EMD-based temporal and spectral features for the classification of EEG signals using supervised learning

Riaz, Farhan, Hassan, Ali, Rehman, Saad , Niazi, Imran Khan and Dremstrup, Kim (2016) EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24 (1). pp. 28-35. ISSN 1534-4320

Full content URL: https://doi.org/10.1109/TNSRE.2015.2441835

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Item Type:Article
Item Status:Live Archive


This paper presents a novel method for feature extraction from electroencephalogram (EEG) signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the EMD gives an effective time-frequency analysis of nonstationary signals. The intrinsic mode functions (IMF) obtained as a result of EMD give the decomposition of a signal according to its frequency components. We present the usage of upto third order temporal moments, and spectral features including spectral centroid, coefficient of variation and the spectral skew of the IMFs for feature extraction from EEG signals. These features are physiologically relevant given that the normal EEG signals have different temporal and spectral centroids, dispersions and symmetries when compared with the pathological EEG signals. The calculated features are fed into the standard support vector machine (SVM) for classification purposes. The performance of the proposed method is studied on a publicly available dataset which is designed to handle various classification problems including the identification of epilepsy patients and detection of seizures. Experiments show that good classification results are obtained using the proposed methodology for the classification of EEG signals. Our proposed method also compares favorably to other state-of-the-art feature extraction methods.

Keywords:computer science, Electroencephalography, Feature extraction, Support vector machines, Wavelet analysis, Time-frequency analysis, Signal processing, Epilepsy
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:52394
Deposited On:18 Nov 2022 11:20

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