Personalised Affective Classification Through Enhanced EEG Signal Analysis

<p>Declining mental health is a prominent and concerning issue. Affective classification, which employs machine learning on brain signals captured from electroencephalogram (EEG), is a prevalent approach to address this issue. However, many existing studies have adopted a one-size-fits-all app...

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Bibliographic Details
Main Author:	Joseph Barrowclough (20574195) (author)
Other Authors:	Nonso Nnamoko (20574198) (author), Ioannis Korkontzelos (20574201) (author)
Published:	2025
Subjects:	Genetics Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified unique emotional patterns overlooks individual differences employs machine learning declining mental health brain signals captured decision tree models best performing models generalised method across respectively .</ p person .</ p conducting affective classification many existing studies commonly called deap benchmark eeg dataset personalised affective classification affective classification existing studies personalised models generalised counterpart affective research eeg samples eeg ), personalised mode deap dataset dataset found study explored research concludes multiple individuals model runtime mcnemar ’ efficiency gain effective way comparative analysis also used accurately capture 32 subjects
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Personalised Affective Classification Through Enhanced EEG Signal Analysis

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