Ricardo Andres Chavarriaga Lozano

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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 ...

Anticipation increases the efficiency of daily tasks by partial advance activation of neural substrates involved in it. Here we develop a method for the recognition of electroencephalogram (EEG) correlates of this activation as early as possible on single ...

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors affect the data distribution between two different experimental sessions. One of these factors is the pot ...

Performance improvement in both humans and artificial systems strongly relies in the ability of recognizing erroneous behavior or decisions. This paper, that builds upon previous studies on EEG error-related signals, presents a hybrid approach for human co ...

Single trial recognition of slow cortical potentials (SCPs) from full-band EEG (FbEEG) faces different challenges to classical EEG such as noisy, high magnitude (~±100µV) infra slow oscillations (ISO) with f

Error-related EEG potentials (ErrP) can be used for brain-machine interfacing (BMI). Decoding of these signals, indicating subject's perception of erroneous system decisions or actions can be used to correct these actions or to improve the overall interfac ...

Electrophysiological and neuroimaging evidence suggest the existence of common mechanisms for monitoring erroneous events, independent of the source of errors. Previous works have described modulations of theta activity in the medial frontal cortex elicite ...

Activity and context recognition in pervasive and wearable computing ought to continuously adapt to changes typical of open-ended scenarios, such as changing users, sensor characteristics, user expectations, or user motor patterns due to learning or aging. ...

This study aims at characterizing the EEG correlates of exploratory behavior. Decision making in an uncertain environment gives rise to a conflict between two opposing needs: gathering information about the environment and exploiting this knowledge in orde ...

Objective. A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside th ...

Adaptively increasing the difficulty level in learning was shown to be beneficial than increasing the level after some fixed time intervals. To efficiently adapt the level, we aimed at decoding the subjective difficulty level based on Electroencephalograph ...

Hybrid Brain-Computer Interfaces (BCI) are representing a recent approach to develop practical BCIs. In such a system disabled users are able to use all their remaining functionalities as control possibilities in parallel with the BCI. Sometimes these peop ...

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 ...

One of the challenges of using brain-computer interfaces (BCIs) over extended periods of time is the variation of the users' performance across different experimental days. The goal of the current study is to propose a performance estimator for an electroe ...

We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects ...

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 ...

The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these s ...

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors can affect the data distribution between two different experimental sessions including sensor displacemen ...

One of the main problems of both synchronous and asynchronous EEG-based BCIs is the need of an initial calibration phase before the system can be used. This phase is necessary due to the high non-stationarity of the EEG, since it changes between sessions a ...

In this paper we want to show the use of inverse solutions ‒in particular cortical current density (CCD)‒with respect to the current surface electroencephalogram (EEG) in classifying event related potentials. We construct feature vector from selected chann ...

Ricardo Andres Chavarriaga Lozano

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

Fast Recognition of Anticipation Related Potentials

Unsupervised adaptation for acceleration-based activity recognition: Robustness to sensor displacement and rotation

Adaptation of Hybrid Human-Computer Interaction Systems using EEG Error-Related Potentials

Single Trial Recognition of Anticipatory Slow Cortical Potentials: The Role of Spatio-Spectral Filtering

Decoding Fast-Paced Error-Related Potentials in Monitoring Protocols

Discriminant Brain Connectivity Patterns of Performance Monitoring at Average and Single-Trial Levels

On the use of brain decoded signals for online user adaptive gesture recognition systems

Characterizing EEG correlates of exploratory behavior

Customizing the human-avatar mapping based on EEG error related potentials during avatar-based interaction

EEG-Based Online Regulation of Difficulty in Simulated Flying

A hybrid BCI based on the fusion of EEG and EMG activities

Non-Invasive Brain-Machine Interaction

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

Collecting complex activity data sets in highly rich networked sensor environments

Harnessing prefrontal cognitive signals for brain-machine interfaces

Errare machinale est: The use of error-related potentials in brain-machine interfaces

Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition

Minimizing calibration time of single-trial recognition of error potentials in brain-computer interfaces

Inverse solutions for classifying Event Related Potentials