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HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis
- Source :
- Social cognitive and affective neuroscience, 16(1-2), 72-83. Oxford University Press, Social Cognitive and Affective Neuroscience, Social Cognitive and Affective Neuroscience, Oxford University Press (OUP), 2021, 16 (1-2), pp.72-83. ⟨10.1093/scan/nsaa141⟩, Social Cognitive and Affective Neuroscience, 2021, 16 (1-2), pp.72-83. ⟨10.1093/scan/nsaa141⟩, Ayrolles, A, Brun, F, Chen, P, Djalovski, A, Beauxis, Y, Delorme, R, Bourgeron, T, Dikker, S & Dumas, G 2021, ' HyPyP : A Hyperscanning Python Pipeline for inter-brain connectivity analysis ', Social cognitive and affective neuroscience, vol. 16, no. 1-2, pp. 72-83 . https://doi.org/10.1093/scan/nsaa141
- Publication Year :
- 2020
- Publisher :
- Center for Open Science, 2020.
-
Abstract
- The bulk of social neuroscience takes a ‘stimulus-brain’ approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a ‘brain-to-brain’ approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, ‘hyperscanning’ setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such ‘inter-brain connectivity analysis’, resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses.
- Subjects :
- Brain activity and meditation
inter-brain connectivity
AcademicSubjects/SCI01880
Cognitive Neuroscience
Social Interaction
Original Manuscript
Experimental and Cognitive Psychology
Social stimuli
050105 experimental psychology
MESH: Brain
bepress|Life Sciences|Neuroscience and Neurobiology
03 medical and health sciences
SDG 17 - Partnerships for the Goals
0302 clinical medicine
Social neuroscience
non-parametric statistics
MESH: Electroencephalography
Humans
Interpersonal Relations
0501 psychology and cognitive sciences
bepress|Life Sciences|Neuroscience and Neurobiology|Cognitive Neuroscience
hyperscanning
MESH: Social Interaction
MESH: Brain Mapping
computer.programming_language
Brain Mapping
MESH: Humans
Field (Bourdieu)
[SCCO.NEUR]Cognitive science/Neuroscience
05 social sciences
Brain
analysis pipeline
Electroencephalography
General Medicine
Python (programming language)
MESH: Interpersonal Relations
Data science
Popularity
Pipeline (software)
Social relation
python
PsyArXiv|Neuroscience|Cognitive Neuroscience
PsyArXiv|Neuroscience
Psychology
computer
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 17495016 and 17495024
- Database :
- OpenAIRE
- Journal :
- Social cognitive and affective neuroscience, 16(1-2), 72-83. Oxford University Press, Social Cognitive and Affective Neuroscience, Social Cognitive and Affective Neuroscience, Oxford University Press (OUP), 2021, 16 (1-2), pp.72-83. ⟨10.1093/scan/nsaa141⟩, Social Cognitive and Affective Neuroscience, 2021, 16 (1-2), pp.72-83. ⟨10.1093/scan/nsaa141⟩, Ayrolles, A, Brun, F, Chen, P, Djalovski, A, Beauxis, Y, Delorme, R, Bourgeron, T, Dikker, S & Dumas, G 2021, ' HyPyP : A Hyperscanning Python Pipeline for inter-brain connectivity analysis ', Social cognitive and affective neuroscience, vol. 16, no. 1-2, pp. 72-83 . https://doi.org/10.1093/scan/nsaa141
- Accession number :
- edsair.doi.dedup.....cd71e296a5d2c48983c67e1123eb144d
- Full Text :
- https://doi.org/10.31234/osf.io/x5apu