Analysis of frequency domain features for the classification of evoked emotions using EEG signals.

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Tác giả: Samannaya Adhikari, Swastika Bhattacharya, Nitin Choudhury, Daisy Das, Nabamita Deb, Ebrahim Ghaderpour, Rajdeep Ghosh, Souvik Phadikar

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: Germany : Experimental brain research , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 117312

Emotion is a natural instinctive state of mind that greatly influences human physiological activities and daily life decisions. Electroencephalogram (EEG) signals created from the central nervous system are very useful for emotion recognition and classification. In this study, EEG signals of individuals are analyzed by the variational mode decomposition (VMD) for frequency domain features to recognize visual stimuli-based evoked emotions (happy, sad, fear). After cleaning EEG signals from artifacts, VMD is employed to decompose the signal into its respective intrinsic mode functions (IMFs). A sliding windowing approach is adopted to calculate the power distributions in each of the predefined frequency bands. The results reveal that extracting frequency domain features using a sliding window of 3 s significantly enhances the efficiency of analyzing induced emotions in subjects. The random forest model shows promising results in classifying various emotions, achieving an accuracy of 99.57% for validation and 99.36% for testing. Moreover, it is observed that the fifth IMF has a strong relationship with emotion elicited from visual stimuli. In addition, the features of the trained model are analyzed by Shapley additive explanations.
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