Fachgebiet Maschinelles Lernen

8 Items

Recent Submissions
Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing

Hwang, Han-Jeong ; Hahne, Janne Mathias ; Müller, Klaus-Robert (2017)

There are some practical factors, such as arm position change and donning/doffing, which prevent robust myoelectric control. The objective of this study is to precisely characterize the impacts of the two representative factors on myoelectric controllability in practical control situations, thereby providing useful references that can be potentially used to find better solutions for clinically ...

Learning from label proportions in brain-computer interfaces

Hübner, David ; Verhoeven, Thibault ; Schmid, Konstantin ; Müller, Klaus-Robert ; Tangermann, Michael ; Kindermans, Pieter-Jan (2017)

Objective Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in p...

Universal exact algorithm for globally augmented MAP inference in structured prediction

Bauer, Alexander (2017)

The ultimate goal of discriminative learning is to train a prediction system by optimizing a desired measure of performance. Unlike in the standard learning scenario with univariate real-valued outputs, in structured prediction we aim at predicting a structured label corresponding to complex objects such as sequences, alignments, sets, or graphs. Here, structural support vector machine (SSVM) e...

Zero training for BCI – Reality for BCI systems based on event-related potentials

Tangermann, Michael ; Kindermans, Pieter-Jan ; Schreuder, Martijn ; Schrauwen, Benjamin ; Müller, Klaus-Robert (2013)

This contribution reviews how usability in Brain- Computer Interfaces (BCI) can be enhanced. As an example, an unsupervised signal processing approach is presented, which tackles usability by an algorithmic improvement from the field of machine learning. The approach completely omits the necessity of a calibration recording for BCIs based on event-related potential (ERP) paradigms. The positive...

The hybrid brain-computer interface: a bridge to assistive technology?

Müller-Putz, Gernot R. ; Schreuder, Martijn ; Tangermann, Michael ; Leeb, R. ; Millán del, R. J. (2013)

Brain-Computer Interfaces (BCIs) can be extended by other input signals to form a so-called hybrid BCI (hBCI). Such an hBCI allows the processing of several input signals with at least one brain signal for control purposes, i.e. communication and environmental control. This work shows the principle, technology and application of hBCIs and discusses future objectives.

Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: a simulation study

Ewald, Arne ; Avarvand, Forooz Shahbazi ; Nolte, Guido (2013)

The investigation of functional neuronal synchronization has recently become a growing field of research. With high temporal resolution, electroencephalography and magnetoencephalography are well-suited measurement techniques to identify networks of interacting sources underlying the recorded data. The analysis of the data in terms of effective connectivity, nevertheless, contains intrinsic iss...

A critical assessment of the importance of seedling age in the system of rice intensification (sri) in Eastern India

Deb, Debal ; Lässig, Jörg ; Kloft, Marius (2012)

A survey of the system of rice intensification (SRI)-related literature indicates that different authors have drawn conflicting inferences about rice yield performances under the SRI, chiefly because the SRI methodology has been variously advocated, interpreted and implemented in the field using different rice varieties, seedling ages at transplantation, cultivation seasons and nutrient managem...

Transfer learning of gaits on a quadrupedal robot

Degrave, Jonas ; Burm, Michael ; Kindermans, Pieter-Jan ; Dambre, Joni ; Wyffels, Francis (2015)

Learning new gaits for compliant robots is a challenging multi-dimensional optimization task. Furthermore, to ensure optimal performance, the optimization process must be repeated for every variation in the environment, for example for every change in inclination of the terrain. This is unfortunately not possible using current approaches, since the time required for the optimization is simply t...