Header logo is


2004


no image
EEG Channel Selection for Brain Computer Interface Systems Based on Support Vector Methods

Schröder, M., Lal, T., Bogdan, M., Schölkopf, B.

7, pages: 50, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
A Brain Computer Interface (BCI) system allows the direct interpretation of brain activity patterns (e.g. EEG signals) by a computer. Typical BCI applications comprise spelling aids or environmental control systems supporting paralyzed patients that have lost motor control completely. The design of an EEG based BCI system requires good answers for the problem of selecting useful features during the performance of a mental task as well as for the problem of classifying these features. For the special case of choosing appropriate EEG channels from several available channels, we propose the application of variants of the Support Vector Machine (SVM) for both problems. Although these algorithms do not rely on prior knowledge they can provide more accurate solutions than standard lter methods [1] for feature selection which usually incorporate prior knowledge about neural activity patterns during the performed mental tasks. For judging the importance of features we introduce a new relevance measure and apply it to EEG channels. Although we base the relevance measure for this purpose on the previously introduced algorithms, it does in general not depend on specic algorithms but can be derived using arbitrary combinations of feature selectors and classifiers.

ei

Web [BibTex]

2004


Web [BibTex]


no image
Learning Depth

Sinz, F., Franz, MO.

pages: 69, (Editors: H.H.Bülthoff, H.A.Mallot, R.Ulrich,F.A.Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
The depth of a point in space can be estimated by observing its image position from two different viewpoints. The classical approach to stereo vision calculates depth from the two projection equations which together form a stereocamera model. An unavoidable preparatory work for this solution is a calibration procedure, i.e., estimating the external (position and orientation) and internal (focal length, lens distortions etc.) parameters of each camera from a set of points with known spatial position and their corresponding image positions. This is normally done by iteratively linearizing the single camera models and reestimating their parameters according to the error on the known datapoints. The advantage of the classical method is the maximal usage of prior knowledge about the underlying physical processes and the explicit estimation of meaningful model parameters such as focal length or camera position in space. However, the approach neglects the nonlinear nature of the problem such that the results critically depend on the choice of the initial values for the parameters. In this study, we approach the depth estimation problem from a different point of view by applying generic machine learning algorithms to learn the mapping from image coordinates to spatial position. These algorithms do not require any domain knowledge and are able to learn nonlinear functions by mapping the inputs into a higher-dimensional space. Compared to classical calibration, machine learning methods give a direct solution to the depth estimation problem which means that the values of the stereocamera parameters cannot be extracted from the learned mapping. On the poster, we compare the performance of classical camera calibration to that of different machine learning algorithms such as kernel ridge regression, gaussian processes and support vector regression. Our results indicate that generic learning approaches can lead to higher depth accuracies than classical calibration although no domain knowledge is used.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Neural mechanisms underlying control of a Brain-Computer-Interface (BCI): Simultaneous recording of bold-response and EEG

Hinterberger, T., Wilhelm, B., Veit, R., Weiskopf, N., Lal, TN., Birbaumer, N.

2004 (poster)

Abstract
Brain computer interfaces (BCI) enable humans or animals to communicate or activate external devices without muscle activity using electric brain signals. The BCI Thought Translation Device (TTD) uses learned regulation of slow cortical potentials (SCPs), a skill most people and paralyzed patients can acquire with training periods of several hours up to months. The neurophysiological mechanisms and anatomical sources of SCPs and other event-related brain macro-potentials are well understood, but the neural mechanisms underlying learning of the self-regulation skill for BCI-use are unknown. To uncover the relevant areas of brain activation during regulation of SCPs, the TTD was combined with functional MRI and EEG was recorded inside the MRI scanner in twelve healthy participants who have learned to regulate their SCP with feedback and reinforcement. The results demonstrate activation of specific brain areas during execution of the brain regulation skill: successf! ul control of cortical positivity allowing a person to activate an external device was closely related to an increase of BOLD (blood oxygen level dependent) response in the basal ganglia and frontal premotor deactivation indicating learned regulation of a cortical-striatal loop responsible for local excitation thresholds of cortical assemblies. The data suggest that human users of a BCI learn the regulation of cortical excitation thresholds of large neuronal assemblies as a prerequisite of direct brain communication: the learning of this skill depends critically on an intact and flexible interaction between these cortico-basal ganglia-circuits. Supported by the Deutsche Forschungsgemeinschaft (DFG) and the National Institute of Health (NIH).

ei

[BibTex]

[BibTex]


no image
Masking by plaid patterns revisited

Wichmann, F.

Experimentelle Psychologie. Beitr{\"a}ge zur 46. Tagung experimentell arbeitender Psychologen, 46, pages: 285, 2004 (poster)

ei

[BibTex]

[BibTex]


no image
Early visual processing—data, theory, models

Wichmann, F.

Experimentelle Psychologie. Beitr{\"a}ge zur 46. Tagung experimentell arbeitender Psychologen, 46, pages: 24, 2004 (poster)

ei

[BibTex]

[BibTex]


no image
Statistical Learning with Similarity and Dissimilarity Functions

von Luxburg, U.

pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


no image
Classification and Feature Extraction in Man and Machine

Graf, AAB.

Biologische Kybernetik, University of Tübingen, Germany, 2004, online publication (phdthesis)

ei

[BibTex]

[BibTex]


no image
Implicit Wiener series for capturing higher-order interactions in images

Franz, M., Schölkopf, B.

Sensory coding and the natural environment, (Editors: Olshausen, B.A. and M. Lewicki), 2004 (poster)

Abstract
The information about the objects in an image is almost exclusively described by the higher-order interactions of its pixels. The Wiener series is one of the standard methods to systematically characterize these interactions. However, the classical estimation method of the Wiener expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear signals such as images. We propose an estimation method based on regression in a reproducing kernel Hilbert space that overcomes these problems using polynomial kernels as known from Support Vector Machines and other kernel-based methods. Numerical experiments show performance advantages in terms of convergence, interpretability and system sizes that can be handled. By the time of the conference, we will be able to present first results on the higher-order structure of natural images.

ei

[BibTex]

[BibTex]


no image
Classification and Memory Behaviour of Man Revisited by Machine

Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B.

CSHL Meeting on Computational & Systems Neuroscience (COSYNE), 2004 (poster)

ei

[BibTex]

[BibTex]


no image
Investigation of oxide layers in tunnel junctions

Amaladass, E. P.

University of Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


no image
Untersuchung der Desorptionskinetik von Metallhydriden in Bezug auf technische Anwendungen

von Zeppelin, F.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

[BibTex]

[BibTex]


no image
Towards Tractable Parameter-Free Statistical Learning (Phd Thesis)

D’Souza, A

Department of Computer Science, University of Southern California, Los Angeles, 2004, clmc (phdthesis)

am

link (url) [BibTex]

link (url) [BibTex]


no image
Dynamik von Wasserstoff in nanokristallinen Systemen

Stanik, E.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

[BibTex]

[BibTex]


no image
Inselwachstum auf Festkörperoberflächen unter Ionenbestrahlung

Frank, A.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


no image
Flusslinienverankerung in HTSL-Schichten mit kontrollierter Defektstruktur im Nanometerbereich

Leonhardt, S.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

[BibTex]

[BibTex]


no image
Investigation of the stability of metals on polymers

Amoako, G.

University of Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


no image
Flusslinienverankerung in Hochtemperatursupraleitern auf nanostrukturierten Substraten

Brück, S.

Universität Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


no image
Ionenstreuung mit Monolagen-Tiefenauflösung

Olliges, S.

Universität Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


no image
Untersuchungen zum Magnetismus von Clustern und Nanopartikeln und zum Einfluss der Wechselwirkung mit ihrer Umgebung

Fauth, Kai

Julius-Maximilians-Universität Würzburg, Würzburg, 2004 (phdthesis)

mms

[BibTex]

[BibTex]