Remove unwanted artifacts in real-time using NeuroPype’s automated artifact rejection algorithms. Obtain clean EEG signals while removing artifacts due to eye and head movement, facial muscle activity, power line noise, and other common sources of interference.
Leverage pipelines for real-time EEG neuroimaging and brain connectivity analysis. Develop advanced neurofeedback paradigms targeting specific brain regions and networks.
Harness active BCI control using validated conventional motor imagery paradigms, or build your own paradigm from dozens of feature extraction and machine learning approaches.
Quickly build and apply workflows using our comprehensive algorithm library and customizable Quick-Start wizards. Missing something? No problem, you can readily add your own algorithms to NeuroPype’s modular Python framework. Export and share workflows between collaborators.
NeuroPype interfaces seamlessly, with the free and open source Lab Streaming Layer, supporting data I/O with most consumer and research EEG hardware and over 20 other device classes.