This conceptual paper compares Vedic concepts—consciousness (Atman and Brahman), Sankalpa, Dharana, Dhyana, Mantra Shakti, and Karma—with Law of Attraction (LOA) practices such as manifestation, visualization, affirmation, intention setting, and gratitude. It does not aim to validate or invalidate LOA principles but presents a balanced perspective on their similarities and differences, interpreting both through contemporary psychological models. The paper discusses how selected Vedic concepts and LOA practices may be understood via modern psychology and considers their potential applications for goal setting and psychological well-being.
Machine learning models can distinguish between the neural oscillations of expert meditators and non-meditators using EEG data. The study analyzed EEG recordings from expert practitioners of Himalayan Yoga, Vipassana, and Isha Shoonya, along with non-expert controls. Thirteen different machine learning models were applied for within-subject and cross-subject classification across six conditions for both meditation and mind-wandering. Features extracted from the mean of 64 EEG time series were used. Within-subject classification achieved maximum accuracy. In cross-subject analysis, accuracy reached 18.3% above chance level in meditation between controls and Isha Shoonya, and over 18% above chance in mind-wandering between controls and Vipassana. These results suggest that both personalized and generalized models could guide novice practitioners to modulate brain signals toward expert levels.