Detecting moments of distraction during meditation practice based on changes in the EEG signal

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.

Study at a glance

Abstract

Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neurofeedback. One of the growing applications of EEG neurofeedback is to facilitate meditation practice. Specifically, EEG neurofeedback can be used to alert participants whenever they get distracted during meditation practice based on changes in their brain activity. In this study, we develop machine learning models to detect moments of distraction (due to mind wandering or drowsiness) during meditation practice using EEG signals. We use EEG data of 24 participants while performing a breath focus meditation with experience sampling and extract twelve linear and non-linear EEG features. Features are fed to ten supervised machine learning models to classify (i) Breath Focus Awake (BFA) vs Breath Focus Sleepy (BFS), and (ii) BFA vs Mind Wandering (MW). We observe that the linear features achieve a maximum accuracy of 86% for classifying awake (BFA) and sleepy (BFS), whereas non-linear features have more predictive ability for classifying between BFA and MW with a maximum accuracy of nearly 78% . In addition, visualization of unsupervised t-SNE lower embeddings supports the evidence of distinct clusters for each condition. Overall our results show that machine learning algorithms can successfully identify periods of distraction during meditation practice in novice meditators based on linear and non-linear features of the EEG signal. Consequently, our results have important implications for the development of mobile EEG neurofeedback protocols aimed at facilitating meditation practice.