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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. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) have been widely used for the diagnosis of neurological diseases like epilepsy and sleep disorders. Support vector machines (SVMs) are widely used classifiers for the classification of EEG signals due to their better generalization performance. However, SVM suffers due to high computational complexity. To reduce the computations, twin support vector machines (TWSVM) solved smaller size quadratic optimization problems. To enhance the performance of the SVM and TWSVM models, prior information known as universum data has been incorporated in the universum SVM (USVM) and universum twin (UTSVM) models. Both SVM and UTSVM employ hinge loss which results in sensitivity to noise and instability. To overcome these issues and incorporate the prior information of the EEG signals, we propose a novel universum twin support vector machine with pinball loss function (Pin-UTSVM) for the classification of EEG signals. The proposed Pin-UTSVM is more stable for resampling and is noise insensitive. Furthermore, the computational complexity of proposed Pin-UTSVM model is similar to the standard UTSVM model. In the proposed approach, we used the interictal EEG signal as the universum data. Numerical experiments at varying level of noise show that the proposed Pin-UTSVM is more robust to noise compared to standard models. To show the efficiency of the proposed Pin-UTSVM model, we used multiple feature extraction techniques for the classification of the EEG signal. Experimental results reveal that the proposed Pin-UTSVM model is performing better compared to the existing models. Moreover, statistical tests show that the proposed Pin-UTSVM model is significantly better in comparison with the existing baseline models. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on advanced machine learning algorithms for biomedical data and image processing. Researchers in machine learning including those working in computer vision, image processing, biomedical analysis, and related fields when tied with experienced clinicians can play a significant role in understanding and working on complex medical data which ultimately improves patient care. Developing a novel machine-learning algorithm specific to medical data is a challenge and need of the hour. Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information. Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance, and low numbers of samples. Although the current research in this field has shown promising results, several research issues need to be explored as follows. There is a need to explore novel feature selection methods to improve predictive performance along with interpretation and to explore large-scale data in biomedical sciences. [ABSTRACT FROM AUTHOR]