Benjamin Blankertz

Oleksandr Zlatov, Benjamin Blankertz

Most EEG-based Brain-Computer Interfaces (BCIs) require a considerable amount of training data to calibrate the classification model, owing to the high variability in the EEG data, which manifests itself between participants, but also within participants from session to session (and, of course, from trial to trial). In general, the more complex the model, the more data for training is needed. We suggest a novel technique for augmenting the training data by generating new data from the data set at hand. Different from existing techniques, our method uses backward and forward projection using source localization and a head model to modify the current source dipoles of the model, thereby generating inter-participant variability in a physiologically meaningful way. In this manuscript, we explain the method and show first preliminary results for participant-independent motor-imagery classification. The accuracy was increased when using the proposed method of data augmentation by 13, 6 and 2 percentage points when using a deep neural network, a shallow neural network and LDA, respectively.

Bastian Venthur, Benjamin Blankertz

A Brain-Computer Interface (BCI) is a system that measures central nervous system activity and translates the recorded data into an output suitable for a computer to use as an input signal. Such a BCI system consists of three parts, the signal acquisition, the signal processing and the feedback/stimulus presentation. In this paper we present Wyrm, a signal processing toolbox for BCI in Python. Wyrm is applicable to a broad range of neuroscientific problems and capable for running online experiments in real time and off-line data analysis and visualisation.

Luís F. Seoane, Stephan Gabler, Benjamin Blankertz

This paper provides a proof of concept for an EEG-based reconstruction of a visual image which is on a user's mind. Our approach is based on the Rapid Serial Visual Presentation (RSVP) of polygon primitives and Brain-Computer Interface (BCI) technology. The presentation of polygons that contribute to build a target image (because they match the shape and/or color of the target) trigger attention-related EEG patterns. Accordingly, these target primitives can be determined using BCI classification of Event-Related Potentials (ERPs). They are then accumulated in the display until a satisfactory reconstruction is reached. Selection steps have an average classification accuracy of $75\%$. $25\%$ of the images could be reconstructed completely, while more than $65\%$ of the available visual details could be captured on average. Most of the misclassifications were not misinterpretations of the BCI concerning users' intent; rather, users tried to select polygons that were different than what was intended by the experimenters. Open problems and alternatives to develop a practical BCI-based image reconstruction application are discussed.