Wyrm: A Brain-Computer Interface Toolbox in Python.

In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm ( https://github.com/bbci/wyrm ), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used as a toolbox for analysis and visualization of neurophysiological data and in real-time settings, like an online BCI application. In order to prevent software defects, Wyrm makes extensive use of unit testing. We will explain the key aspects of Wyrm's software architecture and design decisions for its data structure, and demonstrate and validate the use of our toolbox by presenting our approach to the classification tasks of two different data sets from the BCI Competition III. Furthermore, we will give a brief analysis of the data sets using our toolbox, and demonstrate how we implemented an online experiment using Wyrm. With Wyrm we add the final piece to our ongoing effort to provide a complete, free and open source BCI system in Python.

Exploring motion VEPs for gaze-independent communication.

Motion visually evoked potentials (mVEPs) have recently been explored as input features for brain-computer interfaces, in particular for the implementation of visual spellers. Due to low contrast and luminance requirements, motion-based intensification is less discomforting to the user than conventional approaches. So far, mVEP spellers were operated in the overt attention mode, wherein eye movements were allowed. However, the dependence on eye movements limits clinical applicability. Hence, the purpose of this study was to evaluate the suitability of mVEPs for gaze-independent communication. Sixteen healthy volunteers participated in an online study. We used a conventional speller layout wherein the possible selections are presented at different spatial locations both in the overt attention mode (fixation of the target) and the covert attention mode (central fixation). Additionally, we tested an alternative speller layout wherein all stimuli are sequentially presented at the same spatial location (foveal stimulation), i.e. eye movements are not required for selection. As can be expected, classification performance breaks down when switching from the overt to the covert operation. Despite reduced performance in the covert setting, conventional mVEP spellers are still potentially useful for users with severely impaired eye movements. In particular, they may offer advantages--such as less visual fatigue--over spellers using flashing stimuli. Importantly, the novel mVEP speller presented here recovers good performance in a gaze-independent setting by resorting to the foveal stimulation.

Mushu, a free- and open source BCI signal acquisition, written in Python.

The following paper describes Mushu, a signal acquisition software for retrieval and online streaming of Electroencephalography (EEG) data. It is written, but not limited, to the needs of Brain Computer Interfacing (BCI). It's main goal is to provide a unified interface to EEG data regardless of the amplifiers used. It runs under all major operating systems, like Windows, Mac OS and Linux, is written in Python and is free- and open source software licensed under the terms of the GNU General Public License.

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.