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Updated: Oct 12, 2025
Infant Auditory Processing and Event-related Brain Oscillations
Published on: July 1, 2015
Automated preprocessing and phase-amplitude coupling analysis of scalp EEG discriminates infantile spasms from
Makoto Miyakoshi1, Hiroki Nariai2, Rajsekar R Rajaraman2
1Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, United States.
Objective:
Delta-gamma phase-amplitude coupling in EEG is useful for localizing epileptic sources and to evaluate severity in children with infantile spasms. We (1) develop an automated EEG preprocessing pipeline to clean data using artifact subspace reconstruction (ASR) and independent component (IC) analysis (ICA) and (2) evaluate delta-gamma modulation index (MI) as a method to distinguish children with epileptic spasms (cases) from normal controls during sleep and awake.
Methods:
Using 400 scalp EEG datasets (200 sleep, 200 awake) from 100 subjects, we calculated MI after applying high-pass and line-noise filters (Clean 0), and after ASR followed by either conservative (Clean 1) or stringent (Clean 2) artifactual IC rejection. Classification of cases and controls using MI was evaluated with Receiver Operating Characteristics (ROC) to obtain area under curve (AUC).
Results:
The artifact rejection algorithm reduced raw signal variance by 29-45% and 38-60% for Clean 1 and Clean 2, respectively. MI derived from sleep data, with or without preprocessing, robustly classified the groups (all AUC > 0.98). In contrast, group classification using MI derived from awake data was successful only after Clean 2 (AUC = 0.85).
Conclusions:
We have developed an automated EEG preprocessing pipeline to perform artifact rejection and quantify delta-gamma modulation index.
