# Cross-Subject Classification of Effectiveness in Performing Cognitive Tasks Using Resting-State EEG
## 1 Introduction  
Mathematical skills are inherent in all people and are closely related to wages and professional success [1, 2]. Those who had difficulty acquiring math skills during their school years are at greater risk of developing a lifelong learning disability and economic failure as adults [3, 4]. Mathematical competencies, on the other hand, lead to higher education, especially in the field of engineering, and directly contribute to a career, therefore being critical for academic and professional success [3]. Many studies have focused on the neurophysiological correlates of various aspects of mathematical cognition, including the development of mathematical skills [5, 6, 7, 8, 9]. One functional magnetic resonance imaging (fMRI) paper examined the topography of functional brain activation, particularly in mathematics professionals [10]. We have also reported that the electroencephalography (EEG) spectral pattern differed between specialists in mathematics and specialists in the humanities [11]; and different EEG patterns were induced by solving verbal and mathematic tasks that made it possible to classify subject-specific EEG [12]. However, while most studies focused on specific EEG patterns [5, 9, 11] and/or specific brain regions [7, 8, 10] playing crucial roles in mathematical cognition, only a few explored EEG features in terms of classification of individual cognitive abilities or their prediction in samples ofneurotypical populations [13]. A powerful approach for early detection of cognitive abilities could be EEG classification using specific EEG pattern recognition by machine learning algorithms [14]. A promising area of research is the search for correlates of the behavioral phenotype and resting-state EEG. The spectral power of resting-state EEG frequency bands demonstrated a higher heritability in a large cohort in twin studies [15; 16]. Moreover, some frequency patterns of spontaneous brain oscillations correlated with specific genotypes in healthy aging [17]. In parallel, fMRI studies showed that individual spontaneous neural activity at rest is correlated with functionally active networks and cognitive performance suggesting that patterns of resting-state activity could be explored as endophenotypes of cognitive abilities [18; 19]. EEG studies also reported the predictive power of resting-state EEG patterns, for example, connectivity for attention performance [20] and spectral power for insight [21]. Resting-state EEG activity is related to various cognitive functions and could be inherited together with cognitive abilities, we assumed that success in mathematical education as a behavioral phenotype can also be reflected in resting EEG. While neuroscience applies machine learning models to different classification aims including functional state recognition, imagery movement, and brain disorder detection [22; 23; 24; 25], the success use of subject-data-independent models remains challenging. Even in the case of significant statistical differences obtained by t- or f-statistic based permutation tests [26], the non-stationarity of EEG signals, various artifacts and the high variability between individuals lead to difficulties in training robust models [22]. Mathematical proficiency associated with both inherited and acquired cognitive skills can serve as a reliable and predictable individual trait for testing cross-subject classification algorithms of machine learning using the oscillatory patters of resting-state EEG. In this study, we focused on the application of machine learning algorithms to detect specific frequency patterns of resting-state EEG in volunteers with an advanced level of education in mathematics (AM) and to distinguish them from individuals with a basic level of education in mathematics (BM).