Javier Minguez -- Associate professor (University of Zaragoza)



IEEE Transaction on Robotics paper accepted: Non-Invasive Brain-Actuated Wheelchair based on a P300 Neurophysiological Potrocol and Automated Navigation. Januray 29th 2009.

IEEE ICRA'09 Workshop: Brain-Machine Interfaces for Neuroprosthetics and Robot Control . May12th 2009.

IEEE ICRA'09 paper accepted: Non-Invasive Brain-Actuated Wheelchair based on a P300 Neurophysiological Protocol and Automated Navigation. January 8th 2009.

IEEE ICRA'09 paper accepted: Human Brain-Teleoperated Robot between Remote Places. January 8th 2009.

IEEE Transaction on Robotics paper accepted: Extending Reactive Collision Avoidance Methods to Consider any Vehicle Shape and the Kinematics and Dynamic Constraints. January 4th 2009.

Cool demos of our research:

IEEE ICRA'06. The video demonstrates the full navigation system of the robotic wheelchair. Download paper

The video demonstrates the Nearness Diagram Navigation operating on indoor and outdoor robots with different shapes, kinematics and dynamics. Download paper

The video demonstrates the control of a robotic wheelchair only using voice commands Download paper

The video demonstrates the control of a robotic wheelchair using a PDA (the user selects rooms to direct the robot by touching the screen) Download paper

The video demonstrates the autonomous people tracking of the robotic wheelchair (with obstacle avoidance) Download paper

For historial and sentimental reasons I have added this video. It shows the moment when in 1999 I achieved with a lot of enthusiasm (see video) the first implementation of the Nearness Diagram in a XR4000 in the LAAS-CNRS, France


Javier Minguez (Associate Professor)

Javier Minguez (S’00–A’02) received the physics science degree in 1996 from the Universidad Complutense de Madrid, Madrid, Spain, and the Ph.D. degree in computer science and systems engineering in 2002 from the University of Zaragoza, Zaragoza, Spain. During this period, in 1999 he was with the Robotics and Artificial Intelligence Group, LAASCNRS, Toulouse, France, for eight months. In 2000, he visited the Robot and Computer Vision Laboratory (ISR-IST), Technical University of Lisbon, Lisbon, Portugal, for ten months. In 2001, he was with the Robotics Laboratory, Stanford University, Stanford, CA, for five months. From 2003 to 2008 he was Ramón y Cajal researcher in the University of Zaragoza. In 2008 he was visiting professor at the Institute of Medical Psychology and Behavioural Neurobiology, Tubingen, Germany for six months. Since 2008, he is an associate professor in the Computer Science and Systems Engineering in the University of Zaragoza.

His research activity is framed within the Robotics and Real Time Group of the University of Zaragoza and the Instituto de Investigación en Ingeniería de Aragón. His research interests are mobile robot navigation and brain-computer interfaces

Javier Minguez directs the

Brain-Computer Interfaces research team in the University of Zaragoza.


Research on Brain-Computer Interfaces

During the last two years we have been working on the copupling between brain-computer interfaces and robotics with focuss on rehabilitation devices. In this direction we have developed two prototypes:

Brain-Actuated Robotic Wheelchair

Our research team has developed a prototype of a brain-actuated wheelchair. During May 2008, five subjects, only using their thoughts, successfully carried navigation and manoeuvrability tasks with the wheelchair in the University. The non-invasive method to record the human neural activity was the EEG and the wheelchair was robotized and equipped with a laser sensor.

More info on the Webpage or download paper.

Brain-Actuated Robotic Teleoperation


The first week of June 2008 a research team of the University of Zaragoza achieved a brain-actuated robot teleoperation between two remote cities (260km). During one week, five subjects used the brain-machine interface to develop navigation and exploration tasks with the robot in a remote place. The non-invasive method to record the human neural activity was the EEG, the communication channel between Zaragoza and Barcelona was internet, and the mobile robot was equipped with an orientable camera.

More info on the Webpage or download paper.

Our research now focuss on the Brain Machine Interfaces for Neuroprosthetics and Robot Control.

Research on Mobile Robot Navigation

During the last years we have been working in many aspects related to mobile robot navigation.

Nearness Diagram Navigation: Reactive Collicion Avoidance in Constrained Spaces

Nearness Diagram operating on a Robotic Whelchair

This research addresses the reactive collision avoidance for mobile robots that move in arduous environments (i.e. very dense, complex and cluttered). To achieve this goal, the technique simplifies the difficulty of the navigation by a divide and conquer strategy, which is based on identifying navigational situations and applying the corresponding motion laws. As a result, navigation with this method is successfully achieved in scenarios where existing techniques present a high degree of difficulty to navigate (download paper).

Download Nearness Diagram C code.

This method is the obstacle avoidance technique base for the Stage & Player.

Global Nearness Diagram Navigation

Global Nearness Diagram navigation operating on a XR4000 robot

This research addresses a sensor-based motion control system was designed to autonomously drive vehicles free of collisions in unknown, troublesome and dynamic scenarios. The system was developed based on hybrid architecture with three layers (modeling, planning and reaction). The interaction of the modules was based on a synchronous planner-reactor configuration where the planner computes tactical information to direct the reactivity. It can achieve robust and reliable navigation in difficult scenarios that are troublesome for many existing methods. Experiments carried out in these scenarios with a real vehicle confirm the effectiveness of this technique.(download paper).

Modeling static and dynamic scenarios for Mobile Robot Navigation

Modeling dynamic and static scenarios: a robot working in an indoor environment mapping the static parts and tracking the people around.

This research addresses the modeling of the static and dynamic parts of the scenario and how to use this information with a sensor-based motion planning system. The contribution in the modeling aspect is a formulation of the detection and tracking of mobile objects and the mapping of the static structure in such a way that the nature (static/dynamic) of the observations is included in the estimation process. The algorithm provides a set of filters tracking the moving objects and a local map of the static structure constructed on line (download paper).


Scan Matching: Mobile robot local localization

This research addresses the scan matching problem for mobile robot displacement estimation. The contribution is a new metric distance and all the tools necessary to be used within the Iterative Closest Point framework. The metric distance is defined in the configuration space of the sensor and takes into account both translation and rotation error of the sensor. The new scan matching technique ameliorates previous methods in terms of robustness, precision, convergence and computational load. Furthermore, it has been extensively tested (download paper).

The role of the shape, kinematics and dynamics in the obstacle avoidance problem

Robotic Whelchair moving with Ego-Kinodynamic space (taking into account the exact robot shape, kinematics and dynamics).

Most collision avoidance methods do not consider the vehicle shape and its kinematic and dynamic constraints, assuming the robot to be point-like and omnidirectional with no acceleration constraints. The contribution of this research is a methodology to consider the exact shape and kinematics, as well as the effects of dynamics in the collision avoidance layer, since the original avoidance method does not address them. This is achievable by abstracting the constraints from the avoidance methods in such a way that, when the method is applied, the constraints already have been considered. This work is a starting point to extend the domain of applicability to a wide range of collision avoidance methods (download paper).

Bitbrain Technolgies

Bitbrain es una empresa de neurotecnología que desarrolla avanzados equipos de EEG y biosensores, y aplicaciones para el mercado del neuromarketing y la estimulación cognitiva.

Resumen de equipos.

Equipos para monitorización de Biosensores como ExG, GSR, TEMP, BVP, etc., de movimiento como EMG, IMUs, etc., de localización en interiores y exteriores, y tecnologías de Eye-Tracking..


Bitbrain dispone de una plataforma de investigación con avanzados equipos de EEG y biosensores para aplicaciones para el mercado del neuromarketing.
¿Qué es el neuromarketing? El neuromarketing es un área de investigación puede aplicar para comprender el comportamiento del consumidor, su proceso de decisión de compra o cómo influyen aspectos como las emociones y los cognitivos como los sesgos cognitivos en su toma de decisiones. En este sentido, existen numerosos libros, blogs, vídeos, cursos e incluso artículos científicos hablando sobre ello. Pero sin duda, la principal aplicación del neuromarketing, y de la que probablemente no se hable tanto, es cuando se utilizan las distintas técnicas de neuromarketing para realizar una investigación de mercados. Bitbrain comercializa laboratorios de neuromarketing bajo la marca usenns.

Estimulación cognitiva

Bitbrain dispone de una neurotecnología de estimulación cognitiva que se puede aplicar para el entrenamiento cognitivo de niños, adolescentes, adultos y ancianos, para aumentar el rendimiento cognitivo en deportistas, ejecutivos, fuerzas armadas y otros profesionales o para la rehabilitación cognitiva en patologías con deterioro como la Depresión , el TDAH o la demencia.
¿Qué es la estimulación cognitiva? La estimulación cognitiva es un conjunto de técnicas y estrategias que tienen como objetivo la mejora del rendimiento y eficacia en el funcionamiento de capacidades cognitivas como la memoria, la atención o la percepción, entre otras. Según el tipo de persona al que va dirigido y el objetivo a conseguir podemos considerar dos tipos: el entrenamiento cognitivo o la rehabilitación cognitiva. El funcionamiento de la estimulación cognitiva se basa en la plasticidad cerebral, una propiedad del cerebro que le permite cambiar, no sólo aspectos cognitivos sino también emocionales. Existen diversas técnicas de estimulación cognitiva como las actividades o ejercicios que se pueden ver en numerosos talleres o las aplicaciones de Brain Training, pero todos son técnicas indirectas. Gracias a la neurotecnología se pueden entrenar de forma directa los ritmos que se asocian a las capacidades cognitivas de cada usuario.




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