José del Rocio Millán Ruiz

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Recent advances in the field of Brain-Computer Interfaces (BCIs) have shown that BCIs have the potential to provide a powerful new channel of communication, completely independent of muscular and nervous systems. However, while there have been successful l ...

In this paper we show how healthy subjects can operate a non-invasive asynchronous BCI for controlling a FES neuroprosthesis and manipulate objects to carry out daily tasks in ecological conditions. Both, experienced and novel subjects proved to be able to ...

This paper presents the main features and capabilities of a developed system based on augmented virtuality (AV), which is devoted to the assisted navigation of teleoperated robots. The goal is to provide users with a greater perception of the robot navigat ...

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 ...

The non-invasive Brain-Computer Interface (BCI) developed in our lab targets asynchronous operation of devices by monitoring electroencephalographic (EEG) activity and identifying oscillatory patterns that the user can voluntary modulate through the execut ...

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 ...

Numerous studies have examined neural correlates of the human brain’s action-monitoring system during experimentally segmented tasks. However, it remains unknown how such a system operates during continuous motor output when no experimental time marker is ...

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. ...

Independent mobility is core to being able to perform activities of daily living by oneself. However, powered wheelchairs are not an option for a large number of people who are unable to use conventional interfaces, due to severe motor–disabilities. Non-in ...

In this paper we present further results of our asynchronous and non-invasive BMI for the continuous control of an intelligent wheelchair. Three subjects participated in two experiments where they steered the wheelchair spontaneously, without any external ...

Brain-machine interfaces (BMI) have largely been demonstrated in laboratory conditions involving, mainly, healthy users. We have recently carried out a series of studies with a substantial number of motor-disabled end-users operating different brain-contro ...

Objective: To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair. Methods: In experiment 1 two subjects were asked to mentally drive both a real a ...

A Brain-Computer Interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major c ...

Despite the growing interest in brain-machine interface (BMI)-driven neuroprostheses, the translation of the BMI output into a suitable control signal for the robotic device is often neglected. In this article, we propose a novel control approach based on ...

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 ...

In this paper we present the results of six motor-disabled patients manoeuvring a telepresence robot via a BCI. Remarkably, although five of the patients had never visited the location where the telepresence robot was operating, they achieved similar perfo ...

In this paper we present the preliminary results of a pioneering attempt to predict the timing of steering actions in a driving task from non-invasive EEG measurements. The experiment took place with the subjects driving a car at a constant speed on a simu ...

Controlling a device via a brain-computer interface (BCI) requires the participant to look and to split the attention between the device and the BCI feedback, which is partly contradictory. Therefore, a stimulation system based on 6 coin-motors is develope ...

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 ...

In this paper we argue that for brain-computer interfaces (BCIs) to be used reliably for extended periods of time, they must be able to adapt to the user’s evolving needs. This adaptation should not only be a function of the environmental (external) contex ...

José del Rocio Millán Ruiz

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Towards a Robust BCI: Error Potentials and Online Learning

Towards Natural Non-Invasive Hand Neuroprostheses for Daily Living

Interaction and Evaluation of an Augmented Virtuality Assistance System for Teleoperated Robots

Superposition model for steady state visually evoked potentials

Evidence Accumulation in asynchronous BCI

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

Action Monitoring Cortical Activity Coupled to Submovements

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

Brain-Controlled Wheelchairs: A Robotic Architecture

Asynchronous Non-Invasive Brain-Actuated Control of an Intelligent Wheelchair

Brain-Machine Interfaces: Progress in Large Clinical Validation

A Brain-Actuated Wheelchair: Asynchronous and Non-Invasive Brain-Computer Interfaces for Continuous Control of Robots

The BCI Competition III: Validating Alternative Approaches to Actual BCI Problems

The Role of the Control Framework for Continuous Teleoperation of a Brain-Machine Interface-Driven Mobile Robot

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

BCI Telepresence: A Six Patient Evaluation

Steering Timing Prediction in a Driving Simulator Task

Feel the BCI vibe – vibrotactile BCI feedback

Detecting and rectifying anomalies in body sensor networks

On the Need for Both Internal and External Context Awareness for Reliable BCIs