Nuestros investigadores

Javier Díaz Dorronsoro

Publicaciones científicas más recientes (desde 2010)

Autores: Shirota, C.; Jansa, J.; Díaz, Javier; et al.
Revista: JOURNAL OF NEUROENGINEERING AND REHABILITATION
ISSN 1743-0003  Vol. 13  2016 
Well-developed coordination of the upper extremities is critical for function in everyday life. Interlimb coordination is an intuitive, yet subjective concept that refers to spatio-temporal relationships between kinematic, kinetic and physiological variables of two or more limbs executing a motor task with a common goal. While both the clinical and neuroscience communities agree on the relevance of assessing and quantifying interlimb coordination, rehabilitation engineers struggle to translate the knowledge and needs of clinicians and neuroscientists into technological devices for the impaired. The use of ambiguous definitions in the scientific literature, and lack of common agreement on what should be measured, present large barriers to advancements in this area. Here, we present the different definitions and approaches to assess and quantify interlimb coordination in the clinic, in motor control studies, and by state-of-the-art robotic devices. We then propose a taxonomy of interlimb activities and give recommendations for future neuroscience-based robotic-and sensor-based assessments of upper limb function that are applicable to the everyday clinical practice. We believe this is the first step towards our long-term goal of unifying different fields and help the generation of more consistent and effective tools for neurorehabilitation.
Autores: Martínez, Martín; Loayza, Francis Roderich; et al.
Revista: IEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN 0278-0062  Vol. 33  Nº 5  2014  págs. 1044 - 1053
Repetitive and alternating lower limb movements are a specific component of human gait. Due to technical challenges, the neural mechanisms underlying such movements have not been previously studied with functional magnetic resonance imaging. In this study, we present a novel treadmill device employed to investigate the kinematics and the brain activation patterns involved in alternating and repetitive movements of the lower limbs. Once inside the scanner, 19 healthy subjects were guided by two visual cues and instructed to perform a motor task which involved repetitive and alternating movements of both lower limbs while selecting their individual comfortable amplitude on the treadmill. The device facilitated the performance of coordinated stepping while registering the concurrent lower-limb displacements, which allowed us to quantify some movement primary kinematic features such as amplitude and frequency. During stepping, significant blood oxygen level dependent signal increases were observed bilaterally in primary and secondary sensorimotor cortex, the supplementary motor area, premotor cortex, prefrontal cortex, superior and inferior parietal lobules, putamen and cerebellum, regions that are known to be involved in lower limb motor control. Brain activations related to individual adjustments during motor performance were identified in a right lateralized network including striatal, extrastriatal, and fronto-parietal areas.
Autores: Rubio, A; de Nó, Joaquín Juan; et al.
Revista: EXPERT SYSTEMS WITH APPLICATIONS
ISSN 0957-4174  Vol. 41  Nº 11  2014  págs. 5190 - 5200
The objective of this research is to select a reduced group of surface electromyographic (sEMG) channels and signal-features that is able to provide an accurate classification rate in a myoelectric control system for any user. To that end, the location of 32 sEMG electrodes placed around-along the forearm and 86 signal-features are evaluated simultaneously in a static-hand gesture classification task (14 different gestures). A novel multivariate variable selection filter method named mRMR-FCO is presented as part of the selection process. This process finds the most informative and least redundant combination of sEMG channels and signal-features among all the possible ones. The performance of the selected set of channels and signal-features is evaluated with a Support Vector Machine classifier. (C) 2014 Elsevier Ltd. All rights reserved.
Autores: Sanchez, E.; Toro, C.; et al.
Revista: CYBERNETICS AND SYSTEMS
ISSN 0196-9722  Vol. 45  Nº 2  2014  págs. 92 - 108
In this article we present the design and implementation of a mobile cardiac monitoring system oriented to patients in Phase II and III of cardiac rehabilitation. The complete monitoring system involves both hardware and software design perspectives. At the hardware level, we present a T-shirt with a 12-lead ECG system and an embedded inertial sensor for the monitoring of activity and energy expenditure. At the software level, a modular cloud platform performs data processing to detect relevant cardiac events and to provide advanced visualization capabilities. As a case study, we have implemented our system at the Cardiac Rehabilitation program at Donostia University Hospital (Spain). Finally, the validation of the 12-lead ECG recording system is also presented and discussed.
Autores: Iyer, D.; Díaz, Javier; Zouridakis, G.;
Revista: JOURNAL OF NEUROSCIENCE METHODS
ISSN 0165-0270  Vol. 208  Nº 2  2012  págs. 173 - 180
The structure and distribution of the sources underlying the generation of evoked potentials (EPs) is often very complex. In an effort to improve localization accuracy of the auditory N100 (negative response occurring around 100 ms poststimulus) component, we analyzed 13 datasets of single-trial EPs obtained from normal subjects using an iterative independent component analysis procedure which allowed us to detect a clear N100 component in each single trial and to study gross changes in component morphology across trials. We found that single-trial N100 amplitude was most often negative in polarity, as expected, but occasionally exhibited a marked reversal to become positive. The average N100, however, showed the typical negative polarity, in all subjects. Based on this observation, we separated the processed single trials in two groups of typical and aberrant responses, and from each group, we computed a partial EP that was used to localize the underlying intracranial sources. Additionally, we localized the classical ensemble average EP. Before processing, the N100 sources were identified correctly in the primary auditory cortex in only four datasets, while after processing, all 13 datasets yielded correct localizations, and the confidence volume of the sources improved by about 80%. Further analysis demonstrated that in nine datasets the improvement was mostly due to the typical responses, while the aberrant responses had an antagonistic effect.
Autores: Zouridakis, G.; Iyer, D.; Díaz, Javier;
Libro:  Modern Electroencephalographic Assessment Techniques: Theory and Applications. Neutomethods Book Series
Vol. 91  2015  págs. 231 - 234
In the quest for neurophysiological biomarkers that uniquely characterize schizophrenia subjects, auditory evoked potentials (EPs) have been extensively used during the past several decades. Typically, EPs are estimated using ensemble averaging to obtain robust components. Averaging, however, eliminates all temporal variability of the recorded signals and, therefore, hampers the study of the brain temporal dynamics underlying the generation of EP components. In this chapter, we present a methodology for analyzing EPs on a single-trial basis using an iterative independent component analysis procedure. The method is capable of identifying and measuring the amplitude, latency, and overall morphology of individual EP components in single trials and, as such, permits the study of phase characteristics among single trials while preserving known features of the average EPs. Recordings from schizophrenia patients and normal controls demonstrate that activity phase synchronization plays a crucial role in EP generation and explains the sensory gating deficits observed in schizophrenia subjects. Furthermore, the findings from this method are very robust across recordings from different labs and experimental protocols and can be used to separate schizophrenia patients from normal controls with 100 % classification accuracy.