Ricardo Andres Chavarriaga Lozano

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This paper presents a novel semi-autonomous navigation strategy designed for low throughput interfaces. A mobile robot (e.g. intelligent wheelchair) proposes the most probable action, as analyzed from the environment, to a human user who can either accept ...

Several studies have proposed the use of inverse solutions based features to improve the decoding performance of brain-computer interfaces. Most of these studies have compared the performance of inverse solutions features over scalp activity in a small set ...

Advances in sports sciences and neurosciences offer new opportunities to design efficient and motivating sport training tools. For instance, using NeuroFeedback (NF), athletes can learn to self-regulate specific brain rhythms and consequently improve their ...

Neural signatures of humans’ movement intention can be exploited by future neuroprosthesis. We propose a method for detecting self-paced upper limb movement intention from brain signals acquired with both invasive and noninvasive methods. In the first stud ...

The firing activities of place cells in the rat hippocampus exhibit strong correlations to the animal's location. External (e.g. visual) as well as internal (proprioceptive and vestibular) sensory information take part in controlling hippocampal place fiel ...

There is a growing interest on using ambient and wearable sensors for human activity recognition, fostered by several application domains and wider availability of sensing technologies. This has triggered increasing attention on the development of robust m ...

Results that do not confirm expectations are generally referred to as 'negative' results. While essential for scientific progress, they are too rarely reported in the literature - Brain-Machine Interface (BMI) research is no exception. This led us to organ ...

Decision-making in an uncertain environment is driven by two major needs: exploring the environment to gather information or exploiting acquired knowledge to maximize reward. The neural processes underlying exploratory decision-making have been mainly stud ...

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 c ...

In this paper we propose a method to modulate the level of assistance provided by a shared controller, not only given the environmental context, but also according to the context of the user’s current behaviour. We show that the enhanced situational contex ...

Context and activity recognition in complex scenarios is prone to data loss due to disconnections, sensor failure, transmission problems, etc. This generally implies significant changes in the recognition performance. In the case of classifier fusion fault ...

Practical Brain-Computer Interfaces (BCIs) for disabled people should allow them to use all their remaining functionalities as control possibilities. Sometimes these people have residual activity of their muscles, most likely in the morning when they are n ...

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 ...

We revisit the framework for brain-coupled image search, where the Electroencephalography (EEG) channel under rapid serial visual presentation protocol is used to detect user preferences. Extending previous works on the synergy between content-based image ...

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 ...

The Fifth International Brain-Computer Interface (BCI) Meeting met on 3rd June 2013 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 19 workshops covering topics in brain-computer interface and brain-machine inter ...

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 ...

Different neural systems are involved in animal navigation depending on the type of task. Experimental studies support the idea that the hippocampus is necessary to learn a spatial representation required to navigate toward hidden goals (place response), w ...

Anomalies and changes in sensor networks which are deployed for activity recognition may abate the classification performance. Detection of anomalies followed by compensatory reaction would ameliorate the performance. This paper introduces a novel approach ...

One of the challenges in using brain computer interfaces over extended periods of time is the uncertainty in the system. This uncertainty can be due to the user's internal states, the non stationarity of the brain signals, or the variation of the class dis ...

Ricardo Andres Chavarriaga Lozano

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Brain-coupled Interaction for Semi-autonomous Navigation of an Assistive Robot

Comparing BCI performance using scalp EEG-and inverse solution-based features

Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Self-paced Movement Intention Detection from Human Brain Signals: Invasive and Non-invasive EEG

Modelling Path Integrator Recalibration Using Hippocampal Place cells

The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition

Turning negative into positives! Exploiting 'negative' results in Brain-Machine Interface (BMI) research

The timing of exploratory decision-making revealed by single-trial topographic EEG analyses

Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control

Online Modulation of the Level of Assistance in Shared Control Systems

A Probabilistic Approach to Handle Missing Data for Multi-Sensory Activity Recognition

Multimodal Fusion of Muscle and Brain Signals for a Hybrid-BCI

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

An Iterative Framework for EEG-based Image Search: Robust Retrieval with Weak Classifiers

Brain-actuated gait trainer with visual and proprioceptive feedback

Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future

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

Competition between cue response and place response: A model of rat navigation behaviour

Detecting Anomalies to Improve Classification Performance in Opportunistic Sensor Networks

Prediction of Command Delivery Time for BCI