Cognitive Load During Multitasking Can Be Accurately Assessed Based on Single Channel Electroencephalography Using Graph Methods

Mental workload has been widely estimated based on electroencephalography (EEG) in the frequency domain. However, simple frequency features are not entirely accurate indicators of the cognitive load because surface EEG signals are weak, nonstationary and randomness. We hypothesize that graph methods, which analyse the relationship between each point and other points of the EEG signals, may provide a more precise identification of the mental load. To investigate this hypothesis, we aim to identify the optimum graph features from 14 channel EEG recordings (sampling rate = 128 Hz) in order to detect the high cognitive load related to multitasking. Three graph features: mean degree
$\overline {d}$
, clustering coefficient
$\overline {c}$
, and degree distribution
$p(k)$
, are extracted from 48 subjects EEG records. Each experimental subject conducts two tasks: without tasks and with a simultaneous capacity task, respectively. After the experiment is completed, the feeling of the subject with the cognitive load tags in three types: low load, medium load, and heavy load. The optimal features of these three levels of the subject sensation and two types of cognitive load in different tasks are selected on the basis of statistical analysis. Then all graph features are forwarded into a support vector machine (SVM) and a decision tree to conduct objective scoring classification and a three subjective rating classification, respectively. Based on the present results,channels O2, T8, FC6, F8, and AF4 are considered optimal for a more efficiently estimation of the cognitive load.
$\overline {c}$
associated with F8 and T8 during low cognitive load is significantly lower than those associated with high cognitive load (p < 0.001). Using three graph features, the accuracy of identifying two types of mental load is 89.6%. Current findings suggest that the mental workload associated with multi-tasks can be accurately assessed using the graph approaches to EEG data.