crowdEEG is a collaborative annotation tool for medical time series data. It enables groups of experts or non-expert crowds to perform feature detection or high-level classification tasks.

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The underlying vision of this project is to combine machine and human intelligence for the scalable and accurate analysis of human clinical EEG recordings. For this purpose, we design and implement a computational framework which will selectively elicit feedback from clinical experts and non-expert crowds to train machine learning algorithms for highly accurate classifications of human clinical EEG data.

In the course of this project, we plan to open-source the tool chains, developed by our team, facilitating pre-processing, feature extraction and machine learning procedures in the context of EEG data. Likewise, a long-term goal of this project is to prepare and make publicly available a high-quality dataset of human clinical polysomnograms, i.e., multi-channel biosignal recordings of human subjects during sleep, to support research endeavours of other research groups in this field.