José del Rocio Millán Ruiz

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In this paper we propose a method to modulate the level of assistance provided by a shared controller, not only given the environmental context, but also according to the context of the user’s current behaviour. We show that the enhanced situational contex ...

Opportunistic activity and context recognition systems draw from the characteristic to use sensing devices according to a recognition goal at runtime that just happen to be available instead of pre-defining them at the design time of the system. Whenever a ...

Brain-Computer Interfaces (BCIs) are no longer only used by healthy subjects under control conditions in laboratory environments, but by patients controlling applications at their homes, without the BCI experts around. But are the technology and the field ...

Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable fo ...

For severely disabled people, Brain-Computer Interfaces (BCIs) may provide the means to regain mobility and manipulation capabilities. However, information obtained from current BCIs is uncertain and of limited bandwidth and resolution. This paper presents ...

Objective. In this work we present—for the first time—the online operation of an electroencephalogram (EEG) brain–computer interface (BCI) system based on covert visuospatial attention (CVSA), without relying on any evoked responses. Electrophysiological c ...

Context and activity recognition in complex scenarios is prone to data loss due to disconnections, sensor failure, transmission problems, etc. This generally implies significant changes in the recognition performance. In the case of classifier fusion fault ...

Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not requi ...

Brain-actuated wheelchairs offer paraplegics the potential to gain a degree of independence in performing activities of daily living. It is not currently possible to achieve precise proportional control of devices using the low resolution output of a brain ...

Practical Brain-Computer Interfaces (BCIs) for disabled people should allow them to use all their remaining functionalities as control possibilities. Sometimes these people have residual activity of their muscles, most likely in the morning when they are n ...

In their early days, BCIs were only considered as control channel for end users with severe motor impairments such as people in the locked-in state. But, thanks to the multidisciplinary progress achieved over the last decade, the range of BCI applications ...

We revisit the framework for brain-coupled image search, where the Electroencephalography (EEG) channel under rapid serial visual presentation protocol is used to detect user preferences. Extending previous works on the synergy between content-based image ...

Motor imagery (MI) brain-computer interfaces (BCIs) translate a subject’s motor intention to a command signal. Most MI BCIs use power features in the mu or beta rhythms, while several results have been reported using a measure of phase synchrony, the phase ...

Objective. Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes moto ...

Objective. This work presents a first motor imagery-based, adaptive brain–computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ...

Performance variation is one of the main challenges that BCIs are confronted with, when being used over extended periods of time. Shared control techniques could partially cope with such a problem. In this paper, we propose a taxonomy of shared control app ...

Anomalies and changes in sensor networks which are deployed for activity recognition may abate the classification performance. Detection of anomalies followed by compensatory reaction would ameliorate the performance. This paper introduces a novel approach ...

One of the challenges in using brain computer interfaces over extended periods of time is the uncertainty in the system. This uncertainty can be due to the user's internal states, the non stationarity of the brain signals, or the variation of the class dis ...

Decades of technological developments have populated the field of neuroprosthetics with myriad replacement strategies, neuromodulation therapies, and rehabilitation procedures to improve the quality of life for individuals with neuromotor disorders. Despit ...

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- ...

José del Rocio Millán Ruiz

Chattez avec Graph Search

Online Modulation of the Level of Assistance in Shared Control Systems

Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems

Are we ready? Issues in transferring BCI technology from experts to users

Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance

Augmenting Information from Brain-Computer Interfaces through Bayesian Plan Recognition

An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation

A Probabilistic Approach to Handle Missing Data for Multi-Sensory Activity Recognition

Rewards-Driven Control of Robot Arm by Decoding EEG Signals

Vision-Based Shared Control for a BCI Wheelchair

Multimodal Fusion of Muscle and Brain Signals for a Hybrid-BCI

Towards non-invasive hybrid brain-computer interfaces: Framework, practice, clinical application and beyond

An Iterative Framework for EEG-based Image Search: Robust Retrieval with Weak Classifiers

Phase-based features for Motor Imagery Brain-Computer Interfaces

Brain-actuated gait trainer with visual and proprioceptive feedback

Context-aware adaptive spelling in motor imagery BCI

Long-Term Stable Control of Motor-Imagery BCI by a Locked-in User Through Adaptive Assistance

Detecting Anomalies to Improve Classification Performance in Opportunistic Sensor Networks

Prediction of Command Delivery Time for BCI

Personalized neuroprosthetics

Towards Implementation of Motor Imagery using Brain Connectivity Features