José del Rocio Millán Ruiz

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Gamma band (30-80 Hz) oscillations arising in neuronal ensembles are thought to be a crucial component of the neural code. Recent studies in animals suggest a similar functional role for very high frequency oscillations (VHFO) in the range 80-200Hz. Since ...

Movement Related Cortical Potentials (MRCP) have been the subject of numerous studies. They accompany many self-initiated movements and this makes them a good candidate for incorporation in BCI paradigms. In this work we propose a novel experimental protoc ...

This work extends previous studies on using EEG decoding for automatic image retrieval. We propose an iterative way to integrate the information obtained from the EEG decoding and image processing methods. In the light of real-world BCI applications, we de ...

Human activity recognition is a thriving research field. There are lots of studies in different sub-areas of activity recognition proposing different methods. However, unlike other applications, there is lack of established benchmarking problems for activi ...

Cognitive information has been exploited in non-invasive Brain Computer Interface (BCI) scenarios to provide autonomous external agents with additional information. In this context, Error-related potentials (ErrPs), temporal deflections in electroencephalo ...

Transcranial Direct Current Stimulation (tDCS) has been shown to selectively modulate cortical responses in memory, motor and perceptual tasks. Here we show that this type of stimulation results in targeted enhancement of brain patterns elicited during mot ...

Recently, several studies have started to explore covert visuospatial attention as a control signal for brain–computer interfaces (BCIs). Covert visuospatial attention represents the ability to change the focus of attention from one point in the space with ...

Recognition of brain states and subject's intention from electroencephalogram (EEG) is a challenging problem for brain-computer interaction. Signals recorded from each of EEG electrodes represent noisy spatio-temporal overlapping of activity arising from v ...

Psychophysical studies have reported correlation between neural activity in frontal and parietal areas and subject's reaction time in simple tasks. Here we study whether similar correlates can also be identified in driver's electroencephalography (EEG) act ...

Error-related potentials (ErrPs) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. It is poorly understood whether ErrPs encode further information beyond error awareness. ...

Estimation of intracranial sources, using inverse solutions methods, has been proposed as a mean to improve performance in non-invasive brain-computer interfaces. These methods estimate the activity of a large number of neural sources from a smaller number ...

Brain-computer interfaces (BCI) have been shown to be a promising tool in rehabilitation and assistive scenarios. Within these contexts, brain signals can be decoded and used as commands for a robotic device, allowing to translate user’s intentions into mo ...

The invention relates to a hand exoskeleton device having a modular nature. The device is designed to be wearable, portable and light in weight, and comprises from one to ten modules each having an actuating mechanism such as a linear actuator coupled with ...

The mathematical properties of high-dimensional (HD) spaces show remarkable agreement with behaviors controlled by the brain. Computing with HD vectors, referred to as "hypervectors," is a brain-inspired alternative to computing with numbers. HD computing ...

The term 'brain-computer interface' (BCI) is widely used. However, even among experts, its meaning can sometimes be unclear 1-3. Lack of clarity from the term has real-world implications: it can muddle regulatory guidelines, it may confuse investors, it ca ...

Providing adaptive shared control for Brain- Computer Interfaces (BCIs) can result in better performance while reducing the user’s mental workload. In this respect, online estimation of accuracy and speed of command delivery are important factors. This stu ...

Objective. In contrast to the classical visual brain-computer interface (BCI) paradigms, which adhere to a rigid trial structure and restricted user behavior, electroencephalogram (EEG)-based visual recognition decoding during our daily activities remains ...

Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the E ...

In this paper, we show that the estimated intra-cranial sources using source localization on EEG signals can be used for online Brain Computer Interation (BCI) and the discriminant sources remain stable over days. Classifiers are trained on discriminant so ...

José del Rocio Millán Ruiz

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Very High Frequency Oscillations (VHFO) as a Predictor of Movement Intentions

Detection of Movement Related Cortical Potential: Effects of Causal vs. Non-Causal Processing

Iterative EEG-based Natural Image Search under RSVP

Benchmarking classification techniques using the Opportunity human activity dataset

Inferring subjective preferences on robot trajectories using EEG signals

Selective Enhancement of Motor Imagery Features Using Transcranial Direct Current Stimulation

Time-Dependent Approach for Single Trial Classification of Covert Visuospatial Attention

Discriminative Channel Selection Method for the Recognition of Anticipation related Potentials from CCD estimated Cortical Activity

Sport Psychology: Technologies Ahead

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

Inverse solutions for brain-computer interfaces: Effects of regularisation on localisation and classification

Detecting intention to grasp during reaching movements from EEG

Hand exoskeleton device

Hyperdimensional computing for noninvasive brain-computer interfaces: Blind and one-shot classification of EEG error-related potentials

An application-based taxonomy for brain–computer interfaces

Real-time Prediction of Fast and Slow Delivery of Mental Commands in a Motor Imagery BCI: An Entropy-based Approach

Closed-loop EEG study on visual recognition during driving

Error-Related EEG Potentials Generated during Simulated Brain-Computer Interaction

Online BCI with Stable Sources