Master of Science (MS)
First Advisor's Name
First Advisor's Committee Title
Second Advisor's Name
Second Advisor's Committee Title
Third Advisor's Name
Dr. Wei-Chiang Lin
Third Advisor's Committee Title
Fourth Advisor's Name
Dr.Jessica Ramella Roman
Fourth Advisor's Committee Title
Electriencephalography, machine learning, Preksha, Signal Processing, Concentrative, Mindfulness
Date of Defense
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.
Joshi, Chintan, "EEG Spectral Changes Before and After an Eight-week Intervention Period of Preksha Meditation" (2016). FIU Electronic Theses and Dissertations. 2981.