Monitoring Working Memory Load during Computer-Based Tasks with EEG Pattern Recognition Methods

摘要

We assessed working memory load during computer use with neural network pattern recognition applied to EEG spectral features. Eight participants per- formed high-, moderate-, and low-load working memory tasks. Frontal theta EEG activity increased and alpha activity decreased with increasing load. These Changes probably reflect task difficulty-related increases in mental effort and the Proportion of cortical resources allocated to task performance. In network Analyses, test data segments from high and low load levels were discriminated With better than 95/100 accuracy.