Developments in EEG Analysis, Protocol Selection, and Feedback Delivery

Abstract

It stands to reason that the better the extracted information from the electroencephalogram (EEG), the better the data analysis and subsequent EEG biofeedback. At the core of digital signal processing used in our field is a linear filtering technology that discards significant EEG features. Brainwaves are nonlinear, nonstationary, and noisy signals. The purpose of this letter to the editor is to illuminate the Hilbert-Huang Transform's (HHT's) (Huang et al., 1998) ability to empirically quantify nonlinear, nonstationary signals such as the EEG. I demonstrate how this technique can detect and extract a tiny noisy complex waveform from a raw signal while preserving the majority of the important information from the original source. I contrast and compare the HHT to other quantitative techniques.