Ricardo Andres Chavarriaga Lozano

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This study aims to explore modulation of the connectivity pattern when people perform left hand versus right hand motor imagery and probe the feasibility of adopting connectivity information to discriminate these tasks. Nine subjects were recorded with 16- ...

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 ...

In this paper we use the cortical current density based inverse solution to classify Event Related Potential, in particular for error-related potential elicited during Brain-Computer Interface experiment. We selected discriminant cortical sources for compa ...

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 ...

Brain error processing plays a key role in goal-directed behavior and learning in human brain. Directed transfer function (DTF) on EEG signal brings unique features for discrimination between correct and error cases in braincomputer interface (BCI) system. ...

We propose a method to automatically translate a pre-existing activity recognition system, devised for a source sensor domain S, so that it can operate on a newly discovered target sensor domain T , possibly of different modality. First, we use MIMO system ...

Activity recognition using onbody sensors are prone to degradation due to changes on sensor readings. The changes can occur because of degradation or alteration in the behavior of the sensor with respect to the others. In this paper we propose a method whi ...

raumatic or pathological brain lesions often result in motor disabilities that have strong personal and social effects. Assistive technologies can support this population, potentially improving their autonomy and promoting their participation in society. H ...

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 ...

Human activity recognition is a thriving research field. There are lots of studies in different sub-areas of activity recognition proposing different methods. However, unlike other applications, there is lack of established benchmarking problems for activi ...

Cognitive information has been exploited in non-invasive Brain Computer Interface (BCI) scenarios to provide autonomous external agents with additional information. In this context, Error-related potentials (ErrPs), temporal deflections in electroencephalo ...

Several studies in rats support the idea of multiple neural systems competing to select the best action for reaching a goal or food location. Locale navigation strategies, necessary for reaching invisible goals, seem to be mediated by the hippocampus and t ...

Recognition of brain states and subject's intention from electroencephalogram (EEG) is a challenging problem for brain-computer interaction. Signals recorded from each of EEG electrodes represent noisy spatio-temporal overlapping of activity arising from v ...

Psychophysical studies have reported correlation between neural activity in frontal and parietal areas and subject's reaction time in simple tasks. Here we study whether similar correlates can also be identified in driver's electroencephalography (EEG) act ...

Error-related potentials (ErrPs) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. It is poorly understood whether ErrPs encode further information beyond error awareness. ...

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 ...

Brain-computer interfaces (BCI) have been shown to be a promising tool in rehabilitation and assistive scenarios. Within these contexts, brain signals can be decoded and used as commands for a robotic device, allowing to translate user’s intentions into mo ...

Providing adaptive shared control for Brain- Computer Interfaces (BCIs) can result in better performance while reducing the user’s mental workload. In this respect, online estimation of accuracy and speed of command delivery are important factors. This stu ...

Objective. In contrast to the classical visual brain-computer interface (BCI) paradigms, which adhere to a rigid trial structure and restricted user behavior, electroencephalogram (EEG)-based visual recognition decoding during our daily activities remains ...

In this paper, we show that the estimated intra-cranial sources using source localization on EEG signals can be used for online Brain Computer Interation (BCI) and the discriminant sources remain stable over days. Classifiers are trained on discriminant so ...

Ricardo Andres Chavarriaga Lozano

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Towards Implementation of Motor Imagery using Brain Connectivity Features

Superposition model for steady state visually evoked potentials

Cortical Current Density vs. Surface EEG for Event-Related Potential-based Brain-Computer Interface

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

Improved Recognition of Error Related Potentials through the use of Brain Connectivity Features

Kinect=IMU? Learning MIMO Signal Mappings to Automatically Translate Activity Recognition Systems Across Sensor Modalities

Detecting and rectifying anomalies in body sensor networks

Multidisciplinary design of suitable assistive technologies for motor disabilities in Colombia

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

Benchmarking classification techniques using the Opportunity human activity dataset

Inferring subjective preferences on robot trajectories using EEG signals

A computational model of parallel navigation systems in rodents

Discriminative Channel Selection Method for the Recognition of Anticipation related Potentials from CCD estimated Cortical Activity

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

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

Detecting intention to grasp during reaching movements from EEG

Real-time Prediction of Fast and Slow Delivery of Mental Commands in a Motor Imagery BCI: An Entropy-based Approach

Closed-loop EEG study on visual recognition during driving

Online BCI with Stable Sources