Welcome to the upgraded MacSphere! We're putting the finishing touches on it; if you notice anything amiss, email macsphere@mcmaster.ca

Characterizing Population EEG Dynamics throughout Adulthood

dc.contributor.authorHashemi A
dc.contributor.authorPino LJ
dc.contributor.authorMoffat G
dc.contributor.authorMathewson KJ
dc.contributor.authorAimone C
dc.contributor.authorBennett PJ
dc.contributor.authorSchmidt LA
dc.contributor.authorSekuler AB
dc.contributor.departmentPsychology, Neuroscience & Behaviour
dc.date.accessioned2021-04-28T17:16:44Z
dc.date.available2021-04-28T17:16:44Z
dc.date.issued2016-11
dc.date.updated2021-04-28T17:16:42Z
dc.description.abstractFor decades, electroencephalography (EEG) has been a useful tool for investigating the neural mechanisms underlying human psychological processes. However, the amount of time needed to gather EEG data means that most laboratory studies use relatively small sample sizes. Using the Muse, a portable and wireless four-channel EEG headband, we obtained EEG recordings from 6029 subjects 18-88 years in age while they completed a category exemplar task followed by a meditation exercise. Here, we report age-related changes in EEG power at a fine chronological scale for δ, θ, α, and β bands, as well as peak α frequency and α asymmetry measures for both frontal and temporoparietal sites. We found that EEG power changed as a function of age, and that the age-related changes depended on sex and frequency band. We found an overall age-related shift in band power from lower to higher frequencies, especially for females. We also found a gradual, year-by-year slowing of the peak α frequency with increasing age. Finally, our analysis of α asymmetry revealed greater relative right frontal activity. Our results replicate several previous age- and sex-related findings and show how some previously observed changes during childhood extend throughout the lifespan. Unlike previous age-related EEG studies that were limited by sample size and restricted age ranges, our work highlights the advantage of using large, representative samples to address questions about developmental brain changes. We discuss our findings in terms of their relevance to attentional processes and brain-based models of emotional well-being and aging.
dc.identifier.doihttps://doi.org/10.1523/eneuro.0275-16.2016
dc.identifier.issn2373-2822
dc.identifier.issn2373-2822
dc.identifier.urihttp://hdl.handle.net/11375/26362
dc.publisherSociety for Neuroscience
dc.subjectBig Data
dc.subjectEEG
dc.subjectMuse
dc.subjectage
dc.subjectalpha frequency
dc.subjectmindfulness
dc.subjectAdolescent
dc.subjectAdult
dc.subjectAged
dc.subjectAged, 80 and over
dc.subjectAging
dc.subjectBrain
dc.subjectBrain Waves
dc.subjectElectroencephalography
dc.subjectFemale
dc.subjectHumans
dc.subjectJudgment
dc.subjectMale
dc.subjectMeditation
dc.subjectMiddle Aged
dc.subjectMindfulness
dc.subjectNeuropsychological Tests
dc.subjectSex Characteristics
dc.subjectSignal Processing, Computer-Assisted
dc.subjectYoung Adult
dc.titleCharacterizing Population EEG Dynamics throughout Adulthood
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Characterizing Population EEG Dynamics throughout Adulthood.pdf
Size:
525.12 KB
Format:
Adobe Portable Document Format
Description:
Published version