485 research outputs found
On Riemannian tools for classification improvement in Brain-Computer Interfaces
A Brain Computer Interface (BCI) or Brain Machine Interface (BMI) is a device that allows the exchange of
information between the brain of a person and a computer without the need of physical interaction. This
technology promises to change the way in which we interact with machines, but it is not yet affordable, robust
or quick enough to substitute other classic human to machine interfaces for the general public.
This being said, the lack of need of interaction makes them a very promising solution that would provide
people with severe motor disabilities with a new way of interacting with their surroundings, improving their
quality of life.
The most extended method of extracting information about brain activity and the one used for this project
is the Electroencefalogram (EEG). This device consists of multiple electrodes mounted on a helmet-like
structure that is placed on the userâs scalp. The electrodes detect the sum of action potentials from large
populations of neurons on the brainâs cortex. The main advantages of this technique are the relative low
cost of the device, portability, and the high temporal resolution and ease of use of a non invasive technique.
This is not free of disadvantages, as the method suffers from a low signal to noise ratio, low robustness to
interference, low spatial resolution and the effects of inter and intra session drift, that is, the movement of the
electrodes during and between sessions produce variations on the acquisition of the signal.
There are also multiple paradigms in the field of BCI, each one of them focusing on a different brain signal.
This work is centered around the Motor Imagery Brain Computer Interface (MI-BCI), which differs from
other BCIs in the fact that it directly decodes the intention of the user without the need of inducing a specific
response in the brain by presenting an stimulus. This approach is considered to be more natural and can be
more comfortable, but also requires a higher level of mental effort and proficiency from part of the user.
The MI-BCI is based on a signal of unknown origin that is produced on the sensorymotor cortex, responsible for voluntary movements and touch among others, the Sensorimotor Rhythms (SMR). This signal
is atenuated when the person performs or thinks about performing a movement, which is called an Event
Related Desynchronization (ERD) and amplified when going back to the idling state, an Event Related
Synchronization (ERS). As the brain is a distributed system, the origin of these events can be estimated and
is related to the movement that the person imagined. In an implementation, these movements are limited to
a discrete set of posibilities and each one is mapped to a computer instruction, allowing the unidirectional
transfer of information between brain and machine.
The classical machine learning approach to this problem has been to use very specific signal processing
techniques to extract relevant features for this problem that can then be fed to a general classification
algorithm. The main tecnique is known as Common Spatial Patterns (CSP) followed by classification with
Linear Discriminant Analysis (LDA) or Support Vector Machine (SVM). This has some advantages such as a
relative low requirement of training samples, but also lacks the capability of generalisation, and a system fine
tuned for one user cannot be used for other users or even for another session from the same user reliably.
In this work we study an alternative framework that uses the covariance matrices of the EEG signals as
observations and exploits the Riemannian geometry of Symmetric Positive Definite (SPD) matrices to classify
them in their natural space. This is not only a more general signal processing approach that has been used in
other fields of research, but also opens the possibility of transfering some information between users and
sessions, which may result in a more robust system or in a system that requires less data for training. This
is crucial for the usability of MI-BCI because recording a training session before each use of the system is
mentally exhausting and time consuming.Universidad de Sevilla. MĂĄster Universitario en IngenierĂa de TelecomunicaciĂł
STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits
We present a novel classifier network called STEP, to classify perceived
human emotion from gaits, based on a Spatial Temporal Graph Convolutional
Network (ST-GCN) architecture. Given an RGB video of an individual walking, our
formulation implicitly exploits the gait features to classify the emotional
state of the human into one of four emotions: happy, sad, angry, or neutral. We
use hundreds of annotated real-world gait videos and augment them with
thousands of annotated synthetic gaits generated using a novel generative
network called STEP-Gen, built on an ST-GCN based Conditional Variational
Autoencoder (CVAE). We incorporate a novel push-pull regularization loss in the
CVAE formulation of STEP-Gen to generate realistic gaits and improve the
classification accuracy of STEP. We also release a novel dataset (E-Gait),
which consists of human gaits annotated with perceived emotions along
with thousands of synthetic gaits. In practice, STEP can learn the affective
features and exhibits classification accuracy of 89% on E-Gait, which is 14 -
30% more accurate over prior methods
Computational Methods for Cognitive and Cooperative Robotics
In the last decades design methods in control engineering made substantial progress in
the areas of robotics and computer animation. Nowadays these methods incorporate the
newest developments in machine learning and artificial intelligence. But the problems
of flexible and online-adaptive combinations of motor behaviors remain challenging for
human-like animations and for humanoid robotics. In this context, biologically-motivated
methods for the analysis and re-synthesis of human motor programs provide new insights
in and models for the anticipatory motion synthesis.
This thesis presents the authorâs achievements in the areas of cognitive and developmental robotics, cooperative and humanoid robotics and intelligent and machine learning methods in computer graphics. The first part of the thesis in the chapter âGoal-directed Imitation for Robotsâ considers imitation learning in cognitive and developmental robotics.
The work presented here details the authorâs progress in the development of hierarchical
motion recognition and planning inspired by recent discoveries of the functions of mirror-neuron cortical circuits in primates. The overall architecture is capable of âlearning for
imitationâ and âlearning by imitationâ. The complete system includes a low-level real-time
capable path planning subsystem for obstacle avoidance during arm reaching. The learning-based path planning subsystem is universal for all types of anthropomorphic robot arms, and is capable of knowledge transfer at the level of individual motor acts.
Next, the problems of learning and synthesis of motor synergies, the spatial and spatio-temporal combinations of motor features in sequential multi-action behavior, and the
problems of task-related action transitions are considered in the second part of the thesis
âKinematic Motion Synthesis for Computer Graphics and Roboticsâ. In this part, a new
approach of modeling complex full-body human actions by mixtures of time-shift invariant
motor primitives in presented. The online-capable full-body motion generation architecture
based on dynamic movement primitives driving the time-shift invariant motor synergies
was implemented as an online-reactive adaptive motion synthesis for computer graphics
and robotics applications.
The last chapter of the thesis entitled âContraction Theory and Self-organized Scenarios
in Computer Graphics and Roboticsâ is dedicated to optimal control strategies in multi-agent scenarios of large crowds of agents expressing highly nonlinear behaviors. This last
part presents new mathematical tools for stability analysis and synthesis of multi-agent
cooperative scenarios.In den letzten Jahrzehnten hat die Forschung in den Bereichen der Steuerung und Regelung
komplexer Systeme erhebliche Fortschritte gemacht, insbesondere in den Bereichen
Robotik und Computeranimation. Die Entwicklung solcher Systeme verwendet heutzutage
neueste Methoden und Entwicklungen im Bereich des maschinellen Lernens und der
kĂŒnstlichen Intelligenz. Die flexible und echtzeitfĂ€hige Kombination von motorischen Verhaltensweisen
ist eine wesentliche Herausforderung fĂŒr die Generierung menschenĂ€hnlicher
Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch
motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme
neue Erkenntnisse und Modelle fĂŒr die antizipatorische Bewegungssynthese.
Diese Dissertation prÀsentiert die Ergebnisse der Arbeiten des Autors im Gebiet der
kognitiven und Entwicklungsrobotik, kooperativer und humanoider Robotersysteme sowie
intelligenter und maschineller Lernmethoden in der Computergrafik. Der erste Teil der
Dissertation im Kapitel âZielgerichtete Nachahmung fĂŒr Roboterâ behandelt das Imitationslernen
in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben
neue Methoden fĂŒr die hierarchische Bewegungserkennung und -planung, die durch
Erkenntnisse zur Funktion der kortikalen Spiegelneuronen-Schaltkreise bei Primaten inspiriert
wurden. Die entwickelte Architektur ist in der Lage, âdurch Imitation zu lernenâ
und âzu lernen zu imitierenâ. Das komplette entwickelte System enthĂ€lt ein echtzeitfĂ€higes
Pfadplanungssubsystem zur Hindernisvermeidung wĂ€hrend der DurchfĂŒhrung von Armbewegungen.
Das lernbasierte Pfadplanungssubsystem ist universell und fĂŒr alle Arten von
anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer
Handlungen zu ĂŒbertragen.
Im zweiten Teil der Arbeit âKinematische Bewegungssynthese fĂŒr Computergrafik und
Robotikâ werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h.
von rÀumlichen und rÀumlich-zeitlichen Kombinationen motorischer Bewegungselemente
bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs ĂŒbergĂ€ngen behandelt.
Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen
durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem
wurde ein online-fĂ€higer Synthesealgorithmus fĂŒr Ganzköperbewegungen entwickelt, der
auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten
verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde fĂŒr
verschiedene Probleme der Bewegungssynthese fĂŒr die Computergrafik- und Roboteranwendungen
implementiert.
Das letzte Kapitel der Dissertation mit dem Titel âKontraktionstheorie und selbstorganisierte
Szenarien in der Computergrafik und Robotikâ widmet sich optimalen Kontrollstrategien
in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare
Kinematik gekennzeichnet sind. Dieser letzte Teil prÀsentiert neue mathematische Werkzeuge
fĂŒr die StabilitĂ€tsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien
Reconstructions of science
'In vier Rekonstruktionen wird versucht, die natur- und sozialwissenschaftliche Diskussion der Basiskonzepte von Raum und Zeit zu vereinen. Dazu bedarf es einer neuen Diskursform, die bereits in Alfred North Whiteheads Naturphilosophie anklingt. Auf sozial-wissenschaftlicher Seite besinnen wir uns grundlegender Themen von SozialphĂ€nomenologie, Strukturalismus und Interaktionismus. Fragestellungen von PrĂ€historikerInnen, ĂgyptologInnen und EthnomathematikerInnen werden wichtig, wo wir zeigen, daĂ unsere Konzepte von Raum und Zeit kulturelle Institutionen der Bedeutung sind, die ihrerseits Gesellschaft konstituieren und konstanter Rekonstruktion bedĂŒrfen. Die vierte Rekonstruktion greift die Frage der theoretischen Physik auf und stĂŒtzt sich auf das integrative Instrument der Theorie der geometrischen Clifford Algebren. Wir leiten ab, daĂ und wie die inneren Symmetrien der Materie mit den Ă€uĂeren Symmetrien der Raum-Zeit verbunden sind und daĂ die Metapher vom 'achtfachen Pfad', die Gell-Mann fĂŒr einen Teil des Standardmodells verwendete, entgegen seiner Auffassung nicht als Witz zu verstehen ist. Der Faktor (D4)m in der Dirac-Gruppe jeder geometrischen Clifford Algebra C/p,q bildet eine Grundstruktur von Orientierung und Logik ab und korrespondiert daher mit einem Interface zwischen Geist und Materie.' (Autorenreferat)'In four reconstructions it is attempted to lead the natural and social science debate of the basis concepts of space and time in common. For this we need a new mode of science discourse which has already been initiated in Alfred North Whitehead's philosophy of nature. In social science we reconsider the basis themes of social phenomenology, structuralism and interactionism as far as those contribute to a space-time topic. Investigations of prehistorians, egyptologists and ethno-mathematicians are of importance where we demonstrate that our concepts of space and time represent cultural institutions of meaning which on their part constitute society and require that we constantly reconstruct them. The fourth reconstruction deals with the space-time of postmodern theoretical physics and is founded on the integrative instrument of the theory of geometric Clifford algebras. We show that and how the inner symmetrics of matter are connected with the outer symmetries of space-time and that Gell-Mann's metaphor of the 'eightfold path' that he used to denote part of the standard model of physics cannot be interpreted as quirk, in opposition to his own intention. The factor (D4)m in the Dirac group of any geometric Clifford Algebra C/p,q represents a ground template (or archetypal structure) for both orientation and logic and corresponds therefore with an interface between matter and mind.' (author's abstract)
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