Publication:
Comparison of cross-frequency methods such as cross-term deprived covariance (CTDC) and bispectrum

Дата
2020
Авторы
Skiteva, L.
Ossadtchi, A.
Ushakov, V.
Journal Title
Journal ISSN
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Издатель
Научные группы
Организационные подразделения
Организационная единица
Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
Выпуск журнала
Аннотация
© 2020 The Authors. Published by Elsevier B.V.Cross-frequency coupling (CFC) is typical for the operation of neural networks from different areas of the brain. This, for example, can be characterized by pacemaker neurons activity, the structure of these part's crust, etc. Thus, the highest interest is not the correlation of those areas, but the synchronous activity of the areas in time at different frequencies. Event-related events can induce the work of neurons, but each at its own frequency. It looks like a synchronous manifestation of activity at different points in time, with a lag, but appearing at different frequencies. Correlation methods and coherence for CFC detection are not suitable, since they are for monofrequencies, and a long time series is required. The connectedness of neurons ensemble's work in time is effectively considered by methods such as bispectrum and CTDC. In this work, we compared these two methods as well as their hybrid. According to the results, the CTDC method proved to be more accurate, both in spatial localization and in inter-frequency.
Описание
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Цитирование
Skiteva, L. Comparison of cross-frequency methods such as cross-term deprived covariance (CTDC) and bispectrum / Skiteva, L., Ossadtchi, A., Ushakov, V. // Procedia Computer Science. - 2020. - 169. - P. 881-886. - 10.1016/j.procs.2020.02.148
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