A flexible analytic wavelet transform and ensemble bagged tree model for electroencephalogram-based meditative mind-wandering detection

A flexible analytic wavelet transform and ensemble bagged tree model for electroencephalogram-based meditative mind-wandering detection

Blog Article

Mind-wandering (MW) is when an individual’s concentration drifts away from the task or activity.Researchers found a greater variability in electroencephalogram (EEG) signals due to MW.Collecting more nuanced information from raw EEG data to examine the harmful effects of MW is time-consuming.This study proposes a multi-resolution assessment of EEG signals using the flexible analytic wavelet transform (FAWT).

The Mushroom Vapes FAWT algorithm decomposes raw EEG data into more representative sub-bands (SBs).Several statistical characteristics are derived from the obtained SBs, and the effects of MW Pokers during meditation on the EEG signals are investigated.A set of significant characteristics is chosen and fed into the machine learning modules using a 10-fold validation approach to detect MW subjects automatically.Our proposed framework attained the highest classification accuracy of 92.

41%, the highest sensitivity of 93.56%, and the highest specificity of 91.97%.The proposed framework can be used to design a suitable brain-computer interface (BCI) system to reduce MW and increase meditation depth for holistic and long-term health in society.