Quantification and Discriminant Evaluation of Unpleasant Emotions by Frequency Analysis Using EEG and ECG

The purpose of this study is to verify whether machine learning using electroencephalogram (EEG) and electrocardiogram (ECG) as inputs improves accuracy. participants, 16 healthy adults, were given two stimulations: resting unpleasant stimuli. Their EEG was measured 180 s immediately after the beta band LF, HF, LF/HF ECG calculated. accuracy neural network then compared an EEG-only, ECG-only, combined dataset. dataset 79.51%, which higher than that other datasets. results suggest emotional responses stimuli may differ in brain autonomic nervous system activity, combining both indices allow for more accurate discrimination.