Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144956
Title: Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI
Authors: Fahimi, Fatemeh
Zhang, Zhuo
Goh, Wooi Boon
Lee, Tih-Shi
Ang, Kai Keng
Guan, Cuntai
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Fahimi, F., Zhang, Z., Goh, W. B., Lee, T.-S., Ang, K. K., & Guan, C. (2019). Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI. Journal of Neural Engineering, 16(2), 026007-. doi:10.1088/1741-2552/aaf3f6
Journal: Journal of neural engineering
Abstract: Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In this paper, we propose a framework based on the deep convolutional neural network (CNN) to detect the attentive mental state from single-channel raw electroencephalography (EEG) data.
URI: https://hdl.handle.net/10356/144956
ISSN: 1741-2560
DOI: 10.1088/1741-2552/aaf3f6
Rights: © 2019 IOP Publishing Ltd. All rights reserved. This is an author-created, un-copyedited version of an article accepted for publication in Journal of neural engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at https://doi.org/10.1088/1741-2552/aaf3f6
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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