José del Rocio Millán Ruiz

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Brain-Computer Interfaces (BCIs) provide a mean to access the damaged motor network of the brain after stroke, and could be used to drive and promote beneficial plasticity. Among the available therapeutic approaches, Functional Electrical Stimulation (FES) ...

Objective. While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by end-users outside laboratory environments. To achieve this primary objective, i ...

In order for brain-computer interfaces (BCIs) to be used reliably for extended periods of time, they must be able to adapt to the users evolving needs. This adaptation should not only be a function of the environmental (external) context, but should also c ...

The human capacity to compute the likelihood that a decision is correct—known as metacognition—has proven difficult to study in isolation as it usually cooccurs with decision making. Here, we isolated postdecisional from decisional contributions to metacog ...

Motor-disabled end users have successfully driven a telepresence robot in a complex environment using a Brain-Computer Interface (BCI). However, to facilitate the interaction aspect that underpins the notion of telepresence, users must be able to voluntari ...

OPPORTUNITY is project under the EU FET-Open funding1 in which we develop mobile systems to recognize human activity in dynamically varying sensor setups. The system autonomously discovers available sensors around the user and self-configures to recognize ...

Our brain-actuated wheelchair uses shared control to couple the user input with the contextual information about the surroundings in order to perform natural manoeuvres both safely and efficiently. In this study, we investigate the feasibility of using our ...

For people with severe physical disabilities, low resolution input devices, such as buttons, sip and puff switches and brain–computer interfaces provide an opportunity to interact with the world. However, it can be difficult to control assistive technology ...

One of the problems of non-invasive Brain-Computer Interface (BCI) applications is the occurrence of anomalous (unexpected) signals that might degrade BCI performance. This situation might slip the operator’s attention since raw signals are not usually con ...

This work presents EEG-based Brain-computer interface (BCI) that uses error related brain activity to improve the prediction of driver’s intended turning direction. In experiments while subjects drive in a realistic car simulator, we show a directional cue ...

The idea to use EEG correlates of errors to correct or reinforce BCI operation has been proposed over a decade ago. Since then a body of evidence has corroborated this approach. In this paper we give an overview of our recent work exploring the possibiliti ...

This paper discusses and evaluates the role of shared control approach in a BCI-based telepresence framework. Driving a mobile device by using human brain signals might improve the quality of life of people suffering from severely physical disabilities. By ...

José del Rocio Millán Ruiz

Chattez avec Graph Search

Motor Recovery After Stroke by Means of BCI-Guided Functional Electrical Stimulation

Brain Racers: How paralysed athletes used a Brain-Computer Interface to win gold at the cyborg olympics

Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller

Making the most of context-awareness in brain-computer interfaces

Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging

A Hybrid BCI for Enhanced Control of a Telepresence Robot

Activity Recognition in Opportunistic Sensor Environments

Prospects on Brain-Machine Interfaces for Space System Control

Reliable BMI control using epidural ECoG by an hemiplegic user

Driving a BCI Wheelchair: A Patient Case Study

Evaluation of Proportional and Discrete Shared Control Paradigms for Low Resolution User Inputs

Invasive or Noninvasive: Understanding Brain-Machine Interface Technology

Bayesian Plan Recognition for Brain-Computer Interfaces

Theta phase coupling with rhythmic motor output during visuomotor tracking

BCI-NMES therapy enhances effective connectivity in the damaged hemisphere in stroke patients

Quantifying electrode reliability during Brain-Computer Interface operation

Inferring Driver’s Turning Direction through Detection of Error Related Brain Activity

Error Potentials for Brain-Computer Interfaces

First steps towards adaptive deep brain stimulation in Parkinson’s disease

The Role of Shared-Control in BCI-based Telepresence