Open AccessLJaya optimisation‐based channel selection approach for performance improvement of cognitive workload assessment technique
Author(s) -
Mohdiwale S.,
Sahu M.,
Sinha G.R.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2020.1011
Subject(s) - workload , selection (genetic algorithm) , computer science , channel (broadcasting) , automation , cognition , binary number , artificial intelligence , electronic engineering , engineering , telecommunications , mathematics , mechanical engineering , arithmetic , neuroscience , biology , operating system
In this Letter, the Logical Jaya optimisation is proposed as an extension of the Jaya optimisation algorithm to improve the cognitive workload (CW) assessment technique where channel selection for the EEG signal act as a binary optimisation problem. Channel selection is very crucial, time‐consuming and requires expertise, specially when brain cognitive load is considered. The proposed approach is designed such that it not only improves the performance of the assessment model of CW but also reduces the computational cost. The approach also helps in the automation of brain analysis. The results obtained show that performance is improved by 22% than existing approaches to an average of >90% accuracy in different scenarios. The channels obtained using the approach also provided accurate active brain regions during CW analogous to previous studies.