Research on Working Memory States Based on Weighted K -Order Propagation Number Algorithm: An EEG Perspective

Abstract

Working memory (WM) is considered the mental workplace that retains and manipulates information. This study investigates the internal mechanism in WM states from an electroencephalography (EEG) network perspective. Firstly, we devised a novel letter-sequence version of the $n$ -back experiment to collect EEG data, analyzed the neural oscillations in the theta and gamma bands, and then constructed Phase Lock Value (PLV) grounded brain networks to examine the synchronizations among dissimilar brain regions. The complex topology properties (e.g., global efficiency, local efficiency, and small-worldness) were scrutinized as well. Additionally, we presented an original algorithm, the Weighted $K$ -Order Propagation Number (WKPN) algorithm, to extract the important brain regions associated with WM processes. The simulation revealed that the frontal and posterior regions were activated in two WM states, i.e., update and readout states. Throughout the readout, brain networks performed better in efficiency and resistance to interference. Furthermore, the right prefrontal and parietooccipital regions became more prominent in the completion of extra difficult WM tasks. In summary, these EEG-based results can be taken as promising evidence to understand and improve WM.