Abstract
The meditation consists of various stages of concentrating on the feeling of peaceful realization of becoming a person and communion of the human soul with the supreme soul brain electric source position in the frequency domain used on multichannel EEG recordings to create activation differences between meditation and open eyed, task-free resting. EEG signals are collected after 3rd, 6th, 9th and 12th week of training with three types meditation Trans dental, Raja yoga and Mindfulness meditation. Then EEG signal are classified using transfer learning methods (VGG-16, VGG-19, ResNet-18 and GoogleNet). EEG showed reduced activity in delta and increased activity in low alpha frequencies. The percentage of the alpha activity in the total power was a better indicator of the state of meditation. With the opening of the eyes the total power and the percentage of alpha activity came down. The percentage of alpha activity was higher which signifies perfect meditation. Alpha and beta are highest in the midline central area (Cz) during the relaxed meditation state, and theta is higher in C3 and C4. After experimental evaluation, we observed that the outcomes of these models gives 99.4% accuracy with the VGG-16 transfer learning model.
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Kora, P., Rajani, A., Chinnaiah, M.C., Madhavi, K.R., Swaraja, K., Meenakshi, K. (2021). EEG-Based Brain-Electric Activity Detection During Meditation Using Spectral Estimation Techniques. In: Jyothi, S., Mamatha, D.M., Zhang, YD., Raju, K.S. (eds) Proceedings of the 2nd International Conference on Computational and Bio Engineering . Lecture Notes in Networks and Systems, vol 215. Springer, Singapore. https://doi.org/10.1007/978-981-16-1941-0_68
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DOI: https://doi.org/10.1007/978-981-16-1941-0_68
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