Uploaded May 2024 | Updated September 2026, 2 hours ago
In this webinar, Dr Maria Laura Filippetti presented a registered report that employed both standard approaches and recent machine learning techniques to infant fNIRS data. Specifically, the webinar discussed the use of baseline-corrected averaging, General Linear Model (GLM)-based univariate, and Multivariate Pattern Analysis (MVPA) approaches to show how the conclusions one would draw based on these different analysis approaches converge or differ. The webinar presented fNIRS data from 30 4-to-6-month-old infants who were presented with a standard face inversion paradigm where changes in brain activation in response to upright and inverted face stimuli were measured. By including more standard approaches together with recent machine learning techniques, the webinar aimed to inform the fNIRS community on ways to analyse infant fNIRS datasets.
In this webinar, Dr Maria Laura Filippetti presented a registered report that employed both standard approaches and recent machine learning techniques to infant fNIRS data. Specifically, the webinar discussed the use of baseline-corrected averaging, General Linear Model (GLM)-based univariate, and Multivariate Pattern Analysis (MVPA) approaches to show how the conclusions one would draw based on these different analysis approaches converge or differ. The webinar presented fNIRS data from 30 4-to-6-month-old infants who were presented with a standard face inversion paradigm where changes in brain activation in response to upright and inverted face stimuli were measured. By including more standard approaches together with recent machine learning techniques, the webinar aimed to inform the fNIRS community on ways to analyse infant fNIRS datasets.
