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

A platform-independent open-source feedback framework for BCI systems

This paper introduces the Pythonic Feedback Framework which provides a platform independent framework to develop BCI feedback applications in Python. It was designed to make the development of feedback applications as easy as possible. Existing solutions have either been implemented in C++, which makes the programming task rather tedious, especially for non-computer-scientists, or in Matlab, which is not well suited for more advanced visual (flickering is inavoidable which is unconfortable for the user and has side effects in the EEG) or auditory feedback applications. This framework solves this problem by moving the feedback implementations to a general purpose, and easy to learn language like Python. Python provides many so called bindings to other libraries, which allow it to develop high quality multimedia feedback applications, with little effort. The framework communicates with the rest of the BCI system via a standardized communication protocol using UDP and XML and is therefore suitable to be used with any BCI system that may be adapted to send its control signal via UDP in the specified format. Having such a general feedback framework will also foster a vivid exchange of feedback applications between BCI groups, even if individual system for processing and classification are used

Novel applications of BCI technology: Psychophysiological optimization of working conditions in industry

Brain Computer Interface (BCI) research advanced for more than forty years, providing a rich variety of sophisticated data analysis methods. Yet, most BCI studies have been restricted to the laboratory with controlled and undisturbed environment. BCI research was aiming at developing tools for communication and control. Recently, BCI research has broadened to explore novel applications for improved man-machine interaction. In the present study, we investigated the option to employ neurotechnology in an industrial environment for the psychophysiological optimization of working conditions in such settings. Our findings suggest that it is possible to use BCI-related analysis techniques to qualify responses of an operator by assessing the depth of cognitive processing on the basis of neuronal correlates of behaviourally relevant measures. This could lead to assistive technologies helping to avoid accidents in working environments by designing a collaborative workspace in wh ich the environment takes into account the actual cognitive mental state of the operator

Pyff - A pythonic framework for feedback applications and stimulus presentation in neuroscience

This paper introduces Pyff, the Pythonic feedback framework for feedback applications and stimulus presentation. Pyff provides a platform-independent framework that allows users to develop and run neuroscientific experiments in the programming language Python. Existing solutions have mostly been implemented in C++, which makes for a rather tedious programming task for non-computer-scientists, or in Matlab, which is not well suited for more advanced visual or auditory applications. Pyff was designed to make experimental paradigms (i.e., feedback and stimulus applications) easily programmable. It includes base classes for various types of common feedbacks and stimuli as well as useful libraries for external hardware such as eyetrackers. Pyff is also equipped with a steadily growing set of ready-to-use feedbacks and stimuli. It can be used as a standalone application, for instance providing stimulus presentation in psychophysics experiments, or within a closed loop such as in biofeedback or brain-computer interfacing experiments. Pyff communicates with other systems via a standardized communication protocol and is therefore suitable to be used with any system that may be adapted to send its data in the specified format. Having such a general, open-source framework will help foster a fruitful exchange of experimental paradigms between research groups. In particular, it will decrease the need of reprogramming standard paradigms, ease the reproducibility of published results, and naturally entail some standardization of stimulus presentation

Bci software platforms

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