An Efficient and High Accuracy P300 Detection for Brain Computer Interface System Based on Kernel Principal Component Analysis

Human machine interaction with the use of brain signals has been made possible by the advent of the technology popularly known as brain computer interface (BCI). P300 is one such brain signal which is used in many BCI systems. The problems associated with most of the existing P300 detection methods are that they are time consuming and computationally complex as they follow the procedure of averaging the values obtained from multiple trials. Also the existing single trial methods have been able to obtain only moderate accuracy levels. In this paper, a novel approach which for achieving a high level of accuracy has been proposed for single trial P300 signal detection amidst noise and artifacts. In this method features were obtained by applying Discrete Wavelet Transform followed by a technique making use of the obtained wavelet coefficients. Kernel Principal Component Analysis (KPCA) was used for reducing the feature dimension. Classification of the P300 signal using the reduced features was done using Support Vector Machine (SVM). The Dataset used was the Dataset II of the third BCI Competition. An accuracy of 98.53% was achieved for Subject S1 (signal obtained from the first person) and 99.25% for Subject S2 (signal obtained from the second person) by using the proposed method. A high level of accuracy was obtained, as compared to many existing techniques. Also the speed of classification was improved with the use of reduced feature dimensions.