José del Rocio Millán Ruiz

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The exploitation of EEG signatures of cognitive processes can provide valuable information to improve interaction with brain actuated devices. In this work we study these correlates in a realistic situation simulated in a virtual reality environment. We fo ...

Anticipation increases the efficiency of daily tasks by partial advance activation of neural substrates involved in it. Previous off-line studies have shown the possibility of exploiting this activation for a Brain-Computer Interface (BCI) using electroenc ...

One important source of performance degradation in BCIs is bias towards one of the mental classes. Recent literature has focused on the general problem of classification accuracy drop, identifying non-stationarity as the generating factor, thus leading to ...

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the non-stationary nature of the brain signals provides for a rather unstable input. With the use of intelligent ...

Activity recognition systems based on body-worn motion sensors suffer from a decrease in performance during the deployment and run-time phases, because of probable changes in the sensors (e.g. displacement or rotation), which is the case in many real-life ...

Practical brain-computer interfaces need to overcome several challenges, including tolerance to signal variability within- and across sessions. We introduce Robust Principal Component Analysis (RPCA) as a potential approach to tackle intra-trial variabilit ...

Intelligent cars exploit environmental information to support drivers by providing extra information and assisting complex maneouvers. They can also take into account the internal state of the driver by means of decoding cognition-related brain activity. H ...

This paper presents a new filter for EEG Event-Related Potentials that relies on spatio-temporal features. The results are analyzed with error-related potentials, and compared to the results obtained with Independent Component Analysis. Additionally, the o ...

Non-invasive brain stimulation has shown promis- ing results in neurorehabilitation for motor-impaired stroke patients, by rebalancing the relative involvement of each hemisphere in movement generation. Similarly, brain-computer interfaces have been used t ...

Controlling a brain-actuated device requires the participant to look at and to split his attention between the interaction of the device with its environment and the status information of the Brain-Computer Interface (BCI). Such parallel visual tasks are p ...

Multi-stream automatic speech recognition (MS-ASR) has been confirmed to boost the recognition performance in noisy conditions. In this system, the generation and the fusion of the streams are the essential parts and need to be designed in such a way to re ...

Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functio ...

Brain–Computer Interfaces (BCI) rely on the interpretation of brain activity to provide people with disabilities with an alternative/augmentative interaction path. In light of this, BCI could be considered as enabling technology in many fields, including A ...

Sparse methods are widely used in image and audio processing for denoising and classification, but there have been few previous applications to neural signals for brain-computer interfaces (BCIs). We used the dictionary- learning algorithm K-SVD, coupled w ...

Brain-Computer Interfaces convey a great potential in the field of stroke rehabilitation, where the continuous monitoring of the execution of mental tasks could support the positive effects of the therapy by reinforcing specific mental patterns. We propose ...

Although the Internet of Things allows seamless access to billions of sensors readily deployed throughout the world, current context- and activity-recognition approaches restrict ambient intelligence to domains where dedicated sensors are deployed. The big ...

Brain-Computer Interfaces (BCIs) have been successfully used to control assistive mobility devices (like a telepresence robot or an electric wheelchair) using only motor imagery. Importantly, disabled end-users are able to achieve similar performances as h ...

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific ...

José del Rocio Millán Ruiz

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Anticipation- and Error-related EEG Signals during Realistic Human-Machine interaction: A Study on Visual and Tactile Feedback

Anticipation based Brain-Computer Interfacing (aBCI)

A supervised recalibration protocol for unbiased BCI

Context-based Filtering for Assisted Brain-Actuated Wheelchair Driving

Robust Activity Recognition Combining Anomaly Detection and Classifier Retraining

Using Robust Principal Component Analysis to Reduce EEG Intra-Trial Variability

Detecting Cognitive States for Enhancing Driving Experience

Spatio-temporal filtering for EEG error related potentials

Modulation of the inter-hemispheric asymmetry of motor-related brain activity using brain-computer interfaces

Freeing the visual channel by exploiting vibrotactile BCI feedback

Robust Activity Recognition Combining Anomaly Detection and Classifier Retraining

Stream fusion for multi-stream automatic speech recognition

Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke

Plug&Play brain-computer interfaces for effective active and assisted living control

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

EEG Microstates for BCI Therapist Feedback: Preliminary Results on a Stroke Patient

Opportunistic human activity and context recognition

Brain-Actuated Assistive Mobility For Disabled End-User

Single Trial Classification of Neural Correlates of Anticipatory Behavior during Real Car Driving

OPPORTUNITY: Towards opportunistic activity and context recognition systems