Abstract
Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. To address this issue, a Deep Learning-based Meditation Accuracy Detection System is proposed. Using EEG (electroencephalogram) signals, the system detects the precision of meditation. Using a large dataset of EEG signals collected from experienced meditators, a deep learning model is trained. The model is able to recognize patterns and characteristics in the EEG signals that indicate the level of concentration and precision attained during meditation. Potential applications of the proposed system include enhancing the efficacy of meditation practices, assisting individuals in monitoring their progress, and enabling researchers to investigate the neural mechanisms underlying meditations. Preliminary findings indicate that the proposed system can detect the level of meditation precision with high precision. This system has the potential to revolutionize the field of meditation by providing practitioners with objective feedback, facilitating the creation of personalized meditation programme, and allowing researchers to study the neural mechanisms underlying meditations.
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Welekar, R., Dubey, A. & Hablani, S. Meditation accuracy detection system using deep learning. Multimed Tools Appl 82, 43625–43633 (2023). https://doi.org/10.1007/s11042-023-15273-5
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DOI: https://doi.org/10.1007/s11042-023-15273-5