title: Automated classification of pain perception using high-density electroencephalography data.
abstract: The translation of brief, millisecond-long pain-eliciting stimuli to the subjective perception of pain is associated with changes in theta, alpha, beta, and gamma oscillations over sensorimotor cortex. However, when a pain-eliciting stimulus continues for minutes, regions beyond the sensorimotor cortex, such as the prefrontal cortex, are also engaged. Abnormalities in prefrontal cortex have been associated with chronic pain states, but conventional, millisecond-long EEG paradigms do not engage prefrontal regions. In the current study, we collected high-density EEG data during an experimental paradigm in which subjects experienced a 4-s, low- or high-intensity pain-eliciting stimulus. EEG data were analyzed using independent component analyses, EEG source localization analyses, and measure projection analyses. We report three novel findings. First, an increase in pain perception was associated with an increase in gamma and theta power in a cortical region that included medial prefrontal cortex. Second, a decrease in lower beta power was associated with an increase in pain perception in a cortical region that included the contralateral sensorimotor cortex. Third, we used machine learning for automated classification of EEG data into low- and high-pain classes. Theta and gamma power in the medial prefrontal region and lower beta power in the contralateral sensorimotor region served as features for classification. We found a leave-one-out cross-validation accuracy of 89.58%. The development of biological markers for pain states continues to gain traction in the literature, and our findings provide new information that advances this body of work.NEW & NOTEWORTHYThe development of a biological marker for pain continues to gain traction in literature. Our findings show that high- and low-pain perception in human subjects can be classified with 89% accuracy using high-density EEG data from prefrontal cortex and contralateral sensorimotor cortex. Our approach represents a novel neurophysiological paradigm that advances the literature on biological markers for pain.
authors.affiliation: Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida.
authors.forename: Gaurav
authors.initials: G
authors.lastname: Misra
authors.affiliation: Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida.
authors.forename: Wei-En
authors.initials: WE
authors.lastname: Wang
authors.affiliation: Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida.
authors.forename: Derek B
authors.initials: DB
authors.lastname: Archer
authors.affiliation: Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida.
authors.forename: Arnab
authors.initials: A
authors.lastname: Roy
authors.affiliation: Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida scoombes@ufl.edu.
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Coombes
mesh_terms: D000293:Adolescent; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000503:physiopathology*; D004569:Electroencephalography; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D008297:Male; D010146:Pain / Q000503:physiopathology*; D058748:Pain Perception / Q000502:physiology*; D010812:Physical Stimulation / Q000009:adverse effects; D011569:Psychiatric Status Rating Scales; D013696:Temperature; D064232:Visual Analog Scale; D055815:Young Adult
keywords: EEG; classification; gamma; medial prefrontal cortex; pain