Figure 1. Experimental protocol-Single Trial Classification of Evoked EEG Signals Due to RGB Colors

Various methods exist to enhance and pre-process EEG signals by removing different artifacts like eye movement and blinking, Electrooculography (EOG) or Electromyography (EMG). The complexity of EEG signal's representation makes it difficult to define the circle that encloses most of the data points of their total. The problem with these methods is that they work at frequency domain or time domain, but not both, which causes a loss of important data during the processing stage. The researches show that the combination of frequency and time domain information can provide more completed features that improve the classification performance of EEG signals. Empirical Mode Decomposition (EMD) has recently been developed by N. (Huang Huang et al., 1998) as an adaptive time-frequency data analysis method. It has proven to be quite versatile in a broad range of applications for extracting signals from data generated in noisy nonlinear and non- stationary processes. Wavelet Transform (WT) is also an analysis method that uses the time- frequency domain. However, EMD acts essentially as a filter bank, resembling those involved in wavelet decompositions.https://www.edusoft.ro/brain/index.php/brain/issue/view/3