Anindya Bijoy Das

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In this paper, a comprehensive analysis for the discrimination of the focal and non-focal electroencephalography (EEG) signals is carried out in the ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) domains. A number of spectral entropy-based features such as the Shannon entropy,(More)
In this paper, a comprehensive statistical analysis of electroencephalogram (EEG) signals is carried out in the dual tree complex wavelet transform domain using a publicly available EEG database. It is shown that variance and kurtosis can be effective in distinguishing EEG signals at sub-band levels. It is further shown that the parameters of a normal(More)
In this paper, a statistical method has been proposed to identify motor imagery left and right hand movements from electroencephalogram (EEG) signals in the Dual Tree Complex Wavelet Transform (DTCWT) domain. The total experiment is carried out with the publicly available benchmark BCI-competition 2003 Graz motor imagery dataset. First, the EEG signals are(More)
In this paper, a statistical method for automatic detection of seizure and epilepsy in the dual-tree complex wavelet transform(DT-CWT) domain is proposed. Variances calculated from the EEG signals and their DT-CWT sub-bands are utilized as features in the classifiers such as artificial neural network(ANN) and support vector machine(SVM). Studies are(More)
In this paper, a statistical method of classifying Electroencephalogram (EEG) data for automatic detection of epileptic seizure is carried out using a publicly available scalp EEG database. The classification is carried out to distinguish the seizure segments from the non-seizure ones. The higher order moments (specifically variance) have been calculated in(More)
In this paper, a statistical analysis of EEG signals is carried out in the dual tree complex wavelet transform (DT-CWT) domain. It is shown that Bessel k-form(BKF) pdf can suitably model the DT-CWT sub-bands and the BKF parameters in various DT-CWT sub-bands can discriminate various types of EEG data effectively. Next these parameters are utilized by the(More)
In this paper, a sub-band correlation-based method is proposed for the automatic detection of epilepsy and seizure. The analysis is carried out by decomposing the electroencephalogram (EEG) signals, collected from a publicly available EEG database, into the dual tree complex wavelet transform(DT-CWT) domain. An Artificial Neural Network(ANN) is employed as(More)