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Mind blanking (MB) is a waking state during which we do not report any mental content, challenging the view of a constantly thought-oriented brain. Here, we comprehensively characterize the MB’s neurobehavioral profile with the aim to delineate its role during ongoing mentation. Using fMRI experience-sampling, we show that MB is reported less frequently, faster, and with low transitional dynamics among other mental states, pointing to its role as a transient mental relay. Regarding its neural underpinnings, we observe higher global signal amplitude during MB reports, indicating a distinct physiological substrate. Using the time-varying functional connectome MB reports get classified with high accuracy, suggesting that MB has a unique neural composition. Indeed, a pattern of globally positive-phase coherence shows the highest similarity to the connectivity patterns associated with MB reports. We observe that this pattern’s rigid signal architecture hinders content reportability due to the brain’s inability to differentiate signals in an informative way. Collectively, we show that MB has a unique neurobehavioural profile, indicating that non-reportable mental events can happen during wakefulness. Our results add to the characterization of spontaneous mentation and pave the way for more mechanistic investigations of MB’s phenomenology.Significance StatementThe human mind is generally assumed to be thought-oriented. Mind blanking (MB) challenges this stance because it appears as if we are derived of any particular mental content to report. We here show that, during spontaneous thinking, MB is a mental state that happens by default, it has a unique behavioural profile, and it is of a rigid neural architecture that does not permit the formulation of reportable contents. Our work essentially proposes that non-reportable mental events can happen during wakefulness, and challenges the view of the mind as a constant thought-oriented operator.

Graph signal processing (GSP) is a novel approach to analyse multi-dimensional neuroimaging data, constraining functional measures by structural characteristics in a single framework (i.e. graph signals). In this approach, functional time series are assigned to the vertices of the underlying weighted graph and GSP analysis is performed in each time point of the signal. Here we used GSP to study local brain connectivity changes in patients with disorders of consciousness based on resting state high density electroencephalography (hdEEG) recordings. Total variation of the graph signals is a measure of signal smoothness over the underlying graph. In this study, we constructed the underlying graph based on the geometrical distances between each electrode pairs in such a way that local smoothness of the signal can be studied. Total variation analysis in α-band showed that in the pathological states of altered consciousness, local short range communication of brain regions in this frequency band is stronger than in healthy states which shows that information is segregated in local regions in patients with disorders of consciousness.

Handwritten digit recognition plays a crucial role in various automatic systems and industries nowadays. In steel companies, the code digits written on slab forehead should be recognized with high accuracy because these codes are used for determination of suitable thickness of steel slabs in rolling procedure. Because of special artifacts exist in these kinds of images such as color sagging, faint colors, and color separation, most of the common digit recognition algorithms have low accuracy in recognition of code digits. In this paper, a new two stage algorithm is proposed in order to reach high accuracy in determination of the handwritten codes written on slabs forehead and are corrupted by color sagging, faint colors and color separation. Applying our proposed algorithm on a dataset of images of slabs in Isfahan Steel Company shows that accuracy of the algorithm is 89.12% which is much higher than common and recently proposed algorithms.

Event Related Potentials (ERPs) are generated in ongoing brain electrical activity due to visual, auditory, or sensory stimuli. These signals have very low SNR and are contaminated by background EEG. Extraction of single trial ERPs from background EEG is a challenging task due to overlapping nature of the frequency bands of ERP and EEG signals and much higher power of EEG than ERPs. In this paper we proposed a method based on wavelet transform and adaptive noise cancelers in order to extract single trial ERPs from background EEG in very low SNR conditions. Simulation results show the superiority of the proposed algorithm over the existing methods. In addition, performance of the algorithm is justified under different noise models namely White Gaussian Noise, Auto Regressive, and Real EEG signals.

Detecting the active regions of the brain during cognitive functions is one of the important problems in cognitive neuroscience and disorder diagnosis. One of the promising approaches to solve this problem is to use General Linear Model (GLM) in functional Magnetic Resonance Imaging (fMRI) data. The main difficulty of the GLM method is to determine a flexible design matrix to model mentioned problem appropriately. In this paper, an approach to the critical construction of a flexible design matrix for precise detection of active regions of the brain, according to response in synthetic fMRI data based on GLM is presented. Should the design matrix is accurate, the next detection algorithm can extract a correct response from a very low signal to noise ratio (SNR); therefore, the presented design matrix is flexible to eschew over fitting and capture unfamiliar slow drifts. Using a sparse Bayesian learning method, some specific regressors are selected for flexible design matrix. Results show clearly prominent performance of suggested algorithm rather than conventional t-test methods and other conventional Bayesian analysis of fMRI data.