Robust Multimodal Cognitive Load Measurement (RMCLM)
Abstract
This report summarizes the important research activities, study results and research accomplishments out of the RMCLM project in the past year period. The objective of this project includes research of the fundamental issues related to the use of multiple input modalities and their fusion to enable robust and automatic cognitive load measurement (CLM) in the real world. Firstly, we carried out a further literature review on physiological measures of cognitive workload to include the recent advances of physiological measures of cognitive workload. In the meantime, we examined the use of various features (e.g. spectral and approximate entropies, wavelet-based complexity measures, correlation dimension, Hurst exponent) of electroencephalogram (EEG) signals to evaluate changes in working memory load during the performance of a cognitive task with varying difficulty/load levels. Eye based CLM was also studied. Three types of eye activity were investigated: pupillary response, blink, and eye movement (fixation and saccade). We further investigated the linguistic feature based CLM in this study and analyzed novel linguistic features as potential indices of cognitive load. All together, we had carried out CLM study of three unobtrusive modalities, namely EEG, eye activity, and linguistic feature based CLM, in the past year period.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 26, 2013
- Accession Number
- ADA582471
Entities
People
- Fang Chen