IEEE Computational Intelligence Magazine - May 2021 - 31

Multi-Scale
Neural
Network for
EEG Representation
Learning in BCI
Abstract-Recent advances in deep learning have had a methodological and practical impact on brain-computer interface
(BCI) research. Among the various deep network architectures,
convolutional neural networks (CNNs) have been well suited
for spatio-spectral-temporal electroencephalogram (EEG) signal
representation learning. Most of the existing CNN-based
methods described in the literature extract features at a sequential level of abstraction with repetitive nonlinear operations and
involve densely connected layers for classification. However,
studies in neurophysiology have revealed that EEG signals

Digital Object Identifier 10.1109/MCI.2021.3061875
Date of current version: 12 April 2021

1556-603X/21©2021IEEE	

©SHUTTERSTOCK.COM/METAMORWORKS

Wonjun Ko, Eunjin Jeon,
Seungwoo Jeong, and
Heung-Il Suk
Korea University, KOREA

carry information in different ranges of frequency components.
To better reflect these multi-frequency properties in EEGs, we
propose a novel deep multi-scale neural network that discovers
feature representations in multiple frequency/time ranges and
extracts relationships among electrodes, i.e., spatial representations, for subject intention/condition identification. Furthermore, by completely representing EEG signals with
spatio-spectral-temporal information, the proposed method
can be utilized for diverse paradigms in both active and passive
BCIs, contrary to existing methods that are primarily focused
on single-paradigm BCIs. To demonstrate the validity of our
Corresponding Author: Heung-Il Suk (hisuk@korea.ac.kr).

MAY 2021 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE

31



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IEEE Computational Intelligence Magazine - May 2021

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