A Study of Longitudinal Trends in Time-Frequency Transformations of EEG Data During a Learning Experiment

• Activity Number: 26 - Imaging Speed Session
• Type: Contributed
• Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Joanna Boland* and donatello telesca and Catherine Sugar and Shafali Jeste and Cameron Goldbeck and Damla Senturk
• Companies: UCLA Department of Biostatistics and UCLA and UCLA Department of Biostatistics and Department of Psychiatry and Biobehavioral Sciences and UCLA Department of Biostatistics and UCLA
• Keywords: Event-related potentials; Longitudinal functional data analysis; Mixed effects models; Multidimensional PCA; Time-frequency transformations; Wavelets

Abstract

EEG experiments yield high-dimensional event-related potential (ERP) data in response to repeatedly presented stimuli throughout the experiment. Changes in the ERP signal over the duration of an experiment (longitudinally) are the main quantity of interest in learning paradigms, as they represent learning dynamics. Typical analysis, either in the time or the frequency domain, average the ERP waveform across all trials, leading to the loss of the potentially valuable longitudinal information. We propose longitudinal time-frequency transformation of ERP (LTFT-ERP) to retain information from both domains, while still retaining the longitudinal dynamics. LTFT-ERP begins by time-frequency transformations of the ERP data, collected across subjects, electrodes, conditions, and trials, followed by a data-driven multidimensional principal components analysis (PCA) approach for dimension reduction. The PCA scores capture longitudinal learning dynamics and are modeled within a mixed-effects model. Applications to a learning paradigm depict distinct learning patterns throughout the experiment among children diagnosed with Autism Spectrum Disorder and their typically developing peers.