Mass-Univariate Hypothesis Testing on MEEG Data using Cross-Validation

TL;DR

This paper introduces a novel mass-univariate hypothesis testing method for MEEG data that employs cross-validation, hierarchical classification, and DCT features to improve reliability and sensitivity in detecting brain activity differences.

Contribution

The paper presents a new hierarchical classification approach with cross-validation and DCT features for mass-univariate MEEG analysis, addressing false discovery issues.

Findings

Enhanced detection of brain activity differences.

Improved reliability of hypothesis testing.

Effective handling of multiple comparisons.

Abstract

Recent advances in statistical theory, together with advances in the computational power of computers, provide alternative methods to do mass-univariate hypothesis testing in which a large number of univariate tests, can be properly used to compare MEEG data at a large number of time-frequency points and scalp locations. One of the major problematic aspects of this kind of mass-univariate analysis is due to high number of accomplished hypothesis tests. Hence procedures that remove or alleviate the increased probability of false discoveries are crucial for this type of analysis. Here, I propose a new method for mass-univariate analysis of MEEG data based on cross-validation scheme. In this method, I suggest a hierarchical classification procedure under k-fold cross-validation to detect which sensors at which time-bin and which frequency-bin contributes in discriminating between two different stimuli or tasks. To achieve this goal, a new feature extraction method based on the discrete cosine transform (DCT) employed to get maximum advantage of all three data dimensions. Employing cross-validation and hierarchy architecture alongside the DCT feature space makes this method more reliable and at the same time enough sensitive to detect the narrow effects in brain activities.