ArticleBrain sciences2020
Abnormal Emotional Processing and Emotional Experience in Patients with Peripheral Facial Nerve Paralysis: An MEG Study.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Review
- Temporal dynamics of affective scene processing in the healthy adult human brain.Neuroscience and biobehavioral reviews · 2025Review
- Impact of Bell's palsy in facial emotion recognition: a prospective observational study of 60 patients.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Observational
- Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).Sensors (Basel, Switzerland) · 2022Article
- The Right Hemisphere Is Responsible for the Greatest Differences in Human Brain Response to High-Arousing Emotional versus Neutral Stimuli: A MEG Study.Brain sciences · 2021Article
- The Temporal and Spatial Dynamics of Cortical Emotion Processing in Different Brain Frequencies as Assessed Using the Cluster-Based Permutation Test: An MEG Study.Brain sciences · 2020Article
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Authors and funding
10 authors.
Funding
Abstract
Abnormal emotional reactions of the brain in patients with facial nerve paralysis have not yet been reported. This study aims to investigate this issue by applying a machine-learning algorithm that discriminates brain emotional activities that belong either to patients with facial nerve paralysis or to healthy controls. Beyond this, we assess an emotion rating task to determine whether there are differences in their experience of emotions. MEG signals of 17 healthy controls and 16 patients with facial nerve paralysis were recorded in response to picture stimuli in three different emotional categories (pleasant, unpleasant, and neutral). The selected machine learning technique in this study was the logistic regression with LASSO regularization. We demonstrated significant classification performances in all three emotional categories. The best classification performance was achieved considering features based on event-related fields in response to the pleasant category, with an accuracy of 0.79 (95% CI (0.70, 0.82)). We also found that patients with facial nerve paralysis rated pleasant stimuli significantly more positively than healthy controls. Our results indicate that the inability to express facial expressions due to peripheral motor paralysis of the face might cause abnormal brain emotional processing and experience of particular emotions.
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