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Microstate analysis. (a) 6 microstate class maps generated by the model with assigned colors. (b) GFP plots of TEPs averaged across tested intensities for each stimulation orientation are split into intervals corresponding to microstate classes to illustrate how microstates correspond to TEP components. (c) Microstate sequences of TEPs of all conditions; timepoints labelled with different microstate classes are color-coded according to panel a.
Source publication
Background
The angular gyrus (AG) is involved in numerous cognitive processes, and structural alterations of the AG are reported in many neuropsychiatric diseases. Because abnormal excitability or connectivity of such cortical hubs could precede structural alterations and clinical symptoms, approaches assessing their functional state are needed. Th...
Contexts in source publication
Context 1
... model (92.3% explained total variance) yielded six microstate classes (Fig. 4a) and attributed a prominent topography to each TEP component (Fig. 4b). Intervals of low TEP amplitude, particularly around 50 ms, were associated with rapidly switching short-lasting microstates, suggesting moments of topographic instability or transition (Fig. 4b,c). Analysis of temporal features was performed for all microstate ...
Context 2
... model (92.3% explained total variance) yielded six microstate classes (Fig. 4a) and attributed a prominent topography to each TEP component (Fig. 4b). Intervals of low TEP amplitude, particularly around 50 ms, were associated with rapidly switching short-lasting microstates, suggesting moments of topographic instability or transition (Fig. 4b,c). Analysis of temporal features was performed for all microstate classes, significant results were obtained for classes 2, 3 and ...
Context 3
... model (92.3% explained total variance) yielded six microstate classes (Fig. 4a) and attributed a prominent topography to each TEP component (Fig. 4b). Intervals of low TEP amplitude, particularly around 50 ms, were associated with rapidly switching short-lasting microstates, suggesting moments of topographic instability or transition (Fig. 4b,c). Analysis of temporal features was performed for all microstate classes, significant results were obtained for classes 2, 3 and ...
Citations
... It is located at the connection between the temporal and occipital lobes, being a cortical node involved in multiple functions: episodic memory, the retrieval of facts in calculation processes, the retrieval of verbally expressed numerical facts, visual-spatial processing, and abstract concepts [22,[35][36][37]. The area is more active in the left hemisphere [37], effectively in the central-parietal area on the CP5 channel [38], with, in our case, the closest electrode being P3. ...
Bringing out brain activity through the interpretation of EEG signals is a challenging problem that involves combined methods of signal analysis. The issue of classifying mental states induced by arithmetic tasks can be solved through various classification methods, using diverse characteristic parameters of EEG signals in the time, frequency, and statistical domains. This paper explores the results of an experiment that aimed to highlight arithmetic mental tasks contained in the PhysioNet database, performed on a group of 36 subjects. The majority of publications on this topic deal with machine learning (ML)-based classification methods with supervised learning support vector machine (SVM) algorithms, K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Decision Trees (DTs). Also, there are frequent approaches based on the analysis of EEG data as time series and their classification with Recurrent Neural Networks (RNNs), as well as with improved algorithms such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BLSTM), and Gated Recurrent Units (GRUs). In the present work, we evaluate the classification method based on the comparison of domain limits for two specific characteristics of EEG signals: the statistical correlation of pairs of signals and the size of the spectral peak detected in theta, alpha, and beta bands. This study provides some interpretations regarding the electrical activity of the brain, consolidating and complementing the results of similar research. The classification method used is simple and easy to apply and interpret. The analysis of EEG data showed that the theta and beta frequency bands were the only discriminators between the relaxation and arithmetic calculation states. Notably, the F7 signal, which used the spectral peak criterion, achieved the best classification accuracy (100%) in both theta and beta bands for the subjects with the best results in performing calculations. Also, our study found the Fz signal to be a good sensor in the theta band for mental task discrimination for all subjects in the group with 90% accuracy.
