Decision Tree Method to Classify the Electroencephalography-based Emotion Data
نویسندگان
چکیده
Electroencephalography (EEG) data contains recordings of brain signal activity divided into several channels with different impulse responses that can be used to detect human emotions. In classifying emotions, EEG needs parsed or processed values that help recognize Research related electroencephalography has been carried out previously and experienced success using the Fuzzy C-Means, Multiple Discriminant Analysis, Deep Neural Network methods. This study was conducted classify emotions from on 10 participants. Each participant 40 trials testing Power Spectral Density (PSD) Discrete Wavelet Transform (DWT) methods at initial stage classification Decision Tree method as final improve accuracy two classification. The results this were finding 2 participants (3 trials) who unmatched a total (400 trials), which analyzed decision tree method. correct error increase result 100%. DWT is reference in considering an output arousal valance . contrast, PSD only combined output.
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ژورنال
عنوان ژورنال: Jurnal Infotel
سال: 2022
ISSN: ['2460-0997', '2085-3688']
DOI: https://doi.org/10.20895/infotel.v14i1.750