标题:Development of a Real-Time Motor-Imagery-Based EEG Brain-Machine Interface
作者:Gorjup, Gal ;, Rok ;Stoyanov, Stoyan Petrov ;stergaard ;Manoonpong, Poramate
通讯作者:Gorjup, Gal
作者机构:[Gorjup, Gal ;Gorjup, Gal ;, Rok ;Stoyanov, Stoyan Petrov ] Faculty of Mechanical Engineering, University of Ljubljana, Ljubljana, Slovenia;[stergaard 更多
会议名称:25th International Conference on Neural Information Processing, ICONIP 2018
会议日期:December 13, 2018 - December 16, 2018
来源:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年:2018
卷:11307 LNCS
页码:610-622
DOI:10.1007/978-3-030-04239-4_55
摘要:EEG-based brain-machine interfaces offer an alternative means of interaction with the environment relying solely on interpreting brain activity. They can not only significantly improve the life quality of people with neuromuscular disabilities, but also present a wide range of opportunities for industrial and commercial applications. This work focuses on the development of a real-time brain-machine interface based on processing and classification of motor imagery EEG signals. The goal was to develop a fast and reliable system that can function in everyday noisy environments. To achieve this, various filtering, feature extraction, and classification methods were tested on three data sets, two of which were recorded in a noisy public setting. Results suggested that the tested linear classifier, paired with band power features, offers higher robustness and similar prediction accuracy, compared to a non-linear classifier based on recurrent neural networks. The final configuration was also successfully tested on a real-time system.
© 2018, Springer Nature Switzerland AG.
收录类别:EI
资源类型:会议论文
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