GR-519 Meditation as an intervention to improve Student Attention An EEG study based on Machine learning prediction and Spectral Ratio Analysis

GR-519 Meditation as an intervention to improve Student Attention An EEG study based on Machine learning prediction and Spectral Ratio Analysis

https://ccse.kennesaw.edu/computing-showcase/cday-programs/fall23program.php

This research aims to develop a machine learning model using EEG data to identify student inattention, serving as an early intervention tool. Attention deficit, influenced by social media, adversely affects student performance. ADHD, characterized by inattention, hyperactivity, and impulsivity, is linked to academic challenges. Early detection in academics is crucial. A Machine Learning model was designed, and trained on an attention dataset with 34 EEG recordings of young adults. The raw EEG data was pre-processed and filtered, ICA was applied, and spectral analysis was done. Guided meditation with music was developed as an intervention to improve attention. EEG recordings from 15 young adults during a visual reasoning test assessed the model's efficacy by comparing model output to test scores. The ML model achieved a 98% accuracy rate in classifying attention vs inattention states.