How Light Shapes Memory: Beta Synchrony in the Temporal-Parietal Cortex Predicts Cognitive Ergonomics for BCI Applications

TL;DR

This study introduces a novel EEG-based framework leveraging beta synchrony in temporal-parietal regions to improve real-time cognitive state recognition for BCI applications, considering environmental illumination effects.

Contribution

It proposes a new multi-regional dynamic interplay model based on beta phase synchrony, enhancing memory state decoding and environmental adaptation in BCIs.

Findings

Temporal-parietal phase clustering outperforms other regions in memory state classification.

Illumination conditions enhance temporoparietal synchrony.

Beta-phase connectivity is a key feature for cross-task memory state recognition.

Abstract

Working memory is a promising paradigm for assessing cognitive ergonomics of brain states in brain-computer interfaces(BCIs). This study decodes these states with a focus on environmental illumination effects via two distinct working memory tasks(Recall and Sequence) for mixed-recognition analysis. Leveraging nonlinear patterns in brain connectivity, we propose an innovative framework: multi-regional dynamic interplay patterns based on beta phase synchrony dynamics, to identify low-dimensional EEG regions (prefrontal, temporal, parietal) for state recognition. Based on nonlinear phase map analysis of the above three brain regions using beta-phase connectivity, we found that: (1)Temporal-parietal phase clustering outperforms other regional combinations in distinguishing memory states; (2)Illumination-enhanced environments optimize temporoparietal balance;(3) Machine learning confirms temporal-parietal synchrony as the dominant cross-task classification feature. These results provide a precise prediction algorithm, facilitating a low-dimensional system using temporal and parietal EEG channels with practical value for real-time cognitive ergonomics assessment in BCIs and optimized human-machine interaction.