EEG MVPA

Working memory (WM) is an essential cognitive function. Manipulating WM load and observing corresponding brain responses is a commonly applied technique for uncovering the neural mechanisms of WM. In this study, we applied multivariate pattern analysis (MVPA) to two high-density EEG datasets from healthy human volunteers performing verbal WM tasks with three different levels of memory load to examine the formation and development of WM representations in the brain. By testing at which moment WM-specific neural representations become decodable from scalp EEG, we characterized the temporal dynamics of WM representations. Moreover, we observed evidence for dynamic coding of the memory-specific information as evidenced by the current source density (CSD)-based decoding. Critically, we observed evidence suggesting that WM could be maintained in the format of an activity-silent neural state via the activity-silent synaptic mechanisms. Using CSD connectivity-based decoding, we could decode the neural representation about the contents in WM from the so-called activity-silent period. It is quite remarkable that the patterned hidden state in WM networks can be detected at the scalp level using whole-head EEG CSD functional connectivity. This provides important proof-of-principle evidence for the feasibility of exploring hidden neural states with scalp EEG, with important implications for WM.