Many of us have downloaded mindfulness apps like Headspace or Calm, and have probably benefited from spending 10 minutes each day focusing on our breathing. But reducing stress and softening the sharper edges of anxiety in this way are beginners' steps when it comes to the practice of meditation. Put in the hours, though, and you may well reach the deep end: a place where radical, long-lasting upgrades to how you feel and what you experience are possible. This reality has long been known by full-time contemplatives spending their lives in monasteries and caves. Now, these mental transformations are being examined and understood by neuroscientists at world-leading institutions.

While it is beneficial to objectively determine whether a subject is meditating, most research in the literature reports good results only in a subject-dependent manner. This study aims to distinguish the modified state of consciousness experienced during Rajyoga meditation from the resting state of the brain in a subject-independent manner using EEG data. Three architectures have been proposed and evaluated: The CSP-LDA Architecture utilizes common spatial pattern (CSP) for feature extraction and linear discriminant analysis (LDA) for classification. The CSP-LDA-LSTM Architecture employs CSP for feature extraction, LDA for dimensionality reduction, and long short-term memory (LSTM) networks for classification, modeling the binary classification problem as a sequence learning problem. The SVD-NN Architecture uses singular value decomposition (SVD) to select the most relevant components of the EEG signals and a shallow neural network (NN) for classification. The CSP-LDA-LSTM architecture gives the best performance with 98.2% accuracy for intra-subject classification. The SVD-NN architecture provides significant performance with 96.4\% accuracy for inter-subject classification. This is comparable to the best-reported accuracies in the literature for intra-subject classification. Both architectures are capable of capturing subject-invariant EEG features for effectively classifying the meditative state from the resting state. The high intra-subject and inter-subject classification accuracies indicate these systems' robustness and their ability to generalize across different subjects.

The first time I heard about the jhanas, they sounded too good to be true. These special mental states are described in the sacred texts of an ancient school of Buddhism. Today, advanced meditators usually access them by concentrating on something: a flame, their breath, the sense of loving kindness. The meditators unclench their minds bit by bit, until they reach a state of near-total absorption. If they direct that focus in just the right way, a sequence of intense experiences ensues, beginning with bliss and ending with full-body peace.

Human conscious flows can alter brain states. Such brain activities modulate energy consumptions, which can be manifest in the BOLD effect in fMRI experiment. The goal of this study is to identify whether there is difference in such BOLD effects between experienced Tai Chi master in meditation state and normal control subjects. In this experiment, both the meditator and the controls using their conscious to lead a flow periodically circling in their brain in axial, sagittal, and coronal orientations inside a MRI scanner. The experimental results showed significant differences between the meditator and the controls. The most important one is that the meditator activates frontal medial cortex and precuneous regions without any visual excitation, while the controls only utilize visual cortex and precuneous regions without any frontal medial excitation. These seems suggest that for performing the same tasks, the meditator is in cognitive control state, while the controls are in spatial imagination state.