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Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper.<\/jats:p>","DOI":"10.3233\/idt-200047","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T14:35:20Z","timestamp":1617114920000},"page":"87-97","source":"Crossref","is-referenced-by-count":3,"title":["Whale optimization algorithm fused with SVM to detect stress in EEG signals"],"prefix":"10.1177","volume":"15","author":[{"given":"Richa","family":"Gupta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. 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