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Classify emotions using EEG

[Introduction]

Abstract

Electroencephalography (EEG), a brain imaging technique capturing electrical activity through electrodes, is a valuable tool for studying cognitive processes. Its cost-effectiveness and portability provide practical advantages over other non-invasive measurement techniques, such as MEG, fNIRS and fMRI. This paper examines the application of universal EEG setups within the context of art installations, highlighting both their utility and limitations. Despite challenges like variations in brain anatomy affecting signal accuracy, EEG can still classify general brain states and capture responses to stimuli, offering possible directions for interactive art. Next to introducing the EEG technique, the paper further discusses EEG data processing. Finally, the document illustrates the use of this existing medium in the art installation ‘enclosed ()’. Describing the process starting from EEG data collection to emotion classification, utilising a developed support vector machine model to analyze brainwave frequencies. The paper explains the developed model in detail, achieving a performance of 54% accuracy. This research underscores the potential of EEG to not only revolutionize our understanding of the brain but also to serve as a bridge between technology and art.




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EEG_document
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Emergence NDA (1)
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