Exploring the impact of manual and automatic EEG pre-processing methods on interpersonal neural synchrony measures in parent-infant hyperscanning studies.

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Tác giả: Lucia Billeci, Elena Capelli, Maddalena Cassa, Miriam Paola Pili, Giorgia Procissi, Livio Provenzi, Valentina Riva, Elisa Roberti, Eleonora Siri

Ngôn ngữ: eng

Ký hiệu phân loại: 594.38 *Pulmonata

Thông tin xuất bản: Netherlands : Journal of neuroscience methods , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 225007

 BACKGROUND: Electroencephalograph (EEG) hyperscanning allows studying Interpersonal Neural Synchrony (INS) between two or more individuals across different social conditions, including parent-infant interactions. Signal pre-processing is crucial to optimize computation of INS estimates
  however, few attempts have been made at comparing the impact of different dyadic EEG data pre-processing methods on INS estimates. NEW METHODS: EEG data collected on 31 mother-infant dyads (8-10 months) engaged in a Face-to-Face Still-Face Procedure were pre-processed with two versions of the same pipeline, the "automated" and the "manual". Cross-frequency PLV in the theta (3-5Hz, 4-7Hz) and alpha (6-9Hz, 8-12Hz) frequency bands were computed after automated and manual pre-processing and compared through Pearson's correlations and Repeated Measures ANOVAs. RESULTS: PLVs computed in the theta, but not alpha, frequency band were significantly higher after automated pre-processing than after manual pre-processing. Moreover, the automated pipeline rejected a significantly lower percentage of ICs and epochs compared to the manual pipeline. COMPARISON WITH EXISTING METHODS: While no direct comparison with existing dyadic EEG data pre-processing pipelines was made, this is the first study assessing the impact of different methodological decisions, particularly of the degree of pre-processing automatization, on cross-frequency PLV computed on a dataset of parent-infant dyads. CONCLUSIONS: Non-directional phase-based INS indexes such as the PLV seem to be affected by the degree of automatization of the pre-processing pipeline. Future research should strive for standardization of dyadic EEG pre-processing methods.
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