Brain Active Areas Associated with a Mental Arithmetic Task: An eLORETA Study.

The neural underpinnings of mental calculation, the fundamentals of arithmetic representations and processes, and the development of arithmetic abilities have been explored by researchers over the years. In the present work, we report a study that analyzes the brain-activated areas of a group of 35 healthy subjects (9 males, 26 females, mean age ± SD = 18.23 ± 2.20 years) who performed a serial subtraction arithmetic task. In contrast to most of the studies in the literature based on fMRI, we performed the brain active source reconstruction starting from EEG signals by means of the eLORETA method. In particular, the subjects were classified as bad counters or good counters, according to the results of the task, and the brain activity of the two groups was compared. The results were statistically significant only in the beta band, revealing that the left limbic lobe was found to be more active in people showing better performance. The limbic lobe is involved in visuospatial processing, memory, arithmetic fact retrieval, and emotions. However, the role of the limbic lobe in mental arithmetic has been barely explored, so these interesting findings could represent a starting point for future in-depth analyses. Since there is evidence in the literature that the motor system is affected by the execution of arithmetic tasks, a more extensive knowledge of the brain activation associated with arithmetic tasks could be exploited not only for the assessment of mathematical skills but also in the evaluation of motor impairments and, consequently, in rehabilitation for motor disorders.

Analysis of the automatic detection of critical epochs from coma-EEG by dominant components and features extraction.

Recent works showed that meaningful dominant components can be extracted from the EEG of patients in coma through an algorithm based on the joint use of Principal Component Analysis (PCA) and Independent Component Analysis (ICA). A procedure for automatic critical epoch detection would support the doctor in the long time monitoring of the patients, thus we investigated the automatic quantification of the criticality of the epochs. In this paper we propose a procedure based on the extraction of dominant components and features for the quantification of the critical state of each epoch, in particular we use entropy and kurtosis. This feature analysis allowed us to detect some epochs that are likely to be critical and that are worth being carefully inspected electrographically by the expert.