Classifying EEG Signals of Mind-Wandering Across Different Styles of Meditation

Shivam Chaudhary*, Pankaj Pandey, Krishna Prasad Miyapuram, Derek Lomas

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


In the modern world, it is easy to get lost in thought, partly because of the vast knowledge available at our fingertips via smartphones that divide our cognitive resources and partly because of our intrinsic thoughts. In this work, we aim to find the differences in the neural signatures of mind-wandering and meditation that are common across different meditative styles. We use EEG recording done during meditation sessions by experts of different meditative styles, namely shamatha, zazen, dzogchen, and visualization. We evaluate the models using the leave-one-out validation technique to train on three meditative styles and test the fourth left-out style. With this method, we achieve an average classification accuracy of above 70%, suggesting that EEG signals of meditation techniques have a unique neural signature across meditative styles and can be differentiated from mind-wandering states. In addition, we generate lower-dimensional embeddings from higher-dimensional ones using t-SNE, PCA, and LLE algorithms and observe visual differences in embeddings between meditation and mind-wandering. We also discuss the general flow of the proposed design and contributions to the field of neuro-feedback-enabled mind-wandering detection and correction devices.

Original languageEnglish
Title of host publicationBrain Informatics - 15th International Conference, BI 2022, Proceedings
EditorsMufti Mahmud, Jing He, Stefano Vassanelli, André van Zundert, Ning Zhong
Number of pages12
ISBN (Print)978-3-031-15036-4
Publication statusPublished - 2022
Event15th International Conference on Brain Informatics, BI 2022 - Virtual, Online
Duration: 15 Jul 202217 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13406 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Brain Informatics, BI 2022
CityVirtual, Online

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
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.


  • Classification
  • Cognition
  • Deep learning
  • EEG
  • Machine learning
  • Meditation
  • Mind-wandering
  • Neuro-feedback


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