Using within-subject pattern classification to understand lifespan age differences in oscillatory mechanisms of working memory selection and maintenance.
In lifespan studies, large within-group heterogeneity with regard to behavioral and neuronal data is observed. This casts doubt on the validity of group-statistics-based approaches to understand age-related changes on cognitive and neural levels. Recent progress in brain-computer interface research demonstrates the potential of machine learning techniques to derive reliable person-specific models, representing brain behavior mappings. The present study now proposes a supervised learning approach to derive person-specific models for the identification and quantification of interindividual differences in oscillatory EEG responses related to working memory selection and maintenance mechanisms in a heterogeneous lifespan sample. EEG data were used to discriminate different levels of working memory load and the focus of visual attention. We demonstrate that our approach leads to person-specific models with better discrimination performance compared to classical person-nonspecific models. We show how these models can be interpreted both on an individual as well as on a group level. One of the key findings is that, with regard to the time dimension, the between-person variance of the obtained person-specific models is smaller in older than in younger adults. This is contrary to what we expected because of increased behavioral and neuronal heterogeneity in older adults.
Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany. Electronic address: karch@mpib-berlin.mpg.de.
Julian D
JD
Karch
Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.
Myriam C
MC
Sander
Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany; Department of Psychology, University of VA, Charlottesville, USA.
Timo
T
von Oertzen
Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.
Andreas M
AM
Brandmaier
Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany. Electronic address: werkle@mpib-berlin.mpg.de.
Markus
M
Werkle-Bergner
D000293:Adolescent; D000328:Adult; D000368:Aged; D000375:Aging / Q000502:physiology*; D001288:Attention / Q000502:physiology; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D002648:Child; D004569:Electroencephalography; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008570:Memory, Short-Term / Q000502:physiology*; D008959:Models, Neurological; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
EEG; Lifespan age differences; Prediction; Single-trial analysis