University of Zaragoza (UZ)
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- 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 visual
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 and an autonomous navigation system.
Product Portfolio
Neurotechnology Projects University of Zaragoza
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The Neurotechnology Lab at the University of Zaragoza has extensive
knowledge in the field of "Neurotechnology", with experience in real-time
detection of brain states and the respective data analysis methods, as well
as in experimental brain imaging techniques (especially EEG and MEG) with
development of technology. The technical application domains of this
research are neuroprosthetics (robotics) and neurotherapy (neurofeedback).
Brain-actuated Robot Wheelchair
- In this project, it was constructed a brain-actuated robotic wheelchair to
provide people with severe neuromuscular disabilities a certain degree of
mobility. During operation, the user faces a screen displaying a real-time
virtual reconstruction of the scenario and concentrates on the location to
be reached. The electroencephalogram (EEG) signal processing detects the
user’s intents, which are transferred to the autonomous navigation system
that drives the wheelchair to the desired location while avoiding collisions
with obstacles detected by the sensors. This concept gives the user the
flexibility to operate the device even in unknown or evolving scenarios. The
prototype has been tested and validated with users at the University of
Zaragoza
Brain-Actuated Robot Telepresence System
- This project explores the benefits of brain-actuated
telepresence robots in remote scenarios for paralyzed users. The patients –
unable to leave their clinical environments – are provided a physical entity
embodied in a real environment (anywhere in the world with internet access),
ready to perceive, explore, manipulate, and also interact, controlled only
by brain activity. The underlying idea of the system is that the
brain-computer system decodes the user’s intentions; such intentions are
transferred to the robotic system via internet, and finally, the robot
executes the tasks autonomously. The user can alternate between a navigation
mode (to control the robot motion), an exploration mode (to control the
camera orientation) and an interaction mode (to interact through sounds and
short expressions). This device has been evaluated by users at the
University of Zaragoza with the robot in the
Polytechnic University of Barcelona, as well as by patients at the
University of
Tubingen with the robot at the University of Zaragoza.
Brain-Actuated Robotic Arm Prosthesis
- The overall objective of this research project is to
develop a new generation of brain-controlled robotic arms, which operate
using more natural and intuitive control strategies. This project focuses on
augmenting human capabilities compensating for reduced motor functions, in
particular for those users who need autonomous manipulation and grasping.
The results will be demonstrated by users performing daily manipulation
tasks at home using the brain-controlled robotic arm. Furthermore, this
project is coordinated with the Faktronik
Foundation, which will develop the functional stimulation system (FES)
to accomplish the motion of the patient’s arm as an alternative to the
robotic arm.
Brain-Actuated Telepresence of Low-cost Robots
- This project explores the benefits of a brain-actuated
telepresence system with several low-cost robots located in different and
remote places. The developed system allows the user to control
simultaneously the motion of two robots located anywhere in the world (with
Internet connection) only through brain activity, also providing the user
with the functionality of interaction. The mobile robots are low-cost
systems, portable (fits into one hand) and equipped with a mobile phone
camera. The prototype has been tested and validated by users at the
University of Zaragoza.
Brain-Computer Interface Software Architecture
- This project explores the benefits of a new software
architecture focused on brain-computer interfaces and neurofeedback
applications, bypassing the limitations of existent ones. A modular,
flexible, and portable architecture is being developed, which will support
signal acquisition from several hardware devices, synchronizing them and
simultaneously applying several signal-processing algorithms for different
applications. Furthermore, it will provide common functionalities according
to the brain-computer interface needs as well as interoperability with
robotic architectures and other common interaction frameworks.
Machine Learning for Brain Computer Interfaces
- In this project, machine learning techniques are being
developed to identify and detect online neurophysiologic events, which are
meaningful for an EEG-based brain computer interface. Techniques have been
successfully developed to identify online P300 and Error-related potentials
in single trials. The techniques are based on several spatial filters and
state of the art supervised pattern recognition strategies to adapt the
performance to each particular user as well as to deal with the non-stacionarity
of the EEG on different days. The results have been applied to robot control
and online error detection and correction.
Dynamic Source Localization methods for EEG and MEG
- In this project, new methods are being developed to
localize the neural processes of the dynamic brain activity by using
simultaneous electroencephalography (EEG) and magnetoencephalography (MEG)
recordings or each modality by itself. For such, a methodology was developed
for solving the source localization problem considering the dynamic nature
of the neural activation within a Bayesian framework. The advantages of the
approach are: (1) the variable multidimensionality of the problem is dealt
with, (2) different brain dynamics in different areas are dealt with
simultaneously, and (3) it is real-time. The technique has been validated
using simulated EEG and MEG data from realistic neurophysiologic conditions
and transition of brain activations, and also by real EEG and MEG data
recorded during a specific neurophysiologic protocol designed to elicit
error-related potentials. The approach is being tested using different
neurophysiologic protocols and addressing such common problems as the
identification of epileptic focus.
Identification of Cognitive States with a Brain Computer
Interface and Biosignals
- In this project an automatic system is being developed to
recognize cognitive states (relaxation, stress, etc) through physiological
recordings (electrocardiogram, skin conductivity, body temperature,
breathing, etc.) and EEG signals. This system is used to perform physiologic
understanding and evaluation of users while using a BCI system. This type of
system is relevant in order to improve the user-machine adaptability,
performance, and robustness. The system is being tested with ALS patients in
collaboration with the
University of
Tubingen. The evaluation results of the telepresence robot showed that
while the patient used the P300 BCI to control the robot, various cognitive
states, such as relaxation and stress, and also changes between the “alert”
and “distracted” states could be identified.
Neurofeedback focused on Attention Deficit-Hyperactivity
Disorder (ADHD)
- ADHD is a mental disorder that is reported to afflict
approximately 7% of children in the United States. Although the vast
majority of studies have indicated that pharmacological treatments can exert
a positive effect on its core symptoms, (i) approximately 25% of patients
demonstrate either an adverse response or no response, (ii) there is no
evidence that the clinical improvements continue in the absence of
sustained, long-term treatment, and (iii) the long-term treatment with
stimulants involves risks. In this project an EEG-based brain-computer
interface system is being developed to be used as clinical therapy in the
treatment of the core symptoms of ADHD. EEG biofeedback involves the
application of operant conditioning techniques, providing patients with
visual and auditory “feedback” for certain “neurofeedback behaviors”,
reinforcing or inhibiting electrophysiological activity within specific
frequency bands, which may yield sustaining clinical benefits.