A framework for analyzing EEG data using high-dimensional tests

TL;DR

This paper introduces a statistical framework for analyzing EEG data that handles high dimensionality and temporal dependencies, improving the understanding of brain function.

Contribution

The study introduces RIHT and MPDe, novel statistical methods for EEG data analysis that relax distributional assumptions and handle temporal dependencies.

Findings

RIHT demonstrates high power in detecting changes in EEG mean vectors under unknown distributions.

MPDe effectively estimates and tests precision matrices in time-dependent EEG data.

Key EEG channels like PO3, PO4, and FT7 are identified as significant for human cognitive ability.

Abstract

The objective of EEG data analysis is to extract meaningful insights, enhancing our understanding of brain function. However, the high dimensionality and temporal dependency of EEG data present significant challenges to the effective application of statistical methods. This study systematically addresses these challenges by introducing a high-dimensional statistical framework that includes testing changes in the mean vector and precision matrix, as well as conducting relevant analyses. Specifically, the Ridgelized Hotelling’s T2 test (RIHT) is introduced to test changes in the mean vector of EEG data over time while relaxing traditional distributional and moment assumptions. Secondly, a multiple population de-biased estimation and testing method (MPDe) is developed to estimate and simultaneously test differences in the precision matrix before and after stimulation. This approach extends the joint Gaussian graphical model to multiple populations while incorporating the temporal dependency of EEG data. Meanwhile, a novel data-driven fine-tuning method is applied to automatically search for optimal hyperparameters.

Through comprehensive simulation studies and applications, we have obtained substantial evidence to validate that the RIHT has relatively high power, and it can test for changes when the distribution is unknown. Similarly, the MPDe can infer the precision matrix under time-dependent conditions. Additionally, the conducted analysis of channel selection and dominant channel can identify significant channels which play a crucial role in human cognitive ability, such as PO3, PO4, Pz, P4, P8, FT7, and FT8. All findings confirm that the proposed methods outperform existing ones, demonstrating the effectiveness of the framework in EEG data analysis.

Source code and data used in the article are available at https://github.com/yahu911/Framework_EEG.

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