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Identification of post-meditation perceptual states using wearable EEG and Self-Calibrating Protocols

Published: 26 June 2018 Publication History

Abstract

Since antiquity meditation in its various forms has been robustly associated with multiple physical and mental measurable changes of state. Obstacles to widespread access to the advantageous effects of meditation include the practice time necessary to become an expert meditator and the identification of a personalized beneficial meditative state once achieved. This work leverages previously published and patented results towards the application of Self-Calibrating Protocol (SCP)-assisted wearable EEG measurement of personalized "perceptual prints" and recognition of pre- versus post-meditation perceptual states. We describe avenues towards harvesting the newly enabled technologies towards quantification of meditation benefits and replication of beneficial meditative states leveraging technology to shorten practice times.

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Cited By

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  • (2020)How to Relax in Stressful Situations: A Smart Stress Reduction SystemHealthcare10.3390/healthcare80201008:2(100)Online publication date: 16-Apr-2020
  • (2020)Digital Technology, Meditative and Contemplative Practices, and Transcendent ExperiencesProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376356(1-14)Online publication date: 21-Apr-2020

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  1. Identification of post-meditation perceptual states using wearable EEG and Self-Calibrating Protocols

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        cover image ACM Other conferences
        PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference
        June 2018
        591 pages
        ISBN:9781450363907
        DOI:10.1145/3197768
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        • NSF: National Science Foundation

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 26 June 2018

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        Author Tags

        1. EEG
        2. machine learning
        3. meditation
        4. relaxation response
        5. self-calibrating protocols

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        View all
        • (2020)How to Relax in Stressful Situations: A Smart Stress Reduction SystemHealthcare10.3390/healthcare80201008:2(100)Online publication date: 16-Apr-2020
        • (2020)Digital Technology, Meditative and Contemplative Practices, and Transcendent ExperiencesProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376356(1-14)Online publication date: 21-Apr-2020

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