José del Rocio Millán Ruiz

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This paper presents an important step forward towards increasing the independence of people with severe motor disabilities, by using brain-computer interfaces (BCI) to harness the power of the Internet of Things. We analyze the stability of brain signals a ...

Brain-machine interfaces (BMIs) enable humans to interact with devices by modulating their brain signals. Despite impressive technological advancements, several obstacles remain. The most commonly used BMI control signals are derived from the brain areas i ...

To investigate whether a motor attempt EEG paradigm coupled with functional electrical stimulation can detect command following and, therefore, signs of conscious awareness in patients with disorders of consciousness, we recorded nine patients admitted to ...

The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these s ...

Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the ...

In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for biometric identificat ...

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors can affect the data distribution between two different experimental sessions including sensor displacemen ...

This paper discusses a time-dependent classification approach for single trial recognition of spatial covert visual attention for Brain–Computer Interface. Covert visual attention is a natural and intuitive mental task that does not require any external st ...

One of the main problems of both synchronous and asynchronous EEG-based BCIs is the need of an initial calibration phase before the system can be used. This phase is necessary due to the high non-stationarity of the EEG, since it changes between sessions a ...

To be correctly mastered, Brain-Computer Interfaces (BCIs) need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim is to explore the benefits of vibrotactile feedback during usersï¿½ traini ...

In this paper we want to show the use of inverse solutions ‒in particular cortical current density (CCD)‒with respect to the current surface electroencephalogram (EEG) in classifying event related potentials. We construct feature vector from selected chann ...

This paper presents a novel semi-autonomous navigation strategy designed for low throughput interfaces. A mobile robot (e.g. intelligent wheelchair) proposes the most probable action, as analyzed from the environment, to a human user who can either accept ...

Error-related potentials (ErrP) have been increasingly studied in psychophysical experiments as well as for brain-machine interfacing. In the latter case, the generalisation capabilities of ErrP decoders is a crucial element to avoid frequent recalibration ...

The idea of moving robots or prosthetic devices not by manual control, but by mere thinking (i.e., the brain activity of human subjects) has fascinated researchers for the last 30 years, but it is only now that first experiments have shown the possibility ...

Several studies have proposed the use of inverse solutions based features to improve the decoding performance of brain-computer interfaces. Most of these studies have compared the performance of inverse solutions features over scalp activity in a small set ...

Advances in sports sciences and neurosciences offer new opportunities to design efficient and motivating sport training tools. For instance, using NeuroFeedback (NF), athletes can learn to self-regulate specific brain rhythms and consequently improve their ...

Neural signatures of humans’ movement intention can be exploited by future neuroprosthesis. We propose a method for detecting self-paced upper limb movement intention from brain signals acquired with both invasive and noninvasive methods. In the first stud ...

There is a growing interest on using ambient and wearable sensors for human activity recognition, fostered by several application domains and wider availability of sensing technologies. This has triggered increasing attention on the development of robust m ...

Decision-making in an uncertain environment is driven by two major needs: exploring the environment to gather information or exploiting acquired knowledge to maximize reward. The neural processes underlying exploratory decision-making have been mainly stud ...

Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the c ...

José del Rocio Millán Ruiz

Chattez avec Graph Search

Towards Independence: A BCI Telepresence Robot for People with Severe Motor Disabilities

Harnessing prefrontal cognitive signals for brain-machine interfaces

Motor Attempt EEG Paradigm As a Diagnostic Tool for Disorders of Consciousness

Errare machinale est: The use of error-related potentials in brain-machine interfaces

Subject-oriented training for motor imagery brain-computer interfaces

Person Authentication using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation

Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition

Single trial detection of spatial covert visual attention for BCI

Minimizing calibration time of single-trial recognition of error potentials in brain-computer interfaces

Vibrotactile Feedback for Brain-Computer Interface Operation

Inverse solutions for classifying Event Related Potentials

Brain-coupled Interaction for Semi-autonomous Navigation of an Assistive Robot

Spatial filters yield stable features for error-related potentials across conditions

Brain-Controlled Robots

Comparing BCI performance using scalp EEG-and inverse solution-based features

Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Self-paced Movement Intention Detection from Human Brain Signals: Invasive and Non-invasive EEG

The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition

The timing of exploratory decision-making revealed by single-trial topographic EEG analyses

Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control