EEG-Based Detection of Induced Relaxation

EEG-Based Detection of Induced Relaxation

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Relaxation, which refers to the absence of tension and stress, is important for health because extended periods of tension may lead to illness.This study detected induced relaxation through electroencephalography (EEG) for the real-time monitoring of brain activity.The Multivariate Empirical Mode Decomposition with Dynamic Phase-Synchronized Hilbert-Huang Transform (MEMD-DPS-HHT) algorithm was utilized to extract dynamic brain wave patterns from the EEG data of participants exposed to the blended essential oils.Preprocessing included artifact removal using independent seattle seahawks socks component analysis (ICA), bandpass filtering within the 4-12 Hz range, and normalization.

The EEG data were decomposed into five intrinsic mode functions (IMFs) using multivariate empirical mode decomposition (MEMD).The average frequencies of IMF1 (8.56 Hz) and IMF2 (4.91 Hz) are within the designated frequency range.

Dynamic phase synchronization and instantaneous frequency analysis employ an adaptive time-frequency-energy representation.The phase feline 1-hcpch vaccine synchronization analysis across channels indicated a maximum coherence of 0.87.The dynamic Hilbert spectra and relaxation indices, pre- and post-stimulation, showed an average maximum of 0.

16, aligning with the paired t-test results ( $p ge 0.05$ ), suggesting no significant difference in the Theta and Alpha bands (TA band) frequencies and stimulation.Two-way repeated measures ANOVA indicated $p =1$ , implying no significant effect of stimulation on the TA band.The results demonstrate the ability of the MEMD-DPS-HHT to analyze cross-channel, multi-dimensional, dynamic EEG data associated with relaxation.

The average relaxation index from this dataset was relatively low, suggesting that the impact of this blended essential oil stimulation may differ among individuals.