Key result
TQWT-based feature extraction with a k-NN classifier outperforms BT and SVM for EEG signal classification.
Why the study?
EEG signals are complex, non-stationary, aperiodic, and nonlinear, making their sustained oscillation and non-oscillation transients challenging to analyze using linear approaches.
Does a TQWT-based feature extraction technique improve the classification accuracy of EEG signals compared to existing methods?
Does a TQWT-based feature extraction technique improve the classification accuracy of EEG signals compared to existing methods?
A novel TQWT-based feature extraction technique combined with a k-NN classifier improves the accuracy of EEG signal classification, which may assist in brain health monitoring.
Proposed TQWT-statistical scheme may aid nonlinear EEG analysis; leaves open clinical validation for brain monitoring.
BACKGROUND: Electroencephalogram (EEG) signals are important for brain health monitoring applications. Characteristics of EEG signals are complex, being non-stationarity, aperiodic and nonlinear in nature. EEG signals are a combination of sustained oscillation and non-oscillation transients that are challenging to deal with using linear approaches. METHOD: This research proposes a new scheme based on a tunable Q-factor wavelet transform (TQWT) and a statistical approach to analyse various EEG recordings. Firstly, the proposed method decompose EEG signals into different sub-bands using the TQWT method, which is parameterized by its Q-factor and redundancy. This method depends on the resonance of a signal, instead of frequency or scaling as in the Fourier and wavelet transforms. Secondly, using a statistical feature extraction on the sub-bands to divide each sub-band into n windows, and then extract several statistical features from each window. Finally, the extracted features are forwarded to a bagging tree (BT), k nearest neighbor (k-NN), and support vector machine (SVM) as classifiers to evaluate the performance of the proposed feature extraction technique. RESULTS: The proposed method is tested on two different EEG databases: Bonn University database and Born University database. The experimental results demonstrate that the proposed feature extraction algorithm with thek-NN classifier produces the best performance compared with the other two classifiers. Comparison with existing methods: In order to further evaluate the performances, the proposed scheme is compared with the other existing methods in terms of accuracy. The results prove that the proposed TQWT based feature extraction method has great potential to extract discriminative information from brain signals. CONCLUSION: The outcomes of the proposed technique can assist doctors and other health experts to identify diversified EEG categories.
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Ghayab et al. (2018) studied Brain signal classification (EEG). Tunable Q-factor wavelet transform (TQWT) based feature extraction vs. Bagging tree (BT), support vector machine (SVM), and other existing methods was evaluated on Classification performance/accuracy. A tunable Q-factor wavelet transform based feature extraction method combined with a k-NN classifier produced the best performance for EEG signal classification compared to BT and SVM classifiers.
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