using a sliding window approach, we have also demonstrated how this intention can be decoded on average around 400 ms before the grasp movements for two out of four subjects, and after the onset of grasp itself for the two other subjects.
- Published
- 2015
- Full Text
- View/download PDF
48. Decoding fast-paced error-related potentials in monitoring protocols.
- Author
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Chavarriaga R, Iturrate I, Wannebroucq Q, and Del Millan JR
- Subjects
- Brain, Brain Mapping, Electroencephalography, Feedback, Evoked Potentials
- Abstract
Error-related EEG potentials (ErrP) can be used for brain-machine interfacing (BMI). Decoding of these signals, indicating subject's perception of erroneous system decisions or actions can be used to correct these actions or to improve the overall interfacing system. Multiple studies have shown the feasibility of decoding these potentials in single-trial using different types of experimental protocols and feedback modalities. However, previously reported approaches are limited by the use of long inter-stimulus intervals (ISI > 2 s). In this work we assess if it is possible to overcome this limitation. Our results show that it is possible to decode error-related potentials elicited by stimuli presented with ISIs lower than 1 s without decrease in performance. Furthermore, the increase in the presentation rate did not increase the subject workload. This suggests that the presentation rate for ErrP-based BMI protocols using serial monitoring paradigms can be substantially increased with respect to previous works.
- Published
- 2015
- Full Text
- View/download PDF
49. Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials.
- Author
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Iturrate I, Grizou J, Omedes J, Oudeyer PY, Lopes M, and Montesano L
- Subjects
- Adult, Calibration, Humans, Likelihood Functions, Algorithms, Brain physiology, Brain-Computer Interfaces standards, Evoked Potentials
- Abstract
This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.
- Published
- 2015
- Full Text
- View/download PDF
50. Brain connectivity in continuous error tasks.
- Author
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Omedes J, Iturrate I, and Montesano L
- Subjects
- Algorithms, Electrodes, Electroencephalography, Humans, Brain physiology, Brain-Computer Interfaces, Evoked Potentials physiology
- Abstract
Error-related potentials (ErrP) have been recently incorporated in brain-machine interfaces (BMIs) due to its ability to adapt and correct both the output of the BMI or the behavior of the machine. Most of these applications rely on synchronous tasks with different user's evaluations associated to correct and wrong events. Asynchronous detection during the continuous evaluation of the task, however, has to cope with background noise and an increased number of misdetections common in event-related potential detection. This paper studies a different characteristic that may carry additional information to be exploited by asynchronous ErrP detectors: brain connectivity coherence patterns appearing while the user monitors the continuous operation of a device. The results obtained with five subject revealed the presence of an error potential in an asynchronous reaching task an showed an increase in the coherency within the theta band.
- Published
- 2014
- Full Text
- View/download PDF
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