Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

Objective: We investigate the design of deep recurrent neural networks for detecting sleep stages from single channel EEG signals recorded at home by non-expert users. We report the effect of data set size, architecture choices, regularization, and personalization on the classification performance.Methods: We evaluated 58 different architectures and training configurations using three-fold cross validation.Results: A network consisting of convolutional (CONV) layers and long short term memory (LSTM) layers can achieve an agreement with a human annotator of Cohen's Kappa of ~0.73 using a training data set of 19 subjects. Regularization and personalization do not lead to a performance gain.Conclusion: The optimal neural network architecture achieves a performance that is very close to the previously reported human inter-expert agreement of Kappa 0.75.Significance: We give the first detailed account of CONV/LSTM network design process for EEG sleep staging in single channel home based setting

Ordered Means Models for recognition, reproduction, and organization of interaction time series

Großekathöfer U. Ordered Means Models for recognition, reproduction, and organization of interaction time series. Bielefeld: Bielefeld University; 2013

Adaptive and Reactive Sensor Technology for Musical Instruments: Teaching, Exercising and Pedagogy

Großhauser T, Großekathöfer U, Hermann T. Adaptive and Reactive Sensor Technology for Musical Instruments: Teaching, Exercising and Pedagogy. In: Mornell A, ed. Art in Motion II - Motor Skills, Motivation, and Musical Practice. Frankfurt am Main, Berlin, Bern, Bruxelles, New York, Oxford, Wien: Peter Lang; 2012: 195-224

Learning Hierarchical Prototypes of Motion Time Series for Interactive Systems

Großekathöfer U, Shlomo G, Hermann T, Kopp S. Learning Hierarchical Prototypes of Motion Time Series for Interactive Systems. In: Proceedings of the 1st Workshop on Machine Learning for Interactive Systems. Montpellier, France; 2012: 37-42

On-the-fly behavior coordination for interactive virtual agents – A model for learning, recognizing and reproducing hand-arm gestures online

Großekathöfer U, Wöhler N-C, Hermann T, Kopp S. On-the-fly behavior coordination for interactive virtual agents – A model for learning, recognizing and reproducing hand-arm gestures online. In: 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2012). 2012

BCI Competition 2003 - Dataset IIb: Support Vector Machines for the P300 speller paradigm

Kaper M, Meinicke P, Großekathöfer U, Lingner T, Ritter H. BCI Competition 2003 - Dataset IIb: Support Vector Machines for the P300 speller paradigm. IEEE Transactions on Biomedical Engineering. 2004;51(6):1073-1076.We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing

Learning of Object Manipulation Operations from Continuous Multimodal Input

Großekathöfer U, Barchunova A, Haschke R, Hermann T, Franzius M, Ritter H. Learning of Object Manipulation Operations from Continuous Multimodal Input. In: IEEE/RAS International Conference on Humanoid Robots 2011. 2011.In this paper we propose an approach for identification of high-level object manipulation operations within a continuous multimodal time-series. We focus on a multimodal approach for robust recognition of action primitive data. Our procedure combines an unsupervised Bayesian multimodal segmentation with a supervised machine learning approach. We briefly outline (1) the unsupervised segmentation and selection of uni- and bi-manual manipulation primitives developed in our previous work. We show (2) an application of the ordered means models to classification of estimated segments. To assess the performance of our approach, we compare the computed labels to the ground truth acquired during the data recording. In our experiments we examined the robustness of the procedure on two different sets of segments: full length (≈ 95% overlap with the ground truth on average), partial length (≈ 10% overlap with ground truth on average). We have achieved a cross validation rate of ≈ 0.95 and recognition accuracy of ≈ 0.97 for full length and ≈ 0.84 for partial length test sets