The sense of agency (SoA) is part of psychophysiological modules related to the self. Disturbed SoA is found in several clinical conditions, hence understanding the neural correlates of the SoA is useful for the diagnosis and determining the proper treatment strategies. Although there are several neuroimaging studies on SoA, it is desirable to translate the knowledge to more accessible and inexpensive EEG-based biomarkers for the sake of applicability. However, SoA has not been widely investigated using EEG. To address this issue, we designed an EEG experiment on healthy adults (n = 15) to determine the sensitivity of EEG on the SoA paradigm using hand movement with parametrically delayed visual feedback. We calculated the power spectral density over the traditional EEG frequency bands for ten delay conditions relative to no delay condition. Independent component analysis and equivalent current dipole modeling were applied to address artifact rejection, volume conduction, and source localization to determine the effect of interest. The results revealed that the alpha and low-beta EEG power increased in the parieto-occipital regions in proportion to the reduced SoA reported by the subjects. We conclude that the parieto-occipital alpha and low-beta EEG power reflect the sense of agency.

The Anti-Nuclear Antibodies (ANAs) testing is the primary serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted mainly by the Indirect Immunofluorescence (IIF) on Human Epithelial cell-substrate (HEp-2) protocol. However, due to its high variability, humansubjectivity, and low throughput, there is an insistent need to develop an efficient Computer-Aided Diagnosis system (CADs) to automate this protocol. Many recently proposed Convolutional Neural Networks (CNNs) demonstrated promising results in HEp-2 cell image classification, which is the main task of the HE-p2 IIF protocol. However, the lack of large labeled datasets is still the main challenge in this field. This work provides a detailed study of the applicability of using generative adversarial networks (GANs) algorithms as an augmentation method. Different types of GANs were employed to synthesize HEp-2 cell images to address the data scarcity problem. For systematic comparison, empirical quantitative metrics were implemented to evaluate different GAN models' performance of learning the real data representations. The results of this work showed that though the high visual similarity with the real images, GANs' capacity to generate diverse data is still limited. This deficiency in the generated data diversity is found to be of a crucial impact when used as a standalone method for augmentation. However, combining limited-size GANsgenerated data with classic augmentation improves the classification accuracy across different variants of CNNs. Our results demonstrated a competitive performance for the overall classification accuracy and the mean class accuracy of the HEp-2 cell image classification task.INDEX TERMS Computer-aided diagnosis systems (CADs), Convolutional Neural Networks (CNNs), Data augmentation, Data diversity, Evaluation metrics, Generative adversarial networks (GANs), HEp-2 cell image classification.

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Spectral subtraction denoising as a new preprocessing block for Slow Cortical Potential(SCP)-based brain computer interface is proposed. This method adaptively reduces the noise attached to the signal using spectral subtraction denoising. We show that this technique provides better performance when low number of electrodes is used. This suggests its potential application in portable BCI real time applications where low power and minimum weight are required for practical use. Here, the classification accuracy was used as performance measure. The developed method provides relatively better performance compared to the more commonly used wavelet shrinkage signal denoising.