Islamic Azad University Damavand, Iran
*Corresponding author: Maryam Sajedi, Islamic Azad University Damavand, Iran, Email: Msajedi93@gmail.com/Payamporkar@damavandiau.ac.ir
Submission: January 20, 2018; Published: February 06, 2018
ISSN : 2576-8816Volume3 Issue3
Electro Encephalogram (EEG) is a technique to record electro activity of human brain. The most common way is using an electrode attached to scalp which can facilitate applying non invasive technique for EEG.P300 is one of the most studies using Event Related Potentials (ERP) in Brain Computer Interfaces (BCI). EEG raw data are noisy which make the p300 wave as accurate as possible by using appropriate feature extraction. The mentioned method combined with powerful classifier. P300 speller is one of the important BCI application that allows the selection of Characters on virtual keyboard by analyzing recorded electroencephalography activity. On the other hand, in recent days there have been done a lot of research in field of utilizing brain activity to interact with the external environment as a defined procedure named (BCIs). This paper proposes a novel method to reduce feature dimension by using Kernel Principal Components Analysis (KPCA) which has a significant and considerable advantage over other methods, such as Principal Component Analysis (PCA). This approach enables us to have possibility of reduction of feature dimension in non-linear systems and it will result in promising outcomes such as increase precision and also Signal-to Noise Ratio (SNR) will be enhanced. This method can detect P300 signals feature with high level of accuracy. This technique makes use of wavelet coefficients to reduce feature dimension. Support vector machine (SVM) was used as classifier. The proposed method has achieved accuracy of 98.51% for subject A and 95.12% for subject B, thus it could be assumed as the proposed method could yield a high degree of accuracy. So our analysis demonstrates that the proposed approach achieves better detection accuracy compared to traditional methods including canonical correlation analysis and its variants. Index Terms- The most widely used feature extraction algorithms, Brain-computer interface BCI, Brain Electrical Signals, EEG, KPCA.