EEG daydreaming, a machine learning approach to detect daydreaming activities

Xiaodong Qu

Abstract: In this paper, we propose a new method to detect noise hindrances in Electroencephalographic (EEG) signals caused by mental distractions, which we named "daydreaming signals." Our approach is based on sliding windows and aims to detect and locate these daydreaming signals to specific points in time. We expect to get cleaner data and, therefore, higher prediction accuracy in current available EEG datasets by removing these daydreaming signals. Beyond these improvements to existing data, this approach also has the potential to improve the quality of future data collection, as researchers can discover the pattern of daydreaming signals in trial rounds and deal with these signals accordingly.