PyData Yerevan 2022

The attempt to decode the human brain using computers is not novel, however, doing it dynamically in an uncontrolled environment with many external confounding factors has been deemed to be very challenging computationally, and hence, yet to be explored in depth. This study aims to predict the human physiological behaviors using machine learning and invasively recorded intracranial field potentials received through electroctrocortigography (ECoG) procedure from the brain surface in an uncontrolled real life setting. After a rigorous feature engineering process I showcase that the well-defined behaviors such as sleeping, eating and video gaming can be decoded with greater than 0.95 AUCs, and the noisier behaviors such as movements, spoken and heard speech are decoded with AUCs higher than 0.80. To ensure that the classification results are reliable I run a series of experiments with different controls and find that despite the drop in AUCs the behaviors are still robustly classified better than the random for all of the tests. I also dive deeper into exploring the brain regions which contributed to the high performance of the classification. Not only does this research show that it is possible to classify twelve natural continuous human behaviors with high performance, it also confirms many of the prior literature findings which state that certain brain region activities correspond to specific human physiological actions.