Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification
PBN-AR
Instytucja
Wydział Fizyki Technicznej, Informatyki i Matematyki Stosowanej (Politechnika Łódzka)
Informacje podstawowe
Główny język publikacji
en
Czasopismo
SENSORS (30pkt w roku publikacji)
ISSN
1424-8220
EISSN
Wydawca
MDPI AG
DOI
URL
Rok publikacji
2018
Numer zeszytu
10
Strony od-do
3451
Numer tomu
18
Identyfikator DOI
Liczba arkuszy
Słowa kluczowe
en
mental task classification; EEG; CNN; BCI
Streszczenia
Język
en
Treść
Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single-or multi-channel networks, such as AlexNet, VGG-16 and Cecotti's multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.
Cechy publikacji
discipline:Informatyka – dziedzina nauk matematycznych
discipline:Informatyka – dziedzina nauk technicznych
discipline:Computer science – field of mathematics
discipline:Computer science – field of technical sciences
Original article
Original article presents the results of original research or experiment.
Oryginalny artykuł naukowy
Oryginalny artykuł naukowy przedstawia rezultaty oryginalnych badań naukowych lub eksperymentu.
Inne
System-identifier
PBN-R:882629
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