In the past, most of the related studies on the influence of meditation on cognitive functions were cross-sectional studies or longitudinal studies of short-term training. Cross-sectional research cannot rule out the possibility of pre-existing differences in the brains of long-term meditators. This difference may be related to their interest in meditation, and may also support their longer-term meditation training. Short-term longitudinal studies often emphasized their training effect can be produced within a few days to three months. Although these studies can better clarify the effects of meditation on cognitive functions, yet there are problems such as too short training time and whether the training effect can be sustained or strengthened. This project takes a longer longitudinal study within a controllable period, and uses a variety of cognitive tasks to explore the influence of meditation on cognitive functions within one and a half years, and at the same time explores the relationship between meditation effects and personality traits. In all experiments, a variety of personality trait scales were measured first. During the experiment, brain waves (electroencephalography; EEG) and heart rate signals (electrocardiography; EKG) were detected at the same time to understand the effects of meditation on cognitive functions and neural mechanisms. This project uses “adaptive data analysis” to analyze the EEG/EKG data acquired during experiments. Adaptive data analysis refers to all data analysis methods derived from empirical mode decomposition (EMD), such as Holo-Hilbert spectral analysis (HHSA) proposed recently (2016; the applicant for the project shared the co-authorship), and intrinsic multiscale entropy (iMSE). Adaptive data analysis can effectively quantify the nonlinear characteristics such as amplitude modulation (AM), frequency modulation (FM), cross-frequency brain network, and complexity in brainwave signals during cognitive tasks (or resting state), which is extremely important for understanding meditation and its neural mechanisms.
|Effective start/end date||1/08/21 → 31/10/22|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):