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|a Sano, Akane
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|a Massachusetts Institute of Technology. Media Laboratory
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|a Program in Media Arts and Sciences
|q (Massachusetts Institute of Technology)
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|a Sano, Akane
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|a Picard, Rosalind W.
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|a Picard, Rosalind W.
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|a Comparison of sleep-wake classification using electroencephalogram and wrist-worn multi-modal sensor data
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|b Institute of Electrical and Electronics Engineers (IEEE),
|c 2017-07-12T15:27:50Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/110667
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|a This paper presents the comparison of sleep-wake classification using electroencephalogram (EEG) and multi-modal data from a wrist wearable sensor. We collected physiological data while participants were in bed: EEG, skin conductance (SC), skin temperature (ST), and acceleration (ACC) data, from 15 college students, computed the features and compared the intra-/inter-subject classification results. As results, EEG features showed 83% while features from a wrist wearable sensor showed 74% and the combination of ACC and ST played more important roles in sleep/wake classification.
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|a en_US
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|a Article
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|t 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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