Document Type

Thesis

Degree

Master of Science (MS)

Department

Biomedical Engineering

First Advisor's Name

Dr.Ranu Jung

First Advisor's Committee Title

Major Professor

Second Advisor's Name

Dr.Jorge Riera

Second Advisor's Committee Title

committee member

Third Advisor's Name

Dr. Wei-Chiang Lin

Third Advisor's Committee Title

committee member

Fourth Advisor's Name

Dr.Jessica Ramella Roman

Fourth Advisor's Committee Title

committee member

Keywords

Electriencephalography, machine learning, Preksha, Signal Processing, Concentrative, Mindfulness

Date of Defense

11-10-2016

Abstract

Various types of meditation techniques, primarily categorized into concentrative and mindfulness meditation, have evolved over the years to enhance the physiological and psychological well-being of people in all walks of life. However, the scientific knowledge of the impact of meditation on physiological and psychological well-being is very limited. Electroencephalography (EEG) was used to study the effect of a sequence of different forms of Preksha meditation on brain activity. EEG data from 13 novice participants (10 females, 3 males; Age: 19-49 yrs) were collected while meditating for the first time (pre) and at the end of an eight week (post) intervention period (3 meditation sessions/week). EEG spectral power densities were calculated in delta (1-4Hz), theta (4-8Hz), alpha (8-13Hz), beta (13-40Hz) and gamma (40-100Hz) bands. A Support vector machine algorithm based on the radial basis function kernel was used to classify different forms of Preksha meditation. The SVM classification was able to differentiate the brain activity amongst the forms of Preksha meditation with 6-12% accuracy only. These accuracies are extremely low and the classification was not able to discriminate between different forms of meditation within a session. It is therefore concluded, that the format of Preksha meditation utilized did not elicit clear changes in EEG, discernable using the SVM algorithm.

Identifier

FIDC001244

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