MONITORING WORKING-MEMORY LOAD DURING COMPUTER-BASED TASKS WITH EEG PATTERN-RECOGNITION METHODS

We assessed working memory load during computer use with neural networ k pattern recognition applied to EEG spectral features. Eight particip ants performed high-, moderate-, and low-load working memory tasks, Fr ontal theta EEG activity increased and alpha activity decreased with i ncreasing load. These changes probably reflect task difficulty-related increases in mental effort and the proportion of cortical resources a llocated to task performance. In network analyses, test data segments from high and low load levels were discriminated with better than 95% accuracy. More than 80% of test data segments associated with a modera te load could be discriminated from high- or low-load data segments. S tatistically significant classification was also achieved when applyin g networks trained with data from one day to data from another day, wh en applying networks trained with data from one task to data from anot her task, and when applying networks trained with data from a group of participants to data from new participants. These results support the feasibility of using EEG-based methods for monitoring cognitive load during human-computer interaction.