Quantifying Data Requirements for EEG Independent Component Analysis Using AMICA

TL;DR

This study investigates how the amount of EEG data influences the quality of Independent Component Analysis (ICA) decompositions using AMICA, revealing that more data generally improves results without clear saturation.

Contribution

It provides a systematic analysis of data quantity effects on ICA quality, highlighting that benefits of additional data may persist beyond typical thresholds.

Findings

Increasing data improves mutual information reduction (MIR)

More data leads to higher near dipolarity of components

No clear plateau observed in quality metrics with more data

Abstract

Independent Component Analysis (ICA) is an important step in EEG processing for a wide-ranging set of applications. However, ICA requires well-designed studies and data collection practices to yield optimal results. Past studies have focused on quantitative evaluation of the differences in quality produced by different ICA algorithms as well as different configurations of parameters for AMICA, a multimodal ICA algorithm that is considered the benchmark against which other algorithms are measured. Here, the effect of the data quantity versus the number of channels on decomposition quality is explored. AMICA decompositions were run on a 71 channel dataset with 13 subjects while randomly subsampling data to correspond to specific ratios of the number of frames in a dataset to the channel count. Decomposition quality was evaluated for the varying quantities of data using measures of mutual information reduction (MIR) and the near dipolarity of components. We also note that an asymptotic trend can be seen in the increase of MIR and a general increasing trend in near dipolarity with increasing data, but no definitive plateau in these metrics was observed, suggesting that the benefits of collecting additional EEG data may extend beyond common heuristic thresholds and continue to enhance decomposition quality.