José del Rocio Millán Ruiz

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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 ...

In this conceptual review, we highlight our strategy for, and progress in the development of corticospinal neuroprostheses for restoring locomotor functions and promoting neural repair after thoracic spinal cord injury in experimental animal models. We spe ...

This work presents an electroencephalography (EEG)-based Brain-computer Interface (BCI) that decodes cerebral activities to control a lower-limb gait training exoskeleton. Motor imagery (MI) of flexion and extension of both legs was distinguished from the ...

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 ...

In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, key ...

Brain-computer interfaces (BCIs), as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. An elegant approach to improve ...

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 ...

A brain-machine interface (BMI) is about transforming neural activity into action and sensation into perception (Figure 1). In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor o ...

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 ...

Recent experiments have shown the possibility to use the brain electrical activity to directly control the movement of robots. Such a kind of brain–computer interface is a natural way to augment human capabilities by providing a new interaction link with t ...

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 ...

Here we report on a validation study on brain–machine interfaces (BMIs) performed during the December 2007 ESA parabolic flight campaign. We investigated the feasibility of using BMIs for space applications by performing tests in microgravity. Brain signal ...

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 ...

Recent advances in the Brain-Machine Interface (BMI) and in the robotic fields allowed researchers to design a new generation of brain-actuated neuroprostheses to control a variety of assistive devices-ranging from powered wheelchairs to telepresence robot ...

Successful navigation in a teleoperation scenario requires a good level of situational or environmental awareness. This paper presents the main features and capabilities of a new augmented virtuality-based system aimed at providing users with improved perc ...

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 ...

Recent experiments have shown the possibility to use the brain electrical activity to directly control the movement of robots or prosthetic devices in real time. Such neuroprostheses can be invasive or non-invasive, depending on how the brain signals are r ...

José del Rocio Millán Ruiz

Chattez avec Graph Search

Decoding Fast-Paced Error-Related Potentials in Monitoring Protocols

Corticospinal neuroprostheses to restore locomotion after spinal cord injury

An EEG-based brain-computer interface for gait training

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

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

Simultaneous Real-Time Detection of Motor Imagery and Error-Related Potentials for Improved BCI Accuracy

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

Characterizing EEG correlates of exploratory behavior

Brain-Machine Interfaces: The Perception-Action Closed Loop

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

Brain-Computer Interfaces for Space Applications

Non-Invasive Brain-Machine Interaction

Validation of Brain-Machine Interfaces during Parabolic Flight

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

ROS-Neuro: A common middleware for BMI and robotics. The acquisition and recorder packages

Improving Skills and Perception in Robot Navigation by an Augmented Virtuality Assistance System

Collecting complex activity data sets in highly rich networked sensor environments

Non-Invasive Estimation of Local Field Potentials for Neuroprosthesis Control