Single trial eeg classification of tasks with dominance of mental and sensory attention with deep learning approach

Irina Knyazeva, Alexander Efitorov, Yulia Boytsova, Sergey Danko, Vladimir Shiroky, Nikolay Makarenko

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборникенаучнаярецензирование

Аннотация

© Springer Nature Switzerland AG 2019. 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.
Язык оригиналаанглийский
Название основной публикацииStudies in Computational Intelligence
Страницы190-195
Число страниц6
Том799
DOI
СостояниеОпубликовано - 1 янв 2019

Серия публикаций

НазваниеStudies in Computational Intelligence
ИздательSpringer
ISSN (печатное издание)1860-949X

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    Knyazeva, I., Efitorov, A., Boytsova, Y., Danko, S., Shiroky, V., & Makarenko, N. (2019). Single trial eeg classification of tasks with dominance of mental and sensory attention with deep learning approach. В Studies in Computational Intelligence (Том 799, стр. 190-195). (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-01328-8_21, https://doi.org/10.1007/978-3-030-01328-8_21