Abstract:
Relaxation, defined as the absence of stress, is important for health, as prolonged periods of tension can lead to illness. This study detects induced relaxation through electroencephalography (EEG) for real-time monitoring of brain activities. This study employed the Multivariate Empirical Mode Decomposition with Dynamic Phase-Synchronized Hilbert-Huang Transform (MEMD-DPS-HHT) algorithm to extract dynamic patterns of brain waves. The dataset of essential oil blends was preprocessed to remove artifacts using independent component analysis (ICA), bandpass filtering in the 4-12 Hz frequency range, and normalization. The EEG data were decomposed into five Intrinsic Mode Functions (IMFs) utilizing the MEMD method. The average frequencies of IMF1 during the eyes-closed and eyes-open conditions are 8.56 Hz and 10.77 Hz, respectively. For IMF2, the frequencies are 4.91 Hz and 6.72 Hz, respectively. Phase synchronization analysis across channels revealed a maximum coherence of 0.87. The dynamic Hilbert spectra and relaxation indices, pre- and post-stimulation, averaged a maximum of 0.16. This finding was consistent with the paired t-test and factorial design experiment results, both of which showed no significant differences in theta and alpha bands pre- and post-stimulation. The findings indicate that MEMD-DPS-HHT effectively analyzes cross-channel, multidimensional, and dynamic EEG data related to relaxation.
Description:
Dissertation (Ph.D.) -- Computer Engineering, School of Applied Digital Technology. Mae Fah Luang University, 2025
