EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems
作者：
Highlights：
• Propose Haar wavelet transformation and ROC curve for EEG signal feature extraction.
• Combine wavelets and interval type-2 fuzzy logic system for EEG signal classification.
• Benchmark datasets downloaded from the BCI competition II are used for experiments.
• Proposed wavelet-IT2FLS outperforms the winner methods of the BCI competition II.
• IT2FLS dominates competing classifiers: FFNN, SVM, kNN, AdaBoost and ANFIS.
摘要
•Propose Haar wavelet transformation and ROC curve for EEG signal feature extraction.•Combine wavelets and interval type-2 fuzzy logic system for EEG signal classification.•Benchmark datasets downloaded from the BCI competition II are used for experiments.•Proposed wavelet-IT2FLS outperforms the winner methods of the BCI competition II.•IT2FLS dominates competing classifiers: FFNN, SVM, kNN, AdaBoost and ANFIS.
论文关键词：Interval type-2 fuzzy logic system,Wavelet transformation,Receiver operating characteristics (ROC) curve,EEG signal classification,BCI competition II
论文评审过程：Available online 29 January 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.01.036
