Ricardo Andres Chavarriaga Lozano

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This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum- ...

Coupling behavioral measures and brain imaging in naturalistic, ecological conditions is key to comprehend the neural bases of spatial navigation. This highly integrative function encompasses sensorimotor, cognitive, and executive processes that jointly me ...

Animals behave in different ways depending on the specific task they are required to solve. In certain cases, if a cue marks the goal location, they can rely on simple stimulusresponse associations. In contrast, other tasks require the animal to be endowed ...

Technological advances in sensing and portable computing devices and wireless communication has lead to an increase in the number and variety of sensing enabled devices (e.g. smartphones or sensing garments). Pervasive computing and activity recognition sy ...

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 seamle ...

Decoding the subjective perception of task difficulty may help improve operator performance, i.e., automatically optimize the task difficulty level. Here, we aim to decode a compound of cognitive states that covaries with the task difficulty level. We desi ...

This study aims to characterize the EEG correlates of exploratory behavior. Decision making in an uncertain environment raises a conflict between two opposing needs: gathering information about the environment and exploiting this knowledge in order to opti ...

A computational model of the hippocampal function in spatial learning is presented. A spatial representation is incrementally acquired during exploration. Visual and self-motion information is fed into a network of rate-coded neurons. A consistent and stab ...

Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We st ...

The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics ...

Background Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. ...

Brain-machine interfaces (BMIs) have demonstrated how they can be used for reaching tasks with both invasive and non-invasive signal recording methods. Despite the constant improvements in this field, there still exist diverse factors to overcome before ac ...

Detection of anomalies is a broad field of study, which is applied in different areas such as data monitoring, navigation, and pattern recognition. In this paper we propose two measures to detect anomalous behaviors in an ensemble of classifiers by monitor ...

Opportunistic sensing can be used to obtain data from sensors that just happen to be present in the user’s surroundings. By harnessing these opportunistic sensor configurations to infer activity or context, ambient intelligence environments become more rob ...

Objective: A fundamental issue in EEG event-related potentials (ERPs) studies is the amount of data required to have an accurate ERP model. This also impacts the time required to train a classifier for a brain-computer interface (BCI). This issue is mainly ...

In this paper we describe a new computational model of switching between path-planning and cue-guided navigation strategies. It is based on three main assumptions: (i) the strategies are mediated by separate memory systems that learn independently and in p ...

The promise of Brain-Computer Interfaces (BCI) technology is to augment human capabilities by enabling interaction with computers through a conscious and spontaneous modulation of the brainwaves after a short training period. Indeed, by analyzing brain ele ...

Attentional shifts precede most of the perceptual processes, and therefore their recognition may constitute an interesting process for both the study of human perception as well as the design of EEG-based Brain Computer Interfaces (BCI). Several studies ha ...

Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. However, BCI ...

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 ...

Ricardo Andres Chavarriaga Lozano

Chattez avec Graph Search

Context-Aware Learning for Generative Models

Mobile brain/body imaging of landmark-based navigation with high-density EEG

Spatial learning and navigation in the rat : a biomimetic model

Ensemble creation and reconfiguration for activity recognition: An information theoretic approach

Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states

EEG Correlates of Difficulty Levels in Dynamical Transitions of Simulated Flying and Mapping Tasks

Characterizing the EEG Correlates of Exploratory Behavior

Robust self-localisation and navigation based on hippocampal place cells

Single trial prediction of self-paced reaching directions from EEG signals

Workshops of the seventh international brain-computer interface meeting: not getting lost in translation

Quantifying the time for accurate EEG decoding of single value-based decisions

Asynchronous Decoding of Error Potentials During the Monitoring of a Reaching Task

On-line anomaly detection and resilience in classifier ensembles

The OPPORTUNITY Framework and Data Processing Ecosystem for Opportunistic Activity and Context Recognition

Latency Correction of Event-Related Potentials Between Different Experimental Protocols

Path planning versus cue responding: a bioinspired model of switching between navigation strategies

Non-Invasive Brain-Actuated Interaction

EEG-based Recognition of Covert Attentional Shifts

Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials

Heading for new shores! Overcoming pitfalls in BCI design