Fuzzy rule generation using data mining techniques to classify two-class BCI experiment

In recent years, the Graz Brain-Computer Interface (BCI) has been developed and different aspects of this new field of research such as feature extraction and classification, mode of operation, mental strategy, and type of feedback have been investigated. In this paper, a Fuzzy Rule-Based Classification System (FRBCS) is presented in which a novel approach for fuzzy rule generation is proposed. The proposed algorithm makes the use of data mining principles, which are used by frequent pattern mining algorithms. Employing these principles enables us to well generate rules for subsequent classification purposes. Finally, a rule-weighting mechanism is investigated to tune the rule-base to have better classification ability. To evaluate the performance of the proposed scheme features containing standard bandpower and adaptive autoregressive coefficients are determined on four subjects in order to increase the performance of a cue-based BCI system for imagery classification tasks (left and right hand movements). As comparative classifiers, a number of successful methods of classification including Adaboost, Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) have been assessed. The results show that the proposed method of classification is effective in prediction ability of choosing between the left and right imagery tasks. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.