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 FAWT algorithm decomposes raw EEG data into more representative sub-bands (SBs).Several statistical characteristics are derived from the obtained Playing Cards SBs, and the effects of MW during meditation on the EEG signals Book 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.