  In the last few years,Brain-Computer Interfaces (BCIs) have progressed as an
emerging research area in the fields of human-computer interaction and
interactive systems.This is primarily due to the introduction of low-cost
electroencephalographic (EEG) systems that render BCI technology accessible for
non-medical research but also due to the advancements of signal processing and
machine learning methods.Consequently,BCIs could provide a wide new range of
possibilities in the way users interact with a computer system (e.g.,
neuroadaptive interfaces).However,major challenges must still be addressed for
BCI systems to mature into an established communication medium for effective
human-computer interaction. One of the major challenges involves the easy
integration of real-time processing pipelines with portable EEG systems for an
out-of-the-lab use. To date, despite the amount of options current open-source
tools provide, most toolboxes focus mainly in extending the processing and
classification methods but lack on the ability to provide an easy-to-design yet
extensible architecture for ubiquitous use.Here, we present NeuXus, a modular
toolbox in Python for real-time biosignal processing and pipeline design.NeuXus
is open-source and platform independent,providing high-level implementation of
processing pipelines for easy BCI design and deployment.
