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José del Rocio Millán Ruiz

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Publications associées (230)

Decoding Inner Speech Using Electrocorticography: Progress and Challenges Toward a Speech Prosthesis

José del Rocio Millán Ruiz, Stéphanie Martin, Inaki Asier Iturrate Gil

2018

ROS-Health: An Open-Source Framework for Neurorobotics

José del Rocio Millán Ruiz, Luca Tonin

This paper aims at providing a preliminary description of ROS-Health, a novel framework for neurorobotics based on the middleware Robot Operating System (ROS). The increased interest in the neurorobotics field and the proliferation of several (neuro) physi ...

IEEE2018

Motor Attempt EEG Paradigm As a Diagnostic Tool for Disorders of Consciousness

José del Rocio Millán Ruiz, Serafeim Perdikis, Christoph Schneider

To investigate whether a motor attempt EEG paradigm coupled with functional electrical stimulation can detect command following and, therefore, signs of conscious awareness in patients with disorders of consciousness, we recorded nine patients admitted to ...

2018

Using Robust Principal Component Analysis to Reduce EEG Intra-Trial Variability

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Ping-Keng Jao

Practical brain-computer interfaces need to overcome several challenges, including tolerance to signal variability within- and across sessions. We introduce Robust Principal Component Analysis (RPCA) as a potential approach to tackle intra-trial variabilit ...

2018

Neural encoding of auditory features during music perception and imagery

José del Rocio Millán Ruiz, Stéphanie Martin

Despite many behavioral and neuroimaging investigations, it remains unclear how the human cortex represents spectrotemporal sound features during auditory imagery, and how this representation compares to auditory perception. To assess this, we recorded ele ...

2018

Analysis of EEG Correlates of Perceived Difficulty in Dynamically Changing Flying Tasks

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Ping-Keng Jao

Real-time workload estimation can be an important tool to improve human-machine interaction. Despite multiple efforts in this sense, most studies focused on tasks designed to have extreme conditions of workload (high vs low), and these levels remain rather ...

IEEE2018

mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation

José del Rocio Millán Ruiz, Serafeim Perdikis, Inaki Asier Iturrate Gil, Luca Randazzo

Hand sensorimotor impairments are among the most common consequences of injuries affecting the central and peripheral nervous systems, leading to a drastic reduction in the quality of life for affected individuals. Combining wearable robotic exoskeletons a ...

Ieee-Inst Electrical Electronics Engineers Inc2018

Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke

José del Rocio Millán Ruiz, Dragana Viceic, Hua Zhang, Serafeim Perdikis, Robert Leeb, Andrea Biasiucci, Inaki Asier Iturrate Gil, Thomas Schmidlin, Tiffany Corbet

Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functio ...

Nature Research2018

EEG topographies provide subject-specific correlates of motor control

José del Rocio Millán Ruiz, Dimitri Nestor Alice Van De Ville, Elvira Pirondini, Silvestro Micera, Martina Coscia, Jesús Minguillón Campos

Electroencephalography (EEG) of brain activity can be represented in terms of dynamically changing topographies (microstates). Notably, spontaneous brain activity recorded at rest can be characterized by four distinctive topographies. Despite their well-es ...

Nature Research2017

Electrically Assisted Movement Therapy in Chronic Stroke Patients With Severe Upper Limb Paresis: A Pilot, Single-Blind, Randomized Crossover Study

Stephanie Clarke, José del Rocio Millán Ruiz, Silvio Ionta, Andrea Maesani, Andrea Biasiucci

Objective To evaluate the effects of electrically assisted movement therapy (EAMT) in which patients use functional electrical stimulation, modulated by a custom device controlled through the patient's unaffected hand, to produce or assist task-specific up ...

Elsevier2017

Endogenous Control of Powered Lower-Limb Exoskeleton

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Kyuhwa Lee, Dong Liu

We present an online decoding method for controlling a powered lower-limb exoskeleton using endogenously generated electroencephalogram (EEG) signals of human users. By performing a series of binary classifications, users control the exoskeleton in three d ...

Springer Int Publishing Ag2017

An Approach to a Phase Model for Steady State Visually Evoked Potentials

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Jaiber Evelio Cardona Aristizabal, Wilfredo Alfonso Morales

One of the most used signals in Brain Machine Interfaces (BMI) is the Steady State Visually Evoked Potentials (SSVEP). In a SSVEP-based BMI, a visual stimulus that flickers in a constant frequency is presented to the user, and the system has to detect if t ...

Springer International Publishing Ag2017

Plug&Play brain-computer interfaces for effective active and assisted living control

José del Rocio Millán Ruiz, Nicolas Mora Parra

Brain–Computer Interfaces (BCI) rely on the interpretation of brain activity to provide people with disabilities with an alternative/augmentative interaction path. In light of this, BCI could be considered as enabling technology in many fields, including A ...

Springer Verlag2017

Hyperdimensional computing for noninvasive brain-computer interfaces: Blind and one-shot classification of EEG error-related potentials

José del Rocio Millán Ruiz

The mathematical properties of high-dimensional (HD) spaces show remarkable agreement with behaviors controlled by the brain. Computing with HD vectors, referred to as "hypervectors," is a brain-inspired alternative to computing with numbers. HD computing ...

2017

Brain Racers: How paralysed athletes used a Brain-Computer Interface to win gold at the cyborg olympics

José del Rocio Millán Ruiz, Luca Tonin, Serafeim Perdikis

Ieee-Inst Electrical Electronics Engineers Inc2017

EEG paradigms as a supplemental tool to behavioral assessments of DOC

José del Rocio Millán Ruiz, Serafeim Perdikis, Jean-Paul Noel, Andrea Serino

Introduction Diagnosis and prediction of recovery in the acute phase of disorders of consciousness (DOC) are critical for subsequent medical decisions. However, reliable assessment remains elusive due to the inability of current validated clinical scales t ...

2017

Increasing upper limb training intensity in chronic stroke using embodied virtual reality: a pilot study

José del Rocio Millán Ruiz, Olaf Blanke, Gangadhar Garipelli, Andrea Serino, Thomas Schmidlin, Odile Hélène Chevalley

Background: Technology-mediated neurorehabilitation is suggested to enhance training intensity and therefore functional gains. Here, we used a novel virtual reality (VR) system for task-specific upper extremity training after stroke. The system offers inte ...

BioMed Central2017

Decoding of Self-paced Lower-Limb Movement Intention: A Case Study on the Influence Factors

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Dong Liu

Brain-machine interfaces (BMIs) have been applied as new rehabilitation tools for motor disabled individuals. Active involvement of cerebral activity has been shown to enhance neuroplasticity and thus to restore mobility. Various studies have focused on th ...

Frontiers Research Foundation2017

Inverse solutions for brain-computer interfaces: Effects of regularisation on localisation and classification

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Mohit Kumar Goel

Estimation of intracranial sources, using inverse solutions methods, has been proposed as a mean to improve performance in non-invasive brain-computer interfaces. These methods estimate the activity of a large number of neural sources from a smaller number ...

2017

Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz

Background: One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low freque ...

BioMed Central2017

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