AutomaticSleepScoringTool

The identification of behavioral states based on physiological measures such as EEG, EMG, and LFP is a non-trivial task and much needed for advancing research on topics such as sleep. Jacob Ellen and Michael Dash from Middlebury College created an automated neural network pipeline for classifying behavioral states in rats. Their AutomaticSleepScoringTool takes inputs from electrophysiological readings (EEG/EMG/LFP) and classifies behavioral states automatically. With minimal manual classification and low computational complexity, this artificial neural network can be used for accurate and efficient behavioral state classifications. A small initial training set (around 40 minutes) is recommended to achieve high classification performance. Additionally, algorithmic errors of behavioral states can be manually re-scored to improve models fit. This algorithm uses R with additional packages (Keras and TensorFLow) and is entirely open-source.

Thanks to Caleb Darden, a rising senior majoring in Neuroscience at American University, for writing up this post.

This research tool was created by your colleagues. Please acknowledge the Principal Investigator, cite the article in which the tool was described, and include an RRID in the Materials and Methods of your future publications.  RRID:SCR_022300