Evaluation and Comparision of Common Spatial Patterns (CSP) and Intelligent Segmentation in P300 Detection

In This Paper, two different feature extraction methods were studied and their performances in pattern recognition based- P300 detection were compared. These two methods were Common Spatial Pattern (CSP) and intelligent segmentation. Data set II (P300 speller) from the BCI competition 2005 was used. After pre-processing and feature extraction, these features were compared. For this purpose, first, a statistical analysis had been applied for evaluating the fitness of each feature in discriminating between target and non target signals. Then, each of these two groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Furthermore by using Stepwise Linear Discriminant Analysis (SWLDA), the best set of features was selected. Finally in this research, the best result for P300 detection was 95.25% for intelligent segmentation as a feature extraction method. This result shows that intelligent segmentation is better than CSP method for P300 detection.