Brain Topography Method based on Hilbert-Huang Transform

The development of portable and wireless instruments that measure the electrical activity of the cerebral cortex (EEG) allows the capture and analysis of neurobiological signals in multiple applications. A lot of neurological, psychological and psychiatric disorders have been evaluated by EEG. Traditional EEG data-analysis methods consider linear data and stationary processes. In particular, Fourier transform deals with signals that are composed of superimposed sinusoidal oscillations, and are signals of constant frequency and amplitude. This study analyzes non-linear and non-stationary data processing using Hilbert Huang Transform (HHT), and proposes a new topography method that allows representing the brain activity. The Hilbert Transform performed on each IMF component, allows transforming the spatio-temporal data to time-frequency space, computing the amplitude and instantaneous frequency for every IMF at every time-step. An initial taxonomy concluded among pairs of channels, where IMF defines a form factor, and the pair (A, f) defines the gap between the compared signals.