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25 results (View BibTeX file of all listed publications)

2013

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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)

hi

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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)

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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

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

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

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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

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

pi

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

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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

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

pi

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

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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

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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)

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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

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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)

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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

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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)

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Dry adhesives and methods of making dry adhesives

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

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

pi

[BibTex]

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

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Domain Generalization via Invariant Feature Representation

Muandet, K.

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

ei

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

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2009

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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.

ei

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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)

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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

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

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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

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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)

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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

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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)

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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

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

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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

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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)

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Randomized algorithms for statistical image analysis based on percolation theory

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

27th European Meeting of Statisticians (EMS), July 2009 (talk)

Abstract
We propose a novel probabilistic method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation and random graph theories (see Grimmett (1999)). We address the problem of detection and estimation of signals in situations where the signal-to-noise ratio is particularly low. We present an algorithm that allows to detect objects of various shapes in noisy images. The algorithm has linear complexity and exponential accuracy. Our algorithm substantially di ers from wavelets-based algorithms (see Arias-Castro et.al. (2005)). Moreover, we present an algorithm that produces a crude estimate of an object based on the noisy picture. This algorithm also has linear complexity and is appropriate for real-time systems. We prove results on consistency and algorithmic complexity of our procedures.

ei

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ei Davies, P., Langovoy, M., Wittich, O. Randomized algorithms for statistical image analysis based on percolation theory 27th European Meeting of Statisticians (EMS), July 2009 (talk)

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Learning Motor Primitives for Robotics

Kober, J., Peters, J., Oztop, E.

Advanced Telecommunications Research Center ATR, June 2009 (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

[BibTex]

ei Kober, J., Peters, J., Oztop, E. Learning Motor Primitives for Robotics Advanced Telecommunications Research Center ATR, June 2009 (talk)

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Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer

Lampert, C.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009 (talk)

ei

Web [BibTex]

ei Lampert, C. Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009 (talk)

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Displaying Realistic Contact Accelerations Via a Dedicated Vibration Actuator

McMahan, W., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE World Haptics Conference, Salt Lake City, Utah, Proc. IEEE World Haptics Conference, pp. 613–614, Salt Lake City, Utah, USA, March 2009, {B}est Demonstration Award (misc)

hi

[BibTex]

hi McMahan, W., Kuchenbecker, K. J. Displaying Realistic Contact Accelerations Via a Dedicated Vibration Actuator Hands-on demonstration presented at IEEE World Haptics Conference, Salt Lake City, Utah, Proc. IEEE World Haptics Conference, pp. 613–614, Salt Lake City, Utah, USA, March 2009, {B}est Demonstration Award (misc)

[BibTex]

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The iTorqU 1.0 and 2.0

Winfree, K. N., Gewirtz, J., Mather, T., Fiene, J., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE World Haptics Conference, Salt Lake City, Utah, March 2009 (misc)

hi

[BibTex]

hi Winfree, K. N., Gewirtz, J., Mather, T., Fiene, J., Kuchenbecker, K. J. The iTorqU 1.0 and 2.0 Hands-on demonstration presented at IEEE World Haptics Conference, Salt Lake City, Utah, March 2009 (misc)

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Vibrotactile Feedback System for Intuitive Upper-Limb Rehabilitation

Kapur, P., Premakumar, S., Jax, S. A., Buxbaum, L. J., Dawson, A. M., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE World Haptics Conference, Salt Lake City, Utah, USA, Proc. IEEE World Haptics Conference, pp. 621–622, March 2009 (misc)

hi

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hi Kapur, P., Premakumar, S., Jax, S. A., Buxbaum, L. J., Dawson, A. M., Kuchenbecker, K. J. Vibrotactile Feedback System for Intuitive Upper-Limb Rehabilitation Hands-on demonstration presented at IEEE World Haptics Conference, Salt Lake City, Utah, USA, Proc. IEEE World Haptics Conference, pp. 621–622, March 2009 (misc)

[BibTex]

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The SlipGlove

Romano, J. M., Gray, S. R., Jacobs, N. T., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE World Haptics Conference, Salt Lake City, Utah, March 2009 (misc)

hi

[BibTex]

hi Romano, J. M., Gray, S. R., Jacobs, N. T., Kuchenbecker, K. J. The SlipGlove Hands-on demonstration presented at IEEE World Haptics Conference, Salt Lake City, Utah, March 2009 (misc)

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Real-Time Graphic and Haptic Simulation of Deformable Tissue Puncture

Romano, J. M., Safonova, A., Kuchenbecker, K. J.

Hands-on demonstration presented at Medicine Meets Virtual Reality, Long Beach, California, USA, January 2009 (misc)

hi

[BibTex]

hi Romano, J. M., Safonova, A., Kuchenbecker, K. J. Real-Time Graphic and Haptic Simulation of Deformable Tissue Puncture Hands-on demonstration presented at Medicine Meets Virtual Reality, Long Beach, California, USA, January 2009 (misc)

[BibTex]