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

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

Pandey, Pankaj (IIT Gandhinagar)
Rodriguez-Larios, Julio (Columbia University)
Miyapuram, Krishna Prasad (IIT Gandhinagar)
Lomas, J.D. (TU Delft Design Aesthetics)

2023

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 nonlinear 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.

Protocols
Machine learning algorithms
Machine learning
Feature extraction
Brain modeling
Electroencephalography
Sensors

http://resolver.tudelft.nl/uuid:38286b9a-d4ee-45cb-b4c6-b70b78c6d819

https://doi.org/10.1109/APSCON56343.2023.10101045

IEEE, Piscataway

2023-10-17

978-1-6654-6164-1

APSCON 2023 - IEEE Applied Sensing Conference, Symposium Proceedings

2023 IEEE Applied Sensing Conference (APSCON), 2023-01-23 → 2023-01-25, Bengaluru, India

APSCON 2023 - IEEE Applied Sensing Conference, Symposium Proceedings

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 Pankaj Pandey, Julio Rodriguez-Larios, Krishna Prasad Miyapuram, J.D. Lomas