A predictive method for emotional sentiment analysis by machine learning from electroencephalography of brainwave data

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dc.authorscopusid57213853975
dc.authorscopusid57214941043
dc.authorscopusid56522820200
dc.authorscopusid57816872000
dc.authorscopusid57532050400
dc.contributor.authorDutta P.
dc.contributor.authorPaul S.
dc.contributor.authorCengiz K.
dc.contributor.authorAnand R.
dc.contributor.authorMajumder M.
dc.date.accessioned2024-06-12T10:24:59Z
dc.date.available2024-06-12T10:24:59Z
dc.date.issued2022
dc.description.abstractThe human cerebrum is the focal handling unit for different assignments, for example, observation, comprehension, consideration, feeling, memory, and activity. Understanding of Brain-Computer Interface strategies and revamping human feelings is an exceptionally tremendous field in the area of exploration. Numerous studies were conceived to distinguish the human feelings as bliss; fear, outrage, and bitterness were discovered promising by utilization of electroencephalography (EEG) signs. Experimental datasets were collected from a Muse EEG headband with a global EEG position standard. This arrangement collects 2549 datasets based on time-frequency domain statistical features where a subset of 640 datasets chosen by their symmetrical uncertainty was discovered to be best when utilized with three different classifiers Random Forest (RF), XG Boost, and Decision Tree for emotion detection. All these three algorithms achieved an overall accuracy of more than 95%. XG Boost exhibits the maximum accuracy of 99.04% while RF takes the least training time and occupied the maximum Area under the curve. By considering the entire performance index it is seen that all the proposed algorithms outperform the state-of-art methods. © 2023 Elsevier Inc. All rights reserved.en_US
dc.identifier.doi10.1016/B978-0-323-91916-6.00008-4
dc.identifier.endpage130en_US
dc.identifier.isbn9780323919166
dc.identifier.isbn9780323919364
dc.identifier.scopus2-s2.0-85150079867en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage109en_US
dc.identifier.urihttps://doi.org/10.1016/B978-0-323-91916-6.00008-4
dc.identifier.urihttps://hdl.handle.net/20.500.14551/16134
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofImplementation of Smart Healthcare Systems using AI, IoT, and Blockchainen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassification; Eeg Signal; Machine Learning; Performance Index; Symmetric Uncertaintyen_US
dc.titleA predictive method for emotional sentiment analysis by machine learning from electroencephalography of brainwave dataen_US
dc.typeBook Chapteren_US

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