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Интеллектуальная Система Тематического Исследования НАукометрических данных |
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A nonparametric method for the segmentation of the EEGстатья
Информация о цитировании статьи получена из
Web of Science,
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 5 июня 2019 г.
- Авторы: Brodsky B.E., Darkhovsky B.S., Kaplan A.Ya, Shishkin S.L.
- Журнал: Computer Methods and Programs in Biomedicine
- Том: 60
- Номер: 2
- Год издания: 1999
- Издательство: Elsevier BV
- Местоположение издательства: Netherlands
- Первая страница: 93
- Последняя страница: 106
- DOI: 10.1016/S0169-2607(98)00079-0
- Аннотация: A new method for segmentation of the EEG, based on a nonparametric statistical analysis, is proposed. A nonparametric approach was chosen because it minimises the need for a priori information about a signal. The method provides detection of change-points (quasi-stationary segments’ boundaries) in almost any EEG characteristic for a given level of false alarm probability. The method was applied to 8-channels spontaneous EEG recordings obtained from 12 subjects in eyes closed and eyes open conditions to detect rapid fluctuations of the alpha rhythm power. After preliminary adjustment of false alarm probability values all the recordings were analysed in unsupervised regime with the same parameters. From 15 to 1 19 change-points were found per minute and EEG channel. Automatically detected change-points were in good correspondence with visual estimation of the instants of change in alpha activity. A new method for segmentation of the EEG, based on a nonparametric statistical analysis, is proposed. A nonparametric approach was chosen because it minimizes the need for a priori information about a signal. The method provides detection of change-points (quasi-stationary segments’ boundaries) in almost any EEG characteristic for a given level of false alarm probability. The method was applied to 8-channels spontaneous EEG recordings obtained from 12 subjects in eyes closed and eyes open conditions to detect rapid fluctuations of the alpha rhythm power. After preliminary adjustment of false alarm probability values all the recordings were analyzed in unsupervised regime with the same parameters. From 15 to 1 19 change-points were found per minute and EEG channel. Automatically detected change-points were in good correspondence with visual estimation of the instants of change in alpha activity.
- Добавил в систему: Шишкин Сергей Львович