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Article Dans Une Revue Journal of Ambient Intelligence and Humanized Computing Année : 2023

EEG-based emotion recognition using modified covariance and ensemble classifiers

Résumé

The Electroencephalography (EEG)-based precise emotion identification is one of the most challenging tasks in pattern recognition. In this paper, an innovative EEG signal processing method is devised for an automated emotion identification. The Symlets-4 filters based “Multi Scale Principal Component Analysis” (MSPCA) is used to denoise and reduce the raw signal’s dimension. Onward, the “Modified Covariance” (MCOV) is used as a feature extractor. In the classification step, the ensemble classifiers are used. The proposed method achieved 99.6% classification accuracy by using the ensemble of Bagging and Random Forest (RF). It confirms effectiveness of the devised method in EEG-based emotion recognition.
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Dates et versions

hal-04352828 , version 1 (19-12-2023)

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Abdulhamit Subasi, Saeed Mian Qaisar. EEG-based emotion recognition using modified covariance and ensemble classifiers. Journal of Ambient Intelligence and Humanized Computing, 2023, ⟨10.1007/s12652-023-04715-5⟩. ⟨hal-04352828⟩
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