ArticleFrontiers in neuroscience2020
Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Automated classification of conversation valence and arousal using autonomic nervous system responses.Frontiers in neuroscience · 2026Article
- Scanpath EEG dynamic, a new perspective for neuroaesthetic connoisseurship in paintings.Frontiers in psychology · 2025Review
- Estimating the valence and arousal of dyadic conversations using autonomic nervous system responses and regression algorithms.Frontiers in neuroergonomics · 2025Article
- Regression of Multiple Conversation Aspects using Dyadic Physiological Measurements.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality.Frontiers in neuroscience · 2024Article
- Decoding the neural signatures of valence and arousal from portable EEG headset.Frontiers in human neuroscience · 2022Article
- Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.Brain computer interfaces (Abingdon, England) · 2022Article
- Construction of a fiber-optically connected MEG hyperscanning system for recording brain activity during real-time communication.PloS one · 2022Article
- EEG hyperscanning in motor rehabilitation: a position paper.Journal of neuroengineering and rehabilitation · 2021Article
- Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses.IEEE transactions on affective computingArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Interactions between two brains constitute the essence of social communication. Daily movements are commonly executed during social interactions and are determined by different mental states that may express different positive or negative behavioral intent. In this context, the effective recognition of festive or violent intent before the action execution remains crucial for survival. Here, we hypothesize that the EEG signals contain the distinctive features characterizing movement intent already expressed before movement execution and that such distinctive information can be identified by state-of-the-art classification algorithms based on Riemannian geometry. We demonstrated for the first time that a classifier based on covariance matrices and Riemannian geometry can effectively discriminate between neutral, festive, and violent mental states only on the basis of non-invasive EEG signals in both the actor and observer participants. These results pave the way for new electrophysiological discrimination of mental states based on non-invasive EEG recordings and cutting-edge machine learning techniques.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.