Detecting moments of distraction during meditation practice based on changes in the EEG signal
Pankaj Pandey, Julio Rodriguez-Larios, Krishna Prasad Miyapuram et al.
Machine learning models can detect moments of distraction during meditation by analyzing EEG signals. Using data from 24 novice meditators performing breath focus meditation, researchers extracted twelve linear and non-linear EEG features and tested ten supervised classifiers. Linear features achieved up to 86% accuracy in distinguishing awake from sleepy states, while non-linear features reached nearly 78% accuracy in distinguishing awake from mind-wandering states. Unsupervised t-SNE visualization confirmed distinct clusters for each condition. These findings support the development of mobile EEG neurofeedback protocols that alert meditators when they become distracted.