TY - JOUR
T1 - EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems
AU - Nguyen, Thanh
AU - Khosravi, Abbas
AU - Creighton, Douglas
AU - Nahavandi, Saeid
N1 - Funding Information:
This research is supported by the Australian Research Council (Discovery Grant DP120102112) and the Centre for Intelligent Systems Research (CISR) at Deakin University.
Publisher Copyright:
© 2015 Elsevier Ltd. All rights reserved.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - 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.
AB - 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.
KW - BCI competition II
KW - EEG signal classification
KW - Interval type-2 fuzzy logic system
KW - Receiver operating characteristics (ROC) curve
KW - Wavelet transformation
UR - http://www.scopus.com/inward/record.url?scp=84923239675&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2015.01.036
DO - 10.1016/j.eswa.2015.01.036
M3 - Article
AN - SCOPUS:84923239675
SN - 0957-4174
VL - 42
SP - 4370
EP - 4380
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 9
ER -