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138077 |
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|a Lopez-Martinez, Daniel
|e author
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|a Massachusetts Institute of Technology. Media Laboratory
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|a Harvard-MIT Program in Health Sciences and Technology
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|a Peng, Ke
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|a Steele, Sarah C.
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|a Lee, Arielle J.
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|a Borsook, David
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|a Picard, Rosalind
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|a Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals
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|b Institute of Electrical and Electronics Engineers (IEEE),
|c 2021-11-09T21:23:05Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/138077
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|a © 2018 IEEE. Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure alterations in brain function in evoked and ongoing pain. In this study, we applied multi-task machine learning methods to derive a practical algorithm for pain detection derived from fNIRS signals in healthy volunteers exposed to a painful stimulus. Especially, we employed multi-task multiple kernel learning to account for the inter-subject variability in pain response. Our results support the use of fNIRS and machine learning techniques in developing objective pain detection, and also highlight the importance of adopting personalized analysis in the process.
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|a Article
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|t 10.1109/icpr.2018.8545823
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