Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals

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EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations (2019, background): “, offering more discriminant information) to improve the recognition accuracy for emotion-based HCI system [45]–[48].”
A Review of Emotion Recognition Using Physiological Signals (2018, methodology): “[96] videos 32 Yes terrible, love, hate, sentimental, lovely, happy, fun, shock, cheerful, depressing, exciting, melancholy, mellow EEG+8 peripheral signals different Powers, STD and SE of detail and approximation coefficients.”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “Using ECG, GSR, ST, BVP, RESP, EMG, and EOG signals selected from DEAP [96], Verma and Tiwary [347] extracted multi-resolution features based on DWT and used them for emotion estimation on the valence-arousal-dominance emotion model.”
Human Emotion Recognition: Review of Sensors and Methods (2020, background): “A novel approach for the multimodal fusion of information from a large number of channels to classify and predict emotions is presented in [191].”