Time-frequency analysis of scalp EEG based on Hilbert-Huang transform

Abstract

Electroencephalography (EEG) is the electrical activity of the human brain and thus plays an important role to understand the cognitive function in neuroscience and clinical settings. However, the conventional EEG analyses based on the linear assumption usually are limited to analyzing the nonlinear waveform of brain signals. To address this issue, we present a data-driven method for analyzing scalp EEG signals in the time-frequency domain. Results from both simulation and resting EEG demonstrated that temporal characteristics and non-linear features can be revealed with Hilbert-Huang transform without any prior assumptions. In addition, the Hilbert-Huang transform is less affected by the non-sinusoidal signals.