Adaptive integrated classification method based on immune optimization for EEG
                                                          LI Jiawei, REN Lihong, DING Yongsheng, CHEN Lei
              (EngineeringResearch Center of Digitized Textile & Apparel Technology,Donghua University, Shanghai 201620, China)

Abstract: Aiming at the classification and recognition of brain wave signals in braincomputer interaction (BCI), the algorithms of preprocessing, feature extraction and feature classification of P300 signals in brain electrical signals were mainly studied in the research of P300 classification algorithm of EEG signal, an adaptive SVM classification method was proposed to optimize the integrated SVM classification learner based on Bagging by using the diversity of immune algorithm and selfadjusting ability. The accuracy of P300 EEG recognition and selfadaptability to different individuals can be improved by this method. The results indicate that using adaptive integrated classification algorithm on P300 EEG data of BCI Competition III Dataset II can identify subjects' EEG intent, and classification of P300 EEG can achieve higher classification accuracy. The final experimental results are stable at 98% of the classification accuracy.

Key words: braincomputer interface (BCI);P300 signal; support vector machine (SVM); integrated classifier; immune optimization