Jan Hendrik Metzen

pySPACE—a signal processing and classification environment in Python

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.

Matching of anatomical tree structures for registration of medical images

Many medical applications require a registration of different images of the same organ. In many cases, such a registration is accomplished by manual placement of landmarks in the images. In this paper, we propose a method which is able to find reasonable landmarks automatically. To achieve this, bifurcations of the vessel systems, which have been extracted from the images by a segmentation algorithm, are assigned by the so-called association graph method and the coordinates of these matched bifurcations can be used as landmarks for a non-rigid registration algorithm. Several constraints to be used in combination with the association graph method are proposed and evaluated on a ground truth consisting of anatomical trees from liver and lung. Furthermore, a method for preprocessing (tree pruning) as well as for postprocessing (clique augmentation) are proposed and evaluated on this ground truth. The proposed method achieves promising results for anatomical trees of liver and lung and for medical images obtained with different modalities and at different points in time.

Performance evaluation of EANT in the robocup keepaway benchmark

Several methods have been proposed for solving reinforcement learning (RL) problems. In addition to temporal difference (TD) methods, evolutionary algorithms (EA) are among the most promising approaches. The relative performance of these approaches in certain subdomains of the general RL problem remains an open question at this time. In addition to theoretical analysis, benchmarks are one of the most important tools for comparing different RL methods in certain problem domains. A recently proposed RL benchmark problem is the Keepaway benchmark, which is based on the RoboCup Soccer Simulator. This benchmark is one of the most challenging multiagent learning problems because its state-space is continuous and high dimensional, and both the sensors and actuators are noisy. In this paper we analyze the performance of the neuroevolutionary approach called evolutionary acquisition of neural topologies (EANT) in the Keepaway benchmark, and compare the results obtained using EANT with the results of other algorithms tested on the same benchmark.

Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions

PurposeAdvances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room.MethodsThe review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive.ResultsStudies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices.ConclusionML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical robotics. Current devices possess no intelligence whatsoever and are merely advanced and expensive instruments.

A common genetic encoding for both direct and indirect encodings of networks

In this paper we present a Common Genetic Encoding (CGE) for networks that can be applied to both direct and indirect encoding methods. As a direct encoding method, CGE allows the implicit evaluation of an encoded phenotype without the need to decode the phenotype from the genotype. On the other hand, one can easily decode the structure of a phenotype network, since its topology is implicitly encoded in the genotypes gene-order. Furthermore, we illustrate how CGE can be used for the indirect encoding of networks. CGE has useful properties that makes it suitable for evolving neural networks. A formal definition of the encoding is given, and some of the important properties of the encoding are proven such as its closure under mutation operators, its completeness in representing any phenotype network, and the existence of an algorithm that can evaluate any given phenotype without running into an infinite loop.

Minimizing calibration time for brain reading

Machine learning is increasingly used to autonomously adapt brain-machine interfaces to user-specific brain patterns. In order to minimize the preparation time of the system, it is highly desirable to reduce the length of the calibration procedure, during which training data is acquired from the user, to a minimum. One recently proposed approach is to reuse models that have been trained in historic usage sessions of the same or other users by utilizing an ensemble-based approach. In this work, we propose two extensions of this approach which are based on the idea to combine predictions made by the historic ensemble with session-specific predictions that become available once a small amount of training data has been collected. These extensions are particularly useful for Brain Reading Interfaces (BRIs), a specific kind of brain-machine interfaces. BRIs do not require that user feedback is given and thus, additional training data may be acquired concurrently to the usage session. Accordingly, BRIs should initially perform well when only a small amount of training data acquired in a short calibration procedure is available and allow an increased performance when more training data becomes available during the usage session. An empirical offline-study in a testbed for the use of BRIs to support robotic telemanipulation shows that the proposed extensions allow to achieve this kind of behavior.

Matching of tree structures for registration of medical images

Many medical applications require a registration of different images of the same organ. In many cases, such a registration is accomplished by manually placing landmarks in the images. In this paper we propose a method which is able to find reasonable landmarks automatically. To achieve this, nodes of the vessel systems, which have been extracted from the images by a segmentation algorithm, will be assigned by the so-called association graph method and the coordinates of these matched nodes can be used as landmarks for a non-rigid registration algorithm.

Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface

A major barrier for a broad applicability of brain-computer interfaces (BCIs) based on electroencephalography (EEG) is the large number of EEG sensor electrodes typically used. The necessity for this results from the fact that the relevant information for the BCI is often spread over the scalp in complex patterns that differ depending on subjects and application scenarios. Recently, a number of methods have been proposed to determine an individual optimal sensor selection. These methods have, however, rarely been compared against each other or against any type of baseline. In this paper, we review several selection approaches and propose one additional selection criterion based on the evaluation of the performance of a BCI system using a reduced set of sensors. We evaluate the methods in the context of a passive BCI system that is designed to detect a P300 event-related potential and compare the performance of the methods against randomly generated sensor constellations. For a realistic estimation of the reduced systems performance we transfer sensor constellations found on one experimental session to a different session for evaluation. We identified notable (and unanticipated) differences among the methods and could demonstrate that the best method in our setup is able to reduce the required number of sensors considerably. Though our application focuses on EEG data, all presented algorithms and evaluation schemes can be transferred to any binary classification task on sensor arrays.

Accelerating neuroevolutionary methods using a Kalman filter

In recent years, neuroevolutionary methods have shown great promise in solving learning tasks, especially in domains that are stochastic, partially observable, and noisy. In this paper, we show how the Kalman filter can be exploited (1) to efficiently find an optimal solution (i. e. reducing the number of evaluations needed to find the solution), (2) to find solutions that are robust against noise, and (3) to recover or reconstruct missing state variables, traditionally known as state estimation in control engineering community. Our algorithm has been tested on the double pole balancing without velocities benchmark, and has achieved significantly better results on this benchmark than the published results of other algorithms to date.

Intuitive Interaction with Robots – Technical Approaches and Challenges

A challenging goal in human-robot interaction research is to build robots that are intuitive interaction partners for humans. Although some research does focus on building robots which look and behave exactly like a human, even simple toylike robots can be accepted as adequate and intuitive interaction partners. However, for complex interaction tasks, intelligent support, or cooperative behavior more advanced and ”on board” solutions have to be developed, that still support natural interaction behavior between human and robot. This chapter will discuss some relevant research in the field of human-robot interaction which is fundamental for more complex but still intuitive interaction. The focus is to convey the complexity of research that is required and to point out different research areas which are relevant to achieve the goal of developing robots that can be natural interaction partners for humans.