学术文献库

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.