RECENT PUBLICATIONS

Gevins, A., Smith, M.E., Leong, H., McEvoy, L., Whitfield, S., Du, R., & Rush, G. (1998). Monitoring working memory load during computer-based tasks with EEG pattern recognition. Human Factors, 40, 79-91.

ABSTRACT

Working memory load during computer use was assessed with neural network pattern recognition applied to EEG spectral features. Eight subjects performed high, moderate, and low load working memory tasks. Frontal theta EEG activity increased, and alpha decreased, with increasing load. These changes likely reflect task difficulty-related increases in mental effort and the proportion of cortical resources being allocated to task performance. In network analyses, test data segments from high and low load levels were discriminated with over 95% (p < .001) accuracy. Over 80% (p < .05) of test data segments associated with a moderate load could be discriminated from high or low load data segments. Statistically significant classification was also achieved when applying networks trained with data from one day to data from another day, when applying networks trained with data from one task to data from another task, and when applying networks trained with data from a group of subjects to data from new subjects. These results support the feasibility of using EEG-based methods for monitoring cognitive load during human-computer interaction.