The goal of this repository is to create a computational framework for decoding beavioral variables from neural data.
The particular dataset that will be used to develop this framework is a decision-making dataset, where subjects have implanted stereoelectroencephalography (sEEG) electrodes that record neural data as they perform a virtual gambling game.
Machine learning models are used to identify which brain areas and time periods carry the most information about the subject's decision making. Greater model accuracy for a given electrode channel and time window, implies greater information being carried.