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Ricardo Andres Chavarriaga Lozano

Cette personne n’est plus à l’EPFL

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Publications associées (138)

Differential contributions of subthalamic beta rhythms and 1/f broadband activity to motor symptoms in Parkinson’s disease

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Stéphanie Martin, Robert Leeb, Inaki Asier Iturrate Gil, Aleksander Sobolewski, Jocelyne Bloch

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, quest ...

2018

EEG-based lower-limb movement onset decoding: Continuous classification and asynchronous detection

Mohamed Bouri, Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Kyuhwa Lee, Fumiaki Iwane, Dong Liu

Brain-machine interfaces (BMIs) have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography (EEG). Active neural participation is crucial to promote brain plasticity thus to enhance the ...

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

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

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Marija Uscumlic, Zahra Khaliliardali, Sareh Saeedi, Lucian Andrei Gheorghe, Huaijian Zhang, Ruslan Aydarkhanov

Modern cars can support their drivers by assessing and autonomously performing different driving maneuvers based on information gathered by in-car sensors. We propose that brain–machine interfaces (BMIs) can provide complementary information that can ease ...

2018

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

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

Brain-actuated gait trainer with visual and proprioceptive feedback

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

Objective. Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes moto ...

Institute of Physics2017

Heading for new shores! Overcoming pitfalls in BCI design

Ricardo Andres Chavarriaga Lozano

Research in brain-computer interfaces has achieved impressive progress towards implementing assistive technologies for restoration or substitution of lost motor capabilities, as well as supporting technologies for able-bodied subjects. Notwithstanding this ...

Taylor & Francis Ltd2017

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Ricardo Andres Chavarriaga Lozano, Anton Nijholt, Inaki Asier Iturrate Gil

Brain–computer interfaces (BCI) (also referred to as brain–machine interfaces; BMI) are, by definition, an interface between the human brain and a technological application. Brain activity for interpretation by the BCI can be acquired with either invasive ...

2017

A Brain-Controlled Exoskeleton with Cascaded Event-Related Desynchronization Classifiers

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

This paper describes a brain-machine interface for the online control of a powered lower-limb exoskeleton based on electroencephalogram (EEG) signals recorded over the user’s sensorimotor cortical areas. We train a binary decoder that can distinguish two d ...

Elsevier2017

Harnessing prefrontal cognitive signals for brain-machine interfaces

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz

Brain-machine interfaces (BMIs) enable humans to interact with devices by modulating their brain signals. Despite impressive technological advancements, several obstacles remain. The most commonly used BMI control signals are derived from the brain areas i ...

Elsevier2017

Long-Term Stable Control of Motor-Imagery BCI by a Locked-in User Through Adaptive Assistance

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Sareh Saeedi

Performance variation is one of the main challenges that BCIs are confronted with, when being used over extended periods of time. Shared control techniques could partially cope with such a problem. In this paper, we propose a taxonomy of shared control app ...

Institute of Electrical and Electronics Engineers2017

Spatial filters yield stable features for error-related potentials across conditions

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Inaki Asier Iturrate Gil, Fumiaki Iwane

Error-related potentials (ErrP) have been increasingly studied in psychophysical experiments as well as for brain-machine interfacing. In the latter case, the generalisation capabilities of ErrP decoders is a crucial element to avoid frequent recalibration ...

2016

Single Trial Classification of Neural Correlates of Anticipatory Behavior during Real Car Driving

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Zahra Khaliliardali, Lucian Andrei Gheorghe, Huaijian Zhang

2016

Superposition model for steady state visually evoked potentials

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

Steady State Visually Evoked Potentials (SSVEP) are signals produced in the occipital part of the brain when someone gaze a light flickering at a fixed frequency. These signals have been used for Brain Machine Interfacing (BMI), where one or more stimuli a ...

2016

Detection of Movement Related Cortical Potential: Effects of Causal vs. Non-Causal Processing

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz

Movement Related Cortical Potentials (MRCP) have been the subject of numerous studies. They accompany many self-initiated movements and this makes them a good candidate for incorporation in BCI paradigms. In this work we propose a novel experimental protoc ...

2016

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