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We develop and maintain open-source tools for neuroinformatics, including machine learning libraries and analysis pipelines. Our tools are designed to be scalable, and adaptable to a wide range of neuroscience and biomedical applications. We also share our models to facilitate collaboration and accelerate progress in the field. MNE-LSL is an open-source Python package that provides a robust framework for real-time brain signal streaming and processing. Integrated with MNE-Python, it acts as a high-level Python wrapper for the Lab Streaming Layer (LSL) C++ library, enabling seamless online neuroscience research and real-time applications. A Python-based online brain signal decoding framework to translate brain activity into actions or track cognitive states. It includes training and testing protocols which support Google Glass feedback, classifier training, signal visualization and recording. This First-Prize winning Python code can be run independently on any Python environment or in Azure Machine Learning Studio. This code provides the learning of Stochastic Context-Free Grammar (SCFG) structures and parameters. It was developed to learn hierarchical representations from various human tasks. This dataset provides 6 different types of structured activities, each represented as a sequence of various action components.
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