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Single Trial EEG Classification of Tasks with Dominance of Mental and Sensory Attention with Deep Learning Approachстатья Исследовательская статья
Информация о цитировании статьи получена из
Scopus
Дата последнего поиска статьи во внешних источниках: 25 апреля 2019 г.
- Авторы: Irina Knyazeva, Alexander Efitorov, Yulia Boytsova, Sergey Danko, Vladimir Shiroky, Nikolay Makarenko
- Сборник: Advances in Neural Computation, Machine Learning, and Cognitive Research II
- Серия: Studies in Computational Intelligence
- Том: 799
- Год издания: 2019
- Первая страница: 190
- Последняя страница: 195
- DOI: 10.1007/978-3-030-01328-8_21
- Аннотация: In this paper, we present classification algorithms based on single-trial ElectroEncephaloGraphy (EEG) during the performance of tasks with the dominance of mental and sensory attention. Statistical data analysis showed numerous significant differences of EEG wavelet spectra density during this task at the group level. We decided to use wavelet power spectral density (PSD) computed in each channel for single trial as the source of feature extraction for the classification task. To obtain a low-dimensional representation of PSD image convolutional autoencoder (CNN) was trained. With this encoded representation binary classification for each subject with multilayer perceptron (MLP) were performed. The classification error varies depending on the subject with the average true classification rate is 83.4%, and the standard deviation is 6.6%. So this approach potentially could be used in the tasks where pattern classification is used, such as a clinical decision or in Brain-Computer Interface (BCI) system.
- Добавил в систему: Ефиторов Александр Олегович