Unsupervised approach for measurement of cognitive load using EEG signals

Abstract

Individuals exhibit different levels of cognitive load for a given mental task. Measurement of cognitive load can enable real-time personalized content generation for distant learning, usability testing of applications on mobile devices and other areas related to human interactions. Electroencephalogram (EEG) signals can be used to analyze the brain-signals and measure the cognitive load. We have used a low cost and commercially available neuro-headset as the EEG device. A universal model, generated by supervised learning algorithms, for different levels of cognitive load cannot work for all individuals due to the issue of normalization. In this paper, we propose an unsupervised approach for measuring the level of cognitive load on an individual for a given stimulus. Results indicate that the unsupervised approach is comparable and sometimes better than supervised (e.g. support vector machine) method. Further, in the unsupervised domain, the Component based Fuzzy c-Means (CFCM) outperforms the traditional Fuzzy c-Means (FCM) in terms of the measurement accuracy of the cognitive load.