Energy Landscapes of Emotion: Quantifying Brain Network Stability During Happy and Sad Face Processing Using EEG-Based Hopfield Energy
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arXiv
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| Format: | Preprint |
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2026
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| author | Djibrina, Barry Li, Jiajia |
| author_facet | Djibrina, Barry Li, Jiajia |
| contents | Understanding how the human brain instantiates distinct emotional states is a key challenge in affective neuroscience. While network-based approaches have advanced emotion processing research,they remain largely descriptive,leaving the dynamical stability of emotional brain states unquantified.This study introduces a novel framework to quantify this stability by applying Hopfield network energy to empirically derived functional connectivity. High density EEG was recorded from 20 healthy adults during a happy versus sad facial expression discrimination task. Functional connectivity was estimated using the weighted Phase Lag Index to obtain artifact-robust,frequency-specific matrices, which served as coupling weights in a continuous Hopfield energy model to calculate a scalar energy value per trial. Statistical comparisons showed sad emotional processing was associated with significantly lower(more negative) energy in delta,theta,and alpha bands,with the strongest effect in the alpha band (Cohen's d =0.83). Energy correlated strongly negatively with global efficiency(r=-0.72),indicating hyperconnected,efficient networks correspond to more stable states.Additionally, alpha-band energy correlated positively with reaction time during sad trials(r=0.61),linking deeper network stability to increased cognitive effort. These findings demonstrate emotional valence corresponds to distinct attractor basins in the brain's functional landscape, with sadness occupying a deeper,more stable configuration than happiness.The Hopfield energy metric provides a principled, quantifiable measure of emotional brain state stability, opening new avenues for understanding affective dynamics in health and disease. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27644 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Energy Landscapes of Emotion: Quantifying Brain Network Stability During Happy and Sad Face Processing Using EEG-Based Hopfield Energy Djibrina, Barry Li, Jiajia Neurons and Cognition 92-08 Understanding how the human brain instantiates distinct emotional states is a key challenge in affective neuroscience. While network-based approaches have advanced emotion processing research,they remain largely descriptive,leaving the dynamical stability of emotional brain states unquantified.This study introduces a novel framework to quantify this stability by applying Hopfield network energy to empirically derived functional connectivity. High density EEG was recorded from 20 healthy adults during a happy versus sad facial expression discrimination task. Functional connectivity was estimated using the weighted Phase Lag Index to obtain artifact-robust,frequency-specific matrices, which served as coupling weights in a continuous Hopfield energy model to calculate a scalar energy value per trial. Statistical comparisons showed sad emotional processing was associated with significantly lower(more negative) energy in delta,theta,and alpha bands,with the strongest effect in the alpha band (Cohen's d =0.83). Energy correlated strongly negatively with global efficiency(r=-0.72),indicating hyperconnected,efficient networks correspond to more stable states.Additionally, alpha-band energy correlated positively with reaction time during sad trials(r=0.61),linking deeper network stability to increased cognitive effort. These findings demonstrate emotional valence corresponds to distinct attractor basins in the brain's functional landscape, with sadness occupying a deeper,more stable configuration than happiness.The Hopfield energy metric provides a principled, quantifiable measure of emotional brain state stability, opening new avenues for understanding affective dynamics in health and disease. |
| title | Energy Landscapes of Emotion: Quantifying Brain Network Stability During Happy and Sad Face Processing Using EEG-Based Hopfield Energy |
| topic | Neurons and Cognition 92-08 |
| url | https://arxiv.org/abs/2603.27644 |