EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

Nguyen, Thanh, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems, Expert systems with applications, vol. 42, no. 9, pp. 4370-4380.

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Title EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems
Author(s) Nguyen, ThanhORCID iD for Nguyen, Thanh
Khosravi, AbbasORCID iD for Khosravi, Abbas
Creighton, DouglasORCID iD for Creighton, Douglas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Journal name Expert systems with applications
Volume number 42
Issue number 9
Start page 4370
End page 4380
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-06-01
ISSN 0957-4174
Keyword(s) BCI competition II
EEG signal classification
Interval type-2 fuzzy logic system
Receiver operating characteristics (ROC) curve
Wavelet transformation
Science & Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Operations Research & Management Science
Computer Science
Receiver operating characteristics (ROC)
Summary The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
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