Analysis and Identification of the EEG Signals from Visual Stimulation

In this paper, we describe a method for analysis and identification of Electroencephalography (EEG) about visual stimulation. Here, ODDBALL task has been performed by using 4 different categories images to measure and analysis the EEG Signals, after which the P300 information of the visual stimulation can be detected and identified. In order to improve the identification ratio while avoiding the effects of noise, 1) in the pre-processing stage, we perform Normalization, Gaussian filter, and Non-maximum Suppression for emphasizing the patterns around the peak of the P300; 2) we construct high-dimensional vector which consists of the data obtained from 4 different electrodes. Experiments for comparing the method proposed with others using Linear discriminant analysis (LDA), K-nearest neighbour (k-NN) and Nearest mean (NM) have been implemented, the results of which has confirmed that our method owns an improvement of identification ratio