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Psychology postdoctoral and graduate student research

Permanent URI for this collectionhttps://hdl.handle.net/10222/21990

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Now showing 1 - 7 of 7
  • ItemOpen Access
    A two for one special: EEG hyperscanning using a single-person EEG recording setup (pre-print)
    (2021-09) Douglas, Caitriona; Tremblay, Antoine; Newman, Aaron J.
    EEG 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.
  • ItemOpen Access
    eRpfData: Companion data for package eRpf
    (2015) Tremblay, Antoine
  • ItemOpen Access
    eRpfBrain: Brain masks for package eRpf
    (2015) Tremblay, Antoine
  • ItemOpen Access
    Modelling Non-linear Relationships in ERP Data Using Mixed-effects Regression with R Examples
    (2013-05-29) Tremblay, Antoine; Newman, Aaron J.
    The relationship between two variables is very often assumed to be a straight line. However, there are many problems for which this assumption does not hold. In this paper, we show that the assumption of linearity, an assumption commonly made in ERP research, comes at a cost and may significantly affect the quality of the inferences drawn from the data. We demonstrate why the assumption of linearity should be relaxed and how to model non-linear relationships between ERP amplitudes and predictor variables using restricted cubic splines and mixed-effects regression. This paper has been written in LaTeX using Sweave and R, and the source document is provided as supplementary material. The data, a pdf of this paper, the .Rnw file used to write it, and the R code used to generate all of the analyses, tables, and figures presented here are available in package EEGLMERdata. The package is available as supplementary material for this article, and also from DALSPACE. These resources can be used to work through the examples, and potentially act as a starting point for the reader's own forays into mixed-effects analysis.