@inproceedings{72c3372636664a03a8a0ebf9a5af7fc0,
title = "Deep feature normalization using rest state EEG signals for Brain-Computer Interface",
abstract = "The brain-computer interface (BCI) system provides information exchanges between neural signals containing the user's intention and device control signals. Electroencephalogram (EEG) is a widely used signal for obtaining neural signals. In EEG decoding, EEG variability across different subjects critically degrades deep learning performance. In this paper, we propose a feature normalization method for reducing EEG variability with rest state EEG signals. The decoding structure is trained with a normalized feature which is normalized by subtracting the normalization feature extracted from the normalization structure. Experimental results show that the deep feature normalization algorithm dramatically enhances the performance of conventional deep learning algorithms.",
keywords = "Brain-computer interface (BCI), Deep learning, Electroencephalogram (EEG), Motor imagery",
author = "Youngchul Kwak and Song, \{Woo Jin\} and Kim, \{Seong Eun\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Electronics, Information, and Communication, ICEIC 2021 ; Conference date: 31-01-2021 Through 03-02-2021",
year = "2021",
month = jan,
day = "31",
doi = "10.1109/ICEIC51217.2021.9369712",
language = "English",
series = "2021 International Conference on Electronics, Information, and Communication, ICEIC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 International Conference on Electronics, Information, and Communication, ICEIC 2021",
}