Split-test Bonferroni correction for QEEG statistical maps.

Vialatte, Francois-Benoit; Cichocki, Andrzej

With statistical testing, corrections for multiple comparisons, such as Bonferroni adjustments, have given rise to controversies in the scientific community, because of their negative impact on statistical power. This impact is especially problematic for high-multidimensional data, such as multi-electrode brain recordings. With brain imaging data, a reliable method is needed to assess statistical significance of the data without losing statistical power. Conjunction analysis allows the combination of significance and consistency of an effect. Through a balanced combination of information from retest experiments (multiple trials split testing), we present an intuitively appealing, novel approach for brain imaging conjunction. The method is then tested and validated on synthetic data followed by a real-world test on QEEG data from patients with Alzheimer’s disease. This latter application requires both reliable type-I error and type-II error rates, because of the poor signal-to-noise ratio inherent in EEG signals.

STATISTICAL correlation; ELECTROENCEPHALOGRAPHY; ERROR analysis in mathematics; ALZHEIMER'S patients; BRAIN research; GRAPHIC methods in statistics; PHYSIOLOGICAL control systems

Biological Cybernetics, 2008, Vol 98, Issue 4, p295

0340-1200

Academic Journal

10.1007/s00422-008-0210-8