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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]