A CNN-based Approach for three-class classification of motor imagery EEG data including ‘rest state’ in hybrid multi-user BCI
Artykuł naukowy w wydawnictwie zbiorowym recenzowanyNazwa konferencji: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Współautorzy: Jianhai Zhang, Chongwei Su, Li Zhu, Gaochao Cui, Wanzeng Kong
Miejsce: San Diego
Rok wydania: 2020
Tytuł publikacji: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Redaktorzy: Illhoi Yoo, Jinbo Bi, Xiaohua Hu
Strony od-do: 770-773
Streszczenie: Multi-user BCI (brain computer interface) refers to a kind of BCI in which there are several users participating in one task. Differently from P300 and SSVEP (steady-state visual evoked potential), MI (motor imagery)-BCI does not rely on external stimulus, which is more widely used in the assistance of disabled people. The main problem of MI-BCI is to achieve asynchronous control, which needs to improve the rest state recognition accuracy. We proposed a CNN-based approach for three-class classification of motor imagery EEG data including rest state in hybrid multi-user BCI. Firstly, we designed a two-user hybrid MI-BCI experimental paradigm and moreover proposed a CNN-based processing framework method which contains several strategies for inter-brain phase locked value (PLV) features fusion. Results show that the alpha band shows the significantly better performance than other bands and the classification performance of multi-user MI is better than single-user MI. Multi-user BCI is a potential way to enhance the performance of asynchronous MI-BCI.
Słowa kluczowe: EEG, wyobraźnia ruchowa, CNN, wewnątrzmózgowe PLV
Dostęp WWW: https://ieeexplore.ieee.org/abstract/document/8983380
DOI: 10.1109/BIBM47256.2019.8983380
Cytowanie w formacie Bibtex:
@article{1,
author = "Dariusz Zapała and Jianhai Zhang and Chongwei Su and Li Zhu and Gaochao Cui and Wanzeng Kong",
title = "A CNN-based Approach for three-class classification of motor imagery EEG data including ‘rest state’ in hybrid multi-user BCI",
journal = "",
year = "2020",
pages = "770-773"
}
Cytowanie w formacie APA:
Zapała, D. and Jianhai Zhang and Chongwei Su and Li Zhu and Gaochao Cui and Wanzeng Kong(2020). A CNN-based Approach for three-class classification of motor imagery EEG data including ‘rest state’ in hybrid multi-user BCI. , 770-773.