Abstract
With deep learning emerging as a powerful machine learning tool to build Brain Computer Interface (BCI) systems, researchers are investigating the use of different type of networks architectures and representations of brain activity to attain superior classification accuracy compared to state-of-the-art machine learning approaches, that rely on processed signal and optimally extracted features. This paper presents a deep learning driven electroencephalography (EEG)-BCI system to perform decoding of hand motor imagery using deep convolution neural network architecture, with spectrally localized time-domain representation of multi-channel EEG as input. A significant increase in decoding performance in terms of accuracy of +6.47% is obtained compared to a wideband EEG representation. We further illustrate the movement class specific feature patterns for both the architectures and demonstrate that higher difference between classes is observed using the proposed architecture. We conclude that the network trained by taking into account the dynamic spatial interactions in distinct frequency bands of EEG, can offer better decoding performance and aid in better interpretation of learned features.
Original language | English |
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Title of host publication | 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1322-1326 |
Number of pages | 5 |
ISBN (Electronic) | 9781728145693 |
DOIs | |
Publication status | Published - 2019 Oct |
Event | 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy Duration: 2019 Oct 6 → 2019 Oct 9 |
Publication series
Name | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
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Volume | 2019-October |
ISSN (Print) | 1062-922X |
Conference
Conference | 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 |
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Country/Territory | Italy |
City | Bari |
Period | 19/10/6 → 19/10/9 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT This work was supported by the Agency for Science, Technology and Research, Singapore (No. IAF311022), and the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451).
Publisher Copyright:
© 2019 IEEE.
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction