Abstract | ||
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One of the most successful types of brain computer interfaces (BCI) is based on the P300 evoked potential (EP) elicited by oddball type of paradigms. Given a particular paradigm the main challenge is to obtain an efficient and robust classification. This paper proposes the use of Random Forest (RF), a tree based ensemble learning method providing state-of-the-art generalization performance, for P300 BCI classification. The performance of the proposed method is compared to both the most commonly used classifiers for this problem: the support vector machine (SVM), and the step-wise linear discriminant analysis (SWLDA); and to two state-of-the-art methods: the multiple convolutional neural networks (MCNN) and the ensemble support vector machine (ESVM). The proposed method has been evaluated on two public available BCI datasets: the BCI competition dataset II for healthy subjects and the image driven paradigm dataset for disabled subjects. The proposed method demonstrated a significant improvement in classification accuracy on both datasets. |
Year | Venue | Keywords |
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2013 | EUSIPCO | tree based ensemble learning method,bioelectric potentials,trees (mathematics),learning (artificial intelligence),bci competition dataset ii,brain-computer interfaces,pattern classification,multiple convolutional neural networks,evoked potential (ep),step-wise linear discriminant analysis,oddball paradigms,p300 based brain computer interface applications,image driven paradigm dataset,classifiers,p300 evoked potential,random forest classification,random forest (rf),p300 bci classification,ensemble support vector machine,brain computer interfaces (bcis) |
Field | DocType | Citations |
Computer science,Convolutional neural network,Brain–computer interface,Support vector machine,Artificial intelligence,Linear discriminant analysis,Random forest,Ensemble learning,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |
Name | Order | Citations | PageRank |
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Faisal Farooq | 1 | 245 | 17.57 |
Preben Kidmose | 2 | 36 | 11.27 |