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

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.

[1]  Debashis Kushary,et al.  Bootstrap Methods and Their Application , 2000, Technometrics.

[2]  Inti A. Brazil,et al.  What Is the Molecular Signature of Mind–Body Interventions? A Systematic Review of Gene Expression Changes Induced by Meditation and Related Practices , 2017, Front. Immunol..

[3]  Bruce R. Dunn,et al.  Concentration and Mindfulness Meditations: Unique Forms of Consciousness? , 1999, Applied psychophysiology and biofeedback.

[4]  J. Beary,et al.  The Relaxation Response †. , 1974, Psychiatry.

[5]  Simmie L. Foster,et al.  Neuroimmunity: Physiology and Pathology. , 2016, Annual review of immunology.

[6]  R. K. Wallace,et al.  Physiological Effects of Transcendental Meditation , 1970, Science.

[7]  Hamid Reza Marateb,et al.  Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications , 2016, Basic and Clinical Neuroscience.

[8]  Thrasyvoulos Karydis,et al.  Self-Calibrating Protocols as diagnostic aids for personal medicine, neurological conditions and pain assessment , 2016, PETRA.

[9]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[10]  E. W. Maupin.  INDIVIDUAL DIFFERENCES IN RESPONSE TO A ZEN MEDITATION EXERCISE. , 1965, Journal of consulting psychology.

[11]  Jeffery A. Dusek,et al.  Correction: Relaxation Response Induces Temporal Transcriptome Changes in Energy Metabolism, Insulin Secretion and Inflammatory Pathways , 2017, PloS one.

[12]  Thrasyvoulos Karydis,et al.  Performance characterization of self-calibrating protocols for wearable EEG applications , 2015, PETRA.

[13]  J. González-Mora,et al.  Increased Grey Matter Associated with Long-Term Sahaja Yoga Meditation: A Voxel-Based Morphometry Study , 2016, PloS one.

[14]  Gregory T. Smith,et al.  Construct Validity of the Five Facet Mindfulness Questionnaire in Meditating and Nonmeditating Samples , 2008, Assessment.

[15]  G. Fricchione,et al.  Relaxation Response Induces Temporal Transcriptome Changes in Energy Metabolism, Insulin Secretion and Inflammatory Pathways , 2013, PloS one.

[16]  T. Ros,et al.  EEG Dynamics of Mindfulness Meditation Versus Alpha Neurofeedback: a Sham-Controlled Study , 2017 .

[17]  Cheng-Deng Kuo,et al.  Heart rate variability predicts short-term outcome for successfully resuscitated patients with out-of-hospital cardiac arrest. , 2009, Resuscitation.

[18]  Amjed S. Al-Fahoum,et al.  Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains , 2014, ISRN neuroscience.

[19]  Vernon A Barnes,et al.  Long-term effects of stress reduction on mortality in persons > or = 55 years of age with systemic hypertension. , 2005, The American journal of cardiology.