Classification of human concentration in EEG signals using Hilbert Huang transform

The electroencephalogram (EEG) is an important technique that allows people to study brain signals. Classifying whether a person is concentrating or not is almost impossible with our naked eye. Thus, a system that could accurately distinguish these two categories is highly valued. Previously, the study to determine a mental state of concentration and non-concentration is based on the Fourier Transform. In this study, we employed Hilbert Huang Transform (HHT) to extract important features for this mental classification task. HHT consists of two step: (1) Using EMD and (2) Hilbert Transform. The EMD will decompose the signal into a collection of Intrinsic Mode Functions (IMF). Then Hilbert Transform is employed to obtain the IMF to compute the power spectrum to extract important features based on different subbands i.e. gamma, beta, alpha, theta and delta. An Extreme Learning Machine (ELM) is then used as a classifier to distinguish the two mental states. Four different sets of investigations were performed using the existing FFT and three investigated the HHT framework including single IMF, two and three IMFs. The results showed that the HHT with single IMF offers slight improvement compared to the existing FFT.