Ricardo Andres Chavarriaga Lozano

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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 ...

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 ...

In order for brain-computer interfaces (BCIs) to be used reliably for extended periods of time, they must be able to adapt to the users evolving needs. This adaptation should not only be a function of the environmental (external) context, but should also c ...

OPPORTUNITY is project under the EU FET-Open funding1 in which we develop mobile systems to recognize human activity in dynamically varying sensor setups. The system autonomously discovers available sensors around the user and self-configures to recognize ...

One of the problems of non-invasive Brain-Computer Interface (BCI) applications is the occurrence of anomalous (unexpected) signals that might degrade BCI performance. This situation might slip the operator’s attention since raw signals are not usually con ...

The idea to use EEG correlates of errors to correct or reinforce BCI operation has been proposed over a decade ago. Since then a body of evidence has corroborated this approach. In this paper we give an overview of our recent work exploring the possibiliti ...

This paper describes a brain-machine interface for the online control of a powered lower-limb exoskeleton based on electroencephalogram (EEG) signals recorded over the user’s sensorimotor cortical areas. We train a binary decoder that can distinguish two d ...

Recognition of driver’s intention from electroencephalogram (EEG) can be helpful in developing an in-car brain computer interface (BCI) systems for intelligent cars. This could be beneficial in enhancing the quality of interaction between the driver and th ...

Calibration of brain-machine interfaces exploiting event-related potentials has to be performed for each experimental paradigm. Even if these signals have been used in previous experiments with different protocols. We show that use of signals from previous ...

Ricardo Andres Chavarriaga Lozano

Chattez avec Graph Search

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

OPPORTUNITY: Towards opportunistic activity and context recognition systems

Recording a complex, multi modal activity data set for context recogntion

Making the most of context-awareness in brain-computer interfaces

Activity Recognition in Opportunistic Sensor Environments

Reliable BMI control using epidural ECoG by an hemiplegic user

Quantifying electrode reliability during Brain-Computer Interface operation

Error Potentials for Brain-Computer Interfaces

First steps towards adaptive deep brain stimulation in Parkinson’s disease

Decoding of two hand grasping types from EEG

A Brain-Controlled Exoskeleton with Cascaded Event-Related Desynchronization Classifiers

Detection of Anticipatory Brain Potentials during Car Driving

Latency correction of error-related potentials reduces BCI calibration time

Robust, accurate spelling based on error-related potentials

Adaptive assistance for BCI: a locked-in syndrome end-user case study

Minimal model of strategy switching in the plus-maze navigation task