Abstract
Event-related mental task information collected from electroencephalography (EEG) signals, which are functionally related to different brain areas, possesses complex and non-stationary signal features. It is essential to be able to classify mental task information through the use in brain-computer interface (BCI) applications. This paper proposes a wavelet packet transform (WPT) technique merged with a specific entropy biomarker as a feature extraction tool to classify six mental tasks. First, the data were collected from a healthy control group and the multi-signal information comprised six mental tasks which were decomposed into a number of subspaces spread over a wide frequency spectrum by projecting six different wavelet basis functions. Later, the decomposed subspaces were subjected to three entropy-type statistical measure functions to extract the feature vectors for each mental task to be fed into a backpropagation time-recurrent neural network (BPTT-RNN) model. Cross-validated classification results demonstrated that the model could classify with 85% accuracy through a discrete Meyer basis function coupled with a Renyi entropy biomarker. The classifier model was finally tested in the Simulink platform to demonstrate the Fourier series representation of periodic signals by tracking the harmonic pattern. In order to boost the model performance, ant colony optimization (ACO)-based feature selection method was employed. The overall accuracy increased to 88.98%. The results underlined that the WPT combined with an entropy uncertainty measure methodology is both effective and versatile to discriminate the features of the signal localized in a time-frequency domain.
Author Statement
Research funding: Authors state no funding involved.
Conflict of interest: Authors state no conflict of interest.
Informed consent: Informed consent is not applicable.
Ethical approval: The conducted research is not related to either human or animal use.
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Articles in the same Issue
- Frontmatter
- Reviews
- Invasive and non-invasive point-of-care testing and point-of-care monitoring of the hemoglobin concentration in human blood – how accurate are the data?
- A review on the pattern detection methods for epilepsy seizure detection from EEG signals
- Research articles
- Robust and energy-efficient expression recognition based on improved deep ResNets
- Entropy-based feature extraction technique in conjunction with wavelet packet transform for multi-mental task classification
- On the feasibility of a liquid crystal polymer pressure sensor for intracranial pressure measurement
- Investigation of the retention forces of secondary telescopic crowns made from Pekkton® ivory in combination with primary crowns made from four different dental alloys: an in vitro study
- A comparative study of tapped and untapped pilot holes for bicortical orthopedic screws – 3D finite element analysis with an experimental test
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- Obtaining the sGAG distribution profile in articular cartilage color images
- Optimization of the proposed hybrid denoising technique to overcome over-filtering issue