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.