Prediction of Human Empathy based on EEG Cortical Asymmetry

Abstract

Humans constantly interact with digital devices that disregard their feelings. However, the synergy between human and technology can be strengthened if the technology is able to distinguish and react to human emotions. Models that rely on unconscious indications of human emotions, such as (neuro)physiological signals, hold promise in personalization of feedback and adaptation of the interaction. The current study elaborated on adopting a predictive approach in studying human emotional processing based on brain activity. More specifically, we investigated the proposition of predicting selfreported human empathy based on EEG cortical asymmetry in different areas of the brain. Different types of predictive models i.e. multiple linear regression analyses as well as binary and multiclass classifications were evaluated. Results showed that lateralization of brain oscillations at specific frequency bands is an important predictor of self-reported empathy scores. Additionally, prominent classification performance was found during resting-state which suggests that emotional stimulation is not required for accurate prediction of empathy -as a personality trait-based on EEG data. Our findings not only contribute to the general understanding of the mechanisms of empathy, but also facilitate a better grasp on the advantages of applying a predictive approach compared to hypothesis-driven studies in neuropsychological research. More importantly, our results could be employed in the development of brain-computer interfaces that assist people with difficulties in expressing or recognizing emotions.

Classifying human emotions in HRI: applying global optimization model to EEG brain signals

“…To classify the emotional responses of users from EEG signals, we proposed a global optimization model based on a combination of feature selection and hyperparameter optimization techniques applied to various classification methods, including not only classical machine learning models such as Support Vector Machines and Decision Trees but also deep learning models such as the Multi-Layer Perceptron. The results validated neuroscience theories about the involvement of the FBA including the alpha and beta brain waves in the detection of emotional responses (Al-Nafjan et al, 2017 ; Alimardani et al, 2020 ; Kuijt and Alimardani, 2020 ; Wang and Wang, 2021 ). This was further supported by Figure 4 , where FBA-based neuromarkers of arousal and valence demonstrated a clear separation of the user's negative and positive mental states.…”

Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals

“…On the other hand, emotional valence can be evaluated by measuring changes in EEG brain activity using the well-known neurometric of Frontal Alpha Asymmetry (FAA) [18,[31][32][33]. FAA refers to the lateralization (change between the right and left hemispheres) of the alpha-band brain activity in the frontal brain region.…”