10.4225/03/5a1372afa2067
Dehzangi, Omid
Omid
Dehzangi
Boostani, Reza
Reza
Boostani
Jahromi, Mansoor Zolghadri
Mansoor Zolghadri
Jahromi
Fuzzy rule generation using data mining techniques to classify two-class BCI experiment
Monash University
2017
Bioinformatics -- Congresses
Computational biology -- Congresses
Computer vision in medicine -- Congresses
Computational biology -- Methods -- Congresses
Pattern recognition, automated -- Methods -- Congresses
Brain-computer interface
EEG signals
Fuzzy systems
Data mining
Rule-weighting
2008
conference paper
1959.1/63718
monash:7866
Bioinformatics Software
Bioinformatics
Pattern Recognition and Data Mining
2017-11-21 01:22:21
Conference contribution
https://bridges.monash.edu/articles/conference_contribution/Fuzzy_rule_generation_using_data_mining_techniques_to_classify_two-class_BCI_experiment/5619520
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.