View article

Cognitive workload assessment is important for optimizing multitasking abilities in the current information-rich world. A few good research studies assess cross task classification where a model trained for one task can efficiently handle other tasks. Building a reliable model for the estimation of cross task cognitive workload using electroencephalogram (EEG) signal is challenging as brain dynamics differ for various activities. The present study explores the wide visual aspects for inducing two levels of cognitive workload in the two self-design tasks. The present study addresses temporal dynamics of EEG signal using time windowing, time segment smoothing, and formation of variable duration time frames. The present study computes features from the statistical, morphological and non-linear domain for variable duration segments considering four clinical sub-bands. The neighbourhood component feature selection …