Key result
The Ensemble Empirical Mode Decomposition (EEMD) method generally outperformed EMD and CEEMDAN in terms of classification accuracy, sensitivity, and specificity for multi-mental task classification from EEG signals.
Why the study?
Mental task classification based on EEG signals requires efficient feature representation from raw EEG signals for classifying human cognitive activity in brain-computer interface systems.
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A novel feature representation using EMD and SVM improves the classification of mental tasks from EEG signals for brain-computer interfaces.
Mental task classification (MTC), based on the electroencephalography (EEG) signals is a demanding brain–computer interface (BCI). It is independent of all types of muscular activity. MTC-based BCI systems are capable to identify cognitive activity of human. The success of BCI system depends upon the efficient feature representation from raw EEG signals for classification of mental activities. This paper mainly presents on a novel feature representation (formation of most informative features) of the EEG signal for the both, binary as well as multi MTC, using a combination of some statistical, uncertainty and memory- based coefficient. In this work, the feature formation is carried out in the two stages. In the first stage, the signal is split into different oscillatory functions with the help of three well-known empirical mode decomposition (EMD) algorithms, and a new set of eight parameters (features) are calculated from the oscillatory function in the second stage of feature vector construction. Support vector machine (SVM) is used to classify the feature vectors obtained corresponding to the different mental tasks. This study consists the problem formulation of two variants of MTC; two-class and multi-class MTC. The suggested scheme outperforms the existing work for the both types of mental tasks classification.
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Gupta et al. (2022) studied Cognitive tasks (Brain-Computer Interface) (n=6). Ensemble Empirical Mode Decomposition (EEMD) vs. Empirical Mode Decomposition (EMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was evaluated on Classification accuracy. The Ensemble Empirical Mode Decomposition (EEMD) method generally outperformed EMD and CEEMDAN in terms of classification accuracy, sensitivity, and specificity for multi-mental task classification from EEG signals.
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