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dc.contributor.authorDouglas, Caitriona
dc.contributor.authorTremblay, Antoine
dc.contributor.authorNewman, Aaron J.
dc.date.accessioned2021-09-22T19:04:33Z
dc.date.available2021-09-22T19:04:33Z
dc.date.issued2021-09
dc.identifier.urihttp://hdl.handle.net/10222/80821
dc.descriptionA pre-print of a methods paper along with a dataset and MNE-python Jupyter Notebook scripts that provide a walk-through tutorial of the data analysis method.en_US
dc.description.abstractEEG hyperscanning refers to recording electroencephalographic (EEG) data from multiple participants simultaneously. Many hyperscanning experimental designs seek to mimic naturalistic behavior, relying on unpredictable participant-generated stimuli. The majority of this research has focused on neural oscillatory activity that is quantified over hundreds of milliseconds or more. This contrasts with traditional event-related potential (ERP) research in which analysis focuses on transient responses, often only tens of milliseconds in duration. Deriving ERPs requires precise time-locking between stimuli and EEG recordings, and thus typically relies on pre-set stimuli that are presented to participants by a system that controls stimulus timing and synchronization with an EEG system. EEG hyperscanning methods typically use separate EEG amplifiers for each participant, increasing cost and complexity — including challenges in synchronizing data between systems. Here, we describe a method that allows for simultaneous acquisition of EEG data from a pair of participants engaged in conversation, using a single EEG system with simultaneous audio data collection that is synchronized with the EEG recording. This allows for the post-hoc insertion of trigger codes so that it is possible to analyze ERPs time-locked to specific events. We further demonstrate methods for deriving ERPs elicited by another person’s spontaneous speech, using this setup.en_US
dc.titleA two for one special: EEG hyperscanning using a single-person EEG recording setup (pre-print)en_US
dc.typeDataseten_US
dc.typePreprinten_US
dc.typeOtheren_US
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