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      Smooth tracking of visual targets distinguishes lucid REM sleep dreaming and waking perception from imagination

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      Nature Communications
      Nature Publishing Group UK

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          Abstract

          Humans are typically unable to engage in sustained smooth pursuit for imagined objects. However, it is unknown to what extent smooth tracking occurs for visual imagery during REM sleep dreaming. Here we examine smooth pursuit eye movements during tracking of a slow-moving visual target during lucid dreams in REM sleep. Highly similar smooth pursuit tracking was observed during both waking perception and lucid REM sleep dreaming, in contrast to the characteristically saccadic tracking observed during visuomotor imagination. Our findings suggest that, in this respect, the visual imagery that occurs during REM sleep is more similar to perception than imagination. The data also show that the neural circuitry of smooth pursuit can be driven by a visual percept in the absence of retinal stimulation and that specific voluntary shifts in the direction of experienced gaze within REM sleep dreams are accompanied by corresponding rotations of the physical eyes.

          Abstract

          When tracking a moving object, our eyes make smooth pursuit movements; however, tracking an imaginary object produces jerky saccadic eye movements. Here, the authors show that during lucid dreams, the eyes smoothly follow dreamed objects. In this respect, dream imagery is more similar to perception than imagination.

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          The relation of eye movements during sleep to dream activity: an objective method for the study of dreaming.

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            Eye movement analysis for activity recognition using electrooculography.

            In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.
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              Mental Imagery of Faces and Places Activates Corresponding Stimulus-Specific Brain Regions

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                Author and article information

                Contributors
                bbaird@wisc.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                17 August 2018
                17 August 2018
                2018
                : 9
                : 3298
                Affiliations
                [1 ]ISNI 0000000419368956, GRID grid.168010.e, Department of Psychology, , Stanford University, ; Stanford, CA 94305-2130 USA
                [2 ]ISNI 0000 0001 2167 3675, GRID grid.14003.36, Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, , University of Wisconsin–Madison, ; Madison, WI 53719 USA
                [3 ]Present Address: Lucidity Institute, https://lucidity.com
                Article
                5547
                10.1038/s41467-018-05547-0
                6098118
                30120229
                10552f06-f13f-4b97-b8f3-a4b7e046d35f
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 January 2017
                : 11 July 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: F32NS089348
                Award Recipient :
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