EEG Patterns on Learning Concentration Using Short Learning Videos Based on Time-Frequency Domain

Learning concentration has a very important role in every learning process. One way to maintain concentration and one of the most popular methods in learning used today is utilizing video learning media. However, the most common problem is learning videos that are less interesting and boring. But, until now, there are no systems or parameters to assess how effective a learning video is on student concentration. Electroencephalography (EEG) is a device or sensor that can be used to observe and record images of electrical activity in the brain in response to stimulation so that EEG can be used as a measuring tool that can recognize human concentration. Therefore, this study aims to observe brain signal patterns related to participants' concentration while watching short learning videos. In this study, time-frequency domain features such as MAV, Standard Deviation, and PSD of the EEG signal, are used to analyze the participant's concentration while watching learning videos. After watching the video, the participants were asked to fill out questionnaires to state their concentration. Based on their questionnaire answers, there are three states of concentration (full concentration, partial concentration, and distracted), the data were then labeled according to these states. From the extraction feature results, it can be found that participants who claimed they were distracted tend to have lower energy values in alpha, beta, and gamma bands than those who claimed to have a full concentration state in MAV and PSD values. However, this pattern does not present in the standard deviation feature.