BNCI Horizon 2020 - Towards a Roadmap for Brain/Neural Computer Interaction

In this paper, we present BNCI Horizon 2020, an EU Coordination and Support Action (CSA) that will provide a roadmap for brain-computer interaction research for the next years, starting in 2013, and aiming at research efforts until 2020 and beyond. The project is a successor of the earlier EU-funded Future BNCI CSA that started in 2010 and produced a roadmap for a shorter time period. We present how we, a consortium of the main European BCI research groups as well as companies and end user representatives, expect to tackle the problem of designing a roadmap for BCI research. In this paper, we define the field with its recent developments, in particular by considering publications and EU-funded research projects, and we discuss how we plan to involve research groups, companies, and user groups in our effort to pave the way for useful and fruitful EU-funded BCI research for the next ten years

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Brain–computer interfaces (BCI) (also referred to as brain–machine interfaces; BMI) are, by definition, an interface between the human brain and a technological application. Brain activity for interpretation by the BCI can be acquired with either invasive or non-invasive methods. The key point is that the signals that are interpreted come directly from the brain, bypassing sensorimotor output channels that may or may not have impaired function. This paper provides a concise glimpse of the breadth of BCI research and development topics covered by the workshops of the 6th International Brain–Computer Interface Meeting

Analyse schaltender und driftender Dynamik mit neuronalen Netzen

In this work, a method for unsupervised segmentation and identification of time series is presented. It can be used to analyze dynamical systems with nonstationary switching or drifting behavior { a phenomenon observable in many natural, real-world domains. As examples we examine the dynamics of speech and physiological systems, but also many other complex systems, e.g. the financial markets, are expected to show such type of behavior. The ansatz is based on hard or soft competitive learning and a competitive advantage for temporally adjacent data points in a time series. The competition is performed by a set of (neural network) predictors that compete for the prediction of the data points. Each predictor is capable of predicting only stationary dynamics. Therefore, the predictors specialize during a training phase on those segments of the data where the dynamics is stationary to a large extent. This is achieved by means of a low-pass filter on the prediction errors, which can be motivated by the assumption of a low switching rate between different dynamical modes. A continuously alternating dynamics can then be modeled by a drift segmentation algorithm, which is also presented in this work. An application to physiological wake/sleep data (EEG, EOG, respiration) shows that the proposed method detects dynamical changes with a high resolution. Moreover, it autonomously yields a segmentation into wake and sleep stages which is in good agreement with the manual segmentation of a medical expert

Analysis of Drifting Dynamics with Competing Predictors

A method for the analysis of nonstationary time series with multiple modes of behaviour is presented. In particular, it is not only possible to detect a switching of dynamics but also a less abrupt, time consuming drift from one mode to another. This is achieved by an unsupervised algorithm for segmenting the data according to the modes and a subsequent search through the space of possible drifts. Applications to speech and physiological data demonstrate that analysis and modeling of real world time series can be improved when the drift paradigm is taken into account

Divisive Strategies for Predicting Non-Autonomous and Mixed Systems

We consider the problem of predicting time series originating from nonstationary and from mixed dynamical systems. It is shown that the complexity of finding representations for the dynamics of such systems can be drastically reduced if their composite nature is taken into account. Two paradigmatic cases are discussed and their solutions presented: jump processes and stationary mixtures. Examples demonstrate that divisive approaches can substantially improve predictions of time series compared to methods that model the dynamics globally

Identification of Non-stationary Dynamics in Physiological Recordings

We present a novel framework for the analysis of time series from dynamical systems which alternate between different operating modes. The method simultaneously segments and identifies the dynamical modes by using predictive models. In extension to previous approaches, it allows an identification of smooth transitions between successive modes. The method can be used for analysis, diagnosis, prediction, and control. In an application to EEG and respiratory data recorded from humans during afternoon naps, the obtained segmentations of the data agree with the sleep stage segmentation of a medical expert to a large extent. However, in contrast to the manual segmentation, our method does not require a-priori knowledge about physiology. Moreover, it has a high temporal resolution and reveals previously unclassified details of the transitions. In particular, a parameter is found that is potentially helpful for vigilance monitoring. We expect that the method will generally be usef..