@ARTICLE{26583223_221103793_2018,
author = {Elena Chemerisova and Michael Atanov and Ilya Mikheev and Olga Martynova},
keywords = {, EEG, power spectral density, mental operations, artificial neural networkclassification accuracy},
title = {Classification of Verbal and Mathematical Mental Operations Based on the Power Spectral Density of EEG},
journal = {Psychology. Journal of Higher School of Economics},
year = {2018},
volume = {15},
number = {2},
pages = {268-278},
url = {https://psy-journal.hse.ru/en/2018-15-2/221103793.html},
publisher = {},
abstract = {A classification of spectral patterns of EEG underlies several cognitive neurotechnologies including passive and active brain-computer interfaces. Despite arithmetic tasks often being used in studies of cognitive workload, there is a lack of findings describing a possibility to recognize EEG patterns related to different types of math operations. In the present work, we have shown that the power spectral density of EEG can be used to classify types of mental operations including a classification of verbal and different mathematical tasks for simple arithmetic operations or logical tasks with arithmetic progressions. The verbal tasks were separated from arithmetic ones significantly better than arithmetic from logical tasks, and verbal from logical tasks. Better discrimination of verbal tasks from arithmetic but not from logical tasks supports the hypothesis of unique EEG patterns associated with verbal activity that apparently differ from mental operations in arithmetic. Additionally, we compared the behavioral performance in problem solving and accuracy of EEG classification in two groups of subjects with education in math or humanities (N=8+8). We obtained the predicted differences related to better performance of the math group in solving math tasks than the humanitarian group. However, the classification accuracy of tasks based on EEG did not differ significantly between groups and was essentially higher than random. Considered together, our results support the hypothesis that EEG patterns reflect individual cognitive states corresponding to mental operations and can be used in classification of different cognitive activity.},
annote = {A classification of spectral patterns of EEG underlies several cognitive neurotechnologies including passive and active brain-computer interfaces. Despite arithmetic tasks often being used in studies of cognitive workload, there is a lack of findings describing a possibility to recognize EEG patterns related to different types of math operations. In the present work, we have shown that the power spectral density of EEG can be used to classify types of mental operations including a classification of verbal and different mathematical tasks for simple arithmetic operations or logical tasks with arithmetic progressions. The verbal tasks were separated from arithmetic ones significantly better than arithmetic from logical tasks, and verbal from logical tasks. Better discrimination of verbal tasks from arithmetic but not from logical tasks supports the hypothesis of unique EEG patterns associated with verbal activity that apparently differ from mental operations in arithmetic. Additionally, we compared the behavioral performance in problem solving and accuracy of EEG classification in two groups of subjects with education in math or humanities (N=8+8). We obtained the predicted differences related to better performance of the math group in solving math tasks than the humanitarian group. However, the classification accuracy of tasks based on EEG did not differ significantly between groups and was essentially higher than random. Considered together, our results support the hypothesis that EEG patterns reflect individual cognitive states corresponding to mental operations and can be used in classification of different cognitive activity.}
}