Assessing meditation through electroencephalographic data: a dynamical systems approach

Master thesis (2022)

Authors

F. van Engen Mechanical Engineering

Contributors

Anne Marie Brouwer TNO (supervisor 1)
Raf Van de Plas Team Raf Van de Plas - Mechanical, Maritime and Materials Engineering (supervisor 2)
Y. Vardar Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2022 Femke van Engen
EEG Eigenvalue-eigenvector decomposition Dynamic systems Meditation
More Info
expand_more

Published Date

19-12-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Mechanical Engineering

Abstract

Meditation is a contemplative practice that is believed to entail attentional and emotional regulation. One of the biggest challenges in developing personalized, accessible healthcare options with meditation is finding understandable features that signify whether someone is meditating. Specifically, there is no consensus on a feature resulting from the electroencephalogram (EEG) in the current body of literature on meditation.

In this thesis, I propose a dynamic systems analysis on EEG data to obtain a dynamic feature capable of distinguishing meditation from an eyes-closed resting baseline. I gathered the EEG data at TNO (Dutch Organisation for Applied Scientific Research) from twenty-two participants during a sixteen-minute loving-kindness meditation and two two-minute baselines. The proposed methodology characterizes temporal and spatial characteristics of the EEG simultaneously by approximating the EEG dynamics with a linear model on short time windows. I assess changes among three features: the frequency and magnitude of the oscillatory dynamics and the corresponding active electrodes.

The analysis can identify changes in EEG dynamics for each individual. Across all participants, regions associated with vision and language processing were active throughout the experiment. Notably, attention-related regions were more involved during meditation than rest. Moreover, the results show a shift in active regions throughout the meditation and the baselines for several participants.

Moreover, the thesis investigates the sensitivity of the analytical approach to changes in the electrode subset used for the analysis. For each participant, I constructed a subset of electrodes that were most involved in the changing EEG dynamics. The personalized subset was most sensitive to changes between meditation and rest, compared to other subsets based on commercial wearable EEG headsets. Finally, I compare the findings of the dynamic systems approach to a conventional analytical approach, and the participants’ emotional ratings inquired in subjective questionnaires. Unfortunately, from the current data, there appears to be no relationship between the proposed features and the conventional measures or the subjective questionnaires.