Publications | Max Planck Institute for Intelligent Systems

Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

ei Besserve, M. Independence of cause and mechanism in brain networks DALI workshop on Networks: Processes and Causality, April 2015 (talk)

Haptic Textures for Online Shopping

Culbertson, H., Kuchenbecker, K. J.

Interactive demonstrations in The Retail Collective exhibit, presented at the Dx3 Conference in Toronto, Canada, March 2015 (misc)

hi Culbertson, H., Kuchenbecker, K. J. Haptic Textures for Online Shopping Interactive demonstrations in The Retail Collective exhibit, presented at the Dx3 Conference in Toronto, Canada, March 2015 (misc)

Information-Theoretic Implications of Classical and Quantum Causal Structures

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.

18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

ei Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D. Information-Theoretic Implications of Classical and Quantum Causal Structures 18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

ei Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J. Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI World Molecular Imaging Conference, 2015 (talk)

Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

ei Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J. Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model World Molecular Imaging Conference, 2015 (talk)

The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

ei Foreman-Mackey, D., Hogg, D. W., Schölkopf, B. The search for single exoplanet transits in the Kepler light curves IAU General Assembly, 22, pages: 2258352, 2015 (talk)

Derivation of phenomenological expressions for transition matrix elements for electron-phonon scattering

Illg, C., Haag, M., Müller, B. Y., Czycholl, G., Fähnle, M.

2015 (misc)

mms

mms Illg, C., Haag, M., Müller, B. Y., Czycholl, G., Fähnle, M. Derivation of phenomenological expressions for transition matrix elements for electron-phonon scattering 2015 (misc)

2013

Jointonation: Robotization of the Human Body by Vibrotactile Feedback

Kurihara, Y., Hachisu, T., Kuchenbecker, K. J., Kajimoto, H.

Emerging Technologies Demonstration with Talk at ACM SIGGRAPH Asia, Hong Kong, November 2013, Hands-on demonstration given by Kurihara, Takei, and Nakai. Best Demonstration Award as voted by the Program Committee (misc)

2013

hi Kurihara, Y., Hachisu, T., Kuchenbecker, K. J., Kajimoto, H. Jointonation: Robotization of the Human Body by Vibrotactile Feedback Emerging Technologies Demonstration with Talk at ACM SIGGRAPH Asia, Hong Kong, November 2013, Hands-on demonstration given by Kurihara, Takei, and Nakai. Best Demonstration Award as voted by the Program Committee (misc)

Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent App. 14/016,651 (misc)

pi Sitti, M., Kim, S. Dry adhesives and methods for making dry adhesives sep 2013, US Patent App. 14/016,651 (misc)

Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent App. 14/016,683 (misc)

pi Sitti, M., Kim, S. Dry adhesives and methods for making dry adhesives sep 2013, US Patent App. 14/016,683 (misc)

Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent 8,524,092 (misc)

pi Sitti, M., Kim, S. Dry adhesives and methods for making dry adhesives sep 2013, US Patent 8,524,092 (misc)

Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI

Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.

Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

ei Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A. Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

Data-Driven Modeling and Rendering of Isotropic Textures

Culbertson, H., McDonald, C. G., Goodman, B. E., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE World Haptics Conference, Daejeon, South Korea, April 2013, Best Demonstration Award (by audience vote) (misc)

hi Culbertson, H., McDonald, C. G., Goodman, B. E., Kuchenbecker, K. J. Data-Driven Modeling and Rendering of Isotropic Textures Hands-on demonstration presented at IEEE World Haptics Conference, Daejeon, South Korea, April 2013, Best Demonstration Award (by audience vote) (misc)

Adding Haptics to Robotic Surgery

J. Kuchenbecker, K., Brzezinski, A., D. Gomez, E., Gosselin, M., Hui, J., Koch, E., Koehn, J., McMahan, W., Mahajan, K., Nappo, J., Shah, N.

Learning Center Station at SAGES (Society of American Gastrointestinal and Endoscopic Surgeons) Annual Meeting, Baltimore, Maryland, USA, April 2013 (misc)

hi J. Kuchenbecker, K., Brzezinski, A., D. Gomez, E., Gosselin, M., Hui, J., Koch, E., Koehn, J., McMahan, W., Mahajan, K., Nappo, J., Shah, N. Adding Haptics to Robotic Surgery Learning Center Station at SAGES (Society of American Gastrointestinal and Endoscopic Surgeons) Annual Meeting, Baltimore, Maryland, USA, April 2013 (misc)

Dry adhesives and methods of making dry adhesives

Sitti, M., Murphy, M., Aksak, B.

March 2013, US Patent App. 13/845,702 (misc)

pi Sitti, M., Murphy, M., Aksak, B. Dry adhesives and methods of making dry adhesives March 2013, US Patent App. 13/845,702 (misc)

MR-Based Attenuation Correction for Combined Brain PET/MR: Robustness of Atlas- and Pattern Recognition Method to Atlas Registration Failures

Mantlik, F., Bezrukov, I., Hofmann, M., Schölkopf, B., Pichler, B.

IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE MIC), 2013 (talk)

ei Mantlik, F., Bezrukov, I., Hofmann, M., Schölkopf, B., Pichler, B. MR-Based Attenuation Correction for Combined Brain PET/MR: Robustness of Atlas- and Pattern Recognition Method to Atlas Registration Failures IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE MIC), 2013 (talk)

Domain Generalization via Invariant Feature Representation

Muandet, K.

30th International Conference on Machine Learning (ICML2013), 2013 (talk)

ei Muandet, K. Domain Generalization via Invariant Feature Representation 30th International Conference on Machine Learning (ICML2013), 2013 (talk)

Lopez-Paz, D., Duvenaud, D.

Random Projections (Computational and Biological Learning Lab, University of Cambridge, 2013)

2013 (talk)

ei Lopez-Paz, D., Duvenaud, D. Random Projections (Computational and Biological Learning Lab, University of Cambridge, 2013) 2013 (talk)

2010

Comparative Quantitative Evaluation of MR-Based Attenuation Correction Methods in Combined Brain PET/MR

Mantlik, F., Hofmann, M., Bezrukov, I., Kolb, A., Beyer, T., Reimold, M., Pichler, B., Schölkopf, B.

2010(M08-4), 2010 Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), November 2010 (talk)

Abstract

Combined PET/MR provides at the same time molecular and functional imaging as well as excellent soft tissue contrast. It does not allow one to directly measure the attenuation properties of scanned tissues, despite the fact that accurate attenuation maps are necessary for quantitative PET imaging. Several methods have therefore been proposed for MR-based attenuation correction (MR-AC). So far, they have only been evaluated on data acquired from separate MR and PET scanners. We evaluated several MR-AC methods on data from 10 patients acquired on a combined BrainPET/MR scanner. This allowed the consideration of specific PET/MR issues, such as the RF coil that attenuates and scatters 511 keV gammas. We evaluated simple MR thresholding methods as well as atlas and machine learning-based MR-AC. CT-based AC served as gold standard reference. To comprehensively evaluate the MR-AC accuracy, we used RoIs from 2 anatomic brain atlases with different levels of detail. Visual inspection of the PET images indicated that even the basic FLASH threshold MR-AC may be sufficient for several applications. Using a UTE sequence for bone prediction in MR-based thresholding occasionally led to false prediction of bone tissue inside the brain, causing a significant overestimation of PET activity. Although it yielded a lower mean underestimation of activity, it exhibited the highest variance of all methods. The atlas averaging approach had a smaller mean error, but showed high maximum overestimation on the RoIs of the more detailed atlas. The Nave Bayes and Atlas-Patch MR-AC yielded the smallest variance, and the Atlas-Patch also showed the smallest mean error. In conclusion, Atlas-based AC using only MR information on the BrainPET/MR yields a high level of accuracy that is sufficient for clinical quantitative imaging requirements. The Atlas-Patch approach was superior to alternative atlas-based methods, yielding a quantification error below 10% for all RoIs except very small ones.

2010

ei Mantlik, F., Hofmann, M., Bezrukov, I., Kolb, A., Beyer, T., Reimold, M., Pichler, B., Schölkopf, B. Comparative Quantitative Evaluation of MR-Based Attenuation Correction Methods in Combined Brain PET/MR 2010(M08-4), 2010 Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), November 2010 (talk)

Statistical image analysis and percolation theory

Davies, P., Langovoy, M., Wittich, O.

73rd Annual Meeting of the Institute of Mathematical Statistics (IMS), August 2010 (talk)

Abstract

We develop a novel method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation theory. We specifically address the problem of detection of objects of unknown shapes in the case of nonparametric noise. The noise density is unknown and can be heavy-tailed. We view the object detection problem as hypothesis testing for discrete statistical inverse problems. We present an algorithm that allows to detect objects of various shapes in noisy images. We prove results on consistency and algorithmic complexity of our procedures.

ei Davies, P., Langovoy, M., Wittich, O. Statistical image analysis and percolation theory 73rd Annual Meeting of the Institute of Mathematical Statistics (IMS), August 2010 (talk)

Statistical image analysis and percolation theory

Langovoy, M., Wittich, O.

28th European Meeting of Statisticians (EMS), August 2010 (talk)

ei Langovoy, M., Wittich, O. Statistical image analysis and percolation theory 28th European Meeting of Statisticians (EMS), August 2010 (talk)

Cooperative Cuts: Graph Cuts with Submodular Edge Weights

Jegelka, S., Bilmes, J.

24th European Conference on Operational Research (EURO XXIV), July 2010 (talk)

Abstract

We introduce cooperative cut, a minimum cut problem whose cost is a submodular function on sets of edges: the cost of an edge that is added to a cut set depends on the edges in the set. Applications are e.g. in probabilistic graphical models and image processing. We prove NP hardness and a polynomial lower bound on the approximation factor, and upper bounds via four approximation algorithms based on different techniques. Our additional heuristics have attractive practical properties, e.g., to rely only on standard min-cut. Both our algorithms and heuristics appear to do well in practice.

ei Jegelka, S., Bilmes, J. Cooperative Cuts: Graph Cuts with Submodular Edge Weights 24th European Conference on Operational Research (EURO XXIV), July 2010 (talk)

VerroTouch: High-Frequency Acceleration Feedback for Telerobotic Surgery

Kuchenbecker, K. J., Gewirtz, J., McMahan, W., Standish, D., Martin, P., Bohren, J., Mendoza, P. J., Lee, D. I.

Hands-on demonstration presented at EuroHaptics, Amsterdam, Netherlands, Amsterdam, Netherlands, July 2010 (misc)

hi Kuchenbecker, K. J., Gewirtz, J., McMahan, W., Standish, D., Martin, P., Bohren, J., Mendoza, P. J., Lee, D. I. VerroTouch: High-Frequency Acceleration Feedback for Telerobotic Surgery Hands-on demonstration presented at EuroHaptics, Amsterdam, Netherlands, Amsterdam, Netherlands, July 2010 (misc)

TexturePad: Realistic Rendering of Haptic Textures

Romano, J. M., Landin, N., McMahan, W., Kuchenbecker, K. J.

Hands-on demonstration presented at EuroHaptics, Amsterdam, Netherlands, July 2010 (misc)

hi Romano, J. M., Landin, N., McMahan, W., Kuchenbecker, K. J. TexturePad: Realistic Rendering of Haptic Textures Hands-on demonstration presented at EuroHaptics, Amsterdam, Netherlands, July 2010 (misc)

BCI and robotics framework for stroke rehabilitation

Gomez Rodriguez, M., Grosse-Wentrup, M., Hill, J., Peters, J., Schölkopf, B., Gharabaghi, A.

4th International BCI Meeting, June 2010 (talk)

ei Gomez Rodriguez, M., Grosse-Wentrup, M., Hill, J., Peters, J., Schölkopf, B., Gharabaghi, A. BCI and robotics framework for stroke rehabilitation 4th International BCI Meeting, June 2010 (talk)

Solving Large-Scale Nonnegative Least Squares

Sra, S.

16th Conference of the International Linear Algebra Society (ILAS), June 2010 (talk)

Abstract

We study the fundamental problem of nonnegative least squares. This problem was apparently introduced by Lawson and Hanson [1] under the name NNLS. As is evident from its name, NNLS seeks least-squares solutions that are also nonnegative. Owing to its wide-applicability numerous algorithms have been derived for NNLS, beginning from the active-set approach of Lawson and Han- son [1] leading up to the sophisticated interior-point method of Bellavia et al. [2]. We present a new algorithm for NNLS that combines projected subgradients with the non-monotonic gradient descent idea of Barzilai and Borwein [3]. Our resulting algorithm is called BBSG, and we guarantee its convergence by ex- ploiting properties of NNLS in conjunction with projected subgradients. BBSG is surprisingly simple and scales well to large problems. We substantiate our claims by empirically evaluating BBSG and comparing it with established con- vex solvers and specialized NNLS algorithms. The numerical results suggest that BBSG is a practical method for solving large-scale NNLS problems.

PDF PDF [BibTex]

ei Sra, S. Solving Large-Scale Nonnegative Least Squares 16th Conference of the International Linear Algebra Society (ILAS), June 2010 (talk)

PDF PDF [BibTex]

Matrix Approximation Problems

Sra, S.

EU Regional School: Rheinisch-Westf{\"a}lische Technische Hochschule Aachen, May 2010 (talk)

PDF AVI [BibTex]

ei Sra, S. Matrix Approximation Problems EU Regional School: Rheinisch-Westf{\"a}lische Technische Hochschule Aachen, May 2010 (talk)

PDF AVI [BibTex]

BCI2000 and Python

Hill, NJ.

Invited lecture at the 7th International BCI2000 Workshop, Pacific Grove, CA, USA, May 2010 (talk)

Abstract

A tutorial, with exercises, on how to integrate your own Python code with the BCI2000 realtime software package.

ei Hill, NJ. BCI2000 and Python Invited lecture at the 7th International BCI2000 Workshop, Pacific Grove, CA, USA, May 2010 (talk)

Extending BCI2000 Functionality with Your Own C++ Code

Hill, NJ.

Invited lecture at the 7th International BCI2000 Workshop, Pacific Grove, CA, USA, May 2010 (talk)

Abstract

A tutorial, with exercises, on how to use BCI2000 C++ framework to write your own real-time signal-processing modules.

ei Hill, NJ. Extending BCI2000 Functionality with Your Own C++ Code Invited lecture at the 7th International BCI2000 Workshop, Pacific Grove, CA, USA, May 2010 (talk)

Realistic Haptic Contacts and Textures for Tablet Computing

Romano, J. M., Kuchenbecker, K. J.

Hands-on demonstration presented at the Stanford Medical Innovation Conference on Medical Robotics, Stanford, California, April 2010 (misc)

hi Romano, J. M., Kuchenbecker, K. J. Realistic Haptic Contacts and Textures for Tablet Computing Hands-on demonstration presented at the Stanford Medical Innovation Conference on Medical Robotics, Stanford, California, April 2010 (misc)

High-Frequency Tactile Feedback for the da Vinci Surgical System

Standish, D., Gewirtz, J., McMahan, W., Martin, P., Kuchenbecker, K. J.

Hands-on demonstration presented at the Stanford Medical Innovation Conference on Medical Robotics, April 2010 (misc)

hi Standish, D., Gewirtz, J., McMahan, W., Martin, P., Kuchenbecker, K. J. High-Frequency Tactile Feedback for the da Vinci Surgical System Hands-on demonstration presented at the Stanford Medical Innovation Conference on Medical Robotics, April 2010 (misc)

Machine-Learning Methods for Decoding Intentional Brain States

Hill, NJ.

Symposium "Non-Invasive Brain Computer Interfaces: Current Developments and Applications" (BIOMAG), March 2010 (talk)

Abstract

Brain-computer interfaces (BCI) work by making the user perform a specific mental task, such as imagining moving body parts or performing some other covert mental activity, or attending to a particular stimulus out of an array of options, in order to encode their intention into a measurable brain signal. Signal-processing and machine-learning techniques are then used to decode the measured signal to identify the encoded mental state and hence extract the user&amp;lsquo;s initial intention. The high-noise high-dimensional nature of brain-signals make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since it doesn&amp;lsquo;t matter what classifier you use once your features are extracted. Using examples from our own MEG and EEG experiments, I&amp;lsquo;ll demonstrate how machine-learning principles can be applied in order to improve BCI performance, if they are formulated in a domain-specific way. The result is a type of data-driven analysis that is more than just classification, and can be used to find better feature extractors.

ei Hill, NJ. Machine-Learning Methods for Decoding Intentional Brain States Symposium "Non-Invasive Brain Computer Interfaces: Current Developments and Applications" (BIOMAG), March 2010 (talk)

PAC-Bayesian Analysis in Unsupervised Learning

Seldin, Y.

Foundations and New Trends of PAC Bayesian Learning Workshop, March 2010 (talk)

ei Seldin, Y. PAC-Bayesian Analysis in Unsupervised Learning Foundations and New Trends of PAC Bayesian Learning Workshop, March 2010 (talk)

High-Frequency Tactile Feedback for the da Vinci Surgical System

Standish, D., Gewirtz, J., McMahan, W., Martin, P., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010 (misc)

hi Standish, D., Gewirtz, J., McMahan, W., Martin, P., Kuchenbecker, K. J. High-Frequency Tactile Feedback for the da Vinci Surgical System Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010 (misc)

The Haptic Board

Jiang, Z., Bhoite, M., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, USA, March 2010 (misc)

hi Jiang, Z., Bhoite, M., Kuchenbecker, K. J. The Haptic Board Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, USA, March 2010 (misc)

Tactile Gaming Vest (TGV)

Palan, S., Wang, R., Naukam, N., Li, E., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010 (misc)

hi Palan, S., Wang, R., Naukam, N., Li, E., Kuchenbecker, K. J. Tactile Gaming Vest (TGV) Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010 (misc)

Realistic Haptic Contacts and Textures for Tablet Computing

Romano, J. M., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010, {B}est Teaser Award (misc)

hi Romano, J. M., Kuchenbecker, K. J. Realistic Haptic Contacts and Textures for Tablet Computing Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010, {B}est Teaser Award (misc)

GPU-Based Haptic Rendering of 3D Smoke

Yang, M., Lu, J., Safonova, A., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010 (misc)

hi Yang, M., Lu, J., Safonova, A., Kuchenbecker, K. J. GPU-Based Haptic Rendering of 3D Smoke Hands-on demonstration presented at IEEE Haptics Symposium, Waltham, Massachusetts, March 2010 (misc)

Learning Motor Primitives for Robotics

Kober, J., Peters, J.

EVENT Lab: Reinforcement Learning in Robotics and Virtual Reality, January 2010 (talk)

Abstract

The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.

ei Kober, J., Peters, J. Learning Motor Primitives for Robotics EVENT Lab: Reinforcement Learning in Robotics and Virtual Reality, January 2010 (talk)

\textscLpzRobots: A free and powerful robot simulator

Martius, G., Hesse, F., Güttler, F., Der, R.

\urlhttp://robot.informatik.uni-leipzig.de/software, 2010 (misc)

al Martius, G., Hesse, F., Güttler, F., Der, R. \textscLpzRobots: A free and powerful robot simulator \urlhttp://robot.informatik.uni-leipzig.de/software, 2010 (misc)

Playful Machines: Tutorial

Der, R., Martius, G.

\urlhttp://robot.informatik.uni-leipzig.de/tutorial?lang=en, 2010 (misc)

al Der, R., Martius, G. Playful Machines: Tutorial \urlhttp://robot.informatik.uni-leipzig.de/tutorial?lang=en, 2010 (misc)

2009

Machine Learning for Brain-Computer Interfaces

Hill, NJ.

Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

Abstract

Brain-computer interfaces (BCI) aim to be the ultimate in assistive technology: decoding a user‘s intentions directly from brain signals without involving any muscles or peripheral nerves. Thus, some classes of BCI potentially offer hope for users with even the most extreme cases of paralysis, such as in late-stage Amyotrophic Lateral Sclerosis, where nothing else currently allows communication of any kind. Other lines in BCI research aim to restore lost motor function in as natural a way as possible, reconnecting and in some cases re-training motor-cortical areas to control prosthetic, or previously paretic, limbs. Research and development are progressing on both invasive and non-invasive fronts, although BCI has yet to make a breakthrough to widespread clinical application. The high-noise high-dimensional nature of brain-signals, particularly in non-invasive approaches and in patient populations, make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since "it doesn‘t matter what classifier you use once you‘ve done your preprocessing right and extracted the right features." I shall show a few examples of how this runs counter to both the empirical reality and the spirit of what needs to be done to bring BCI into clinical application. Along the way I‘ll highlight some of the interesting problems that remain open for machine-learners.

2009

ei Hill, NJ. Machine Learning for Brain-Computer Interfaces Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

PAC-Bayesian Approach to Formulation of Clustering Objectives

Seldin, Y.

NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

Abstract

Clustering is a widely used tool for exploratory data analysis. However, the theoretical understanding of clustering is very limited. We still do not have a well-founded answer to the seemingly simple question of "how many clusters are present in the data?", and furthermore a formal comparison of clusterings based on different optimization objectives is far beyond our abilities. The lack of good theoretical support gives rise to multiple heuristics that confuse the practitioners and stall development of the field. We suggest that the ill-posed nature of clustering problems is caused by the fact that clustering is often taken out of its subsequent application context. We argue that one does not cluster the data just for the sake of clustering it, but rather to facilitate the solution of some higher level task. By evaluation of the clustering‘s contribution to the solution of the higher level task it is possible to compare different clusterings, even those obtained by different optimization objectives. In the preceding work it was shown that such an approach can be applied to evaluation and design of co-clustering solutions. Here we suggest that this approach can be extended to other settings, where clustering is applied.

ei Seldin, Y. PAC-Bayesian Approach to Formulation of Clustering Objectives NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data

Shelton, JA.

Women in Machine Learning Workshop (WiML), December 2009 (talk)

Abstract

Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, KCCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, KCCA may suffer from small sample effects. We propose to use semi-supervised Laplacian regularization to utilize data that are present in only one modality. This manifold learning approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned and such data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, Laplacian regularization improved performance whereas the semi-supervised variants of KCCA yielded the best performance. We additionally analyze the weights learned by the regression in order to infer brain regions that are important during different types of visual processing.

ei Shelton, JA. Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data Women in Machine Learning Workshop (WiML), December 2009 (talk)

Event-Related Potentials in Brain-Computer Interfacing

Hill, NJ.

Invited lecture on the bachelor & masters course "Introduction to Brain-Computer Interfacing", October 2009 (talk)

Abstract

An introduction to event-related potentials with specific reference to their use in brain-computer interfacing applications and research.

ei Hill, NJ. Event-Related Potentials in Brain-Computer Interfacing Invited lecture on the bachelor & masters course "Introduction to Brain-Computer Interfacing", October 2009 (talk)

BCI2000 and Python

Hill, NJ.

Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

Abstract

A tutorial, with exercises, on how to integrate your own Python code with the BCI2000 software package.

ei Hill, NJ. BCI2000 and Python Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

Implementing a Signal Processing Filter in BCI2000 Using C++

Hill, NJ., Mellinger, J.

Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

Abstract

This tutorial shows how the functionality of the BCI2000 software package can be extended with one‘s own code, using BCI2000‘s C++ API.

ei Hill, NJ., Mellinger, J. Implementing a Signal Processing Filter in BCI2000 Using C++ Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)