PMID- 33519352
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210202
IS  - 1662-4548 (Print)
IS  - 1662-453X (Electronic)
IS  - 1662-453X (Linking)
VI  - 14
DP  - 2020
TI  - A Dynamic Multi-Scale Network for EEG Signal Classification.
PG  - 578255
LID - 10.3389/fnins.2020.578255 [doi]
LID - 578255
AB  - Accurate and automatic classification of the speech imagery 
      electroencephalography (EEG) signals from a Brain-Computer Interface (BCI) system 
      is highly demanded in clinical diagnosis. The key factor in designing an 
      automatic classification system is to extract essential features from the 
      original input; though many methods have achieved great success in this domain, 
      they may fail to process the multi-scale representations from different receptive 
      fields and thus hinder the model from achieving a higher performance. To address 
      this challenge, in this paper, we propose a novel dynamic multi-scale network to 
      achieve the EEG signal classification. The whole classification network is based 
      on ResNet, and the input signal first encodes the features by the Short-time 
      Fourier Transform (STFT); then, to further improve the multi-scale feature 
      extraction ability, we incorporate a dynamic multi-scale (DMS) layer, which 
      allows the network to learn multi-scale features from different receptive fields 
      at a more granular level. To validate the effectiveness of our designed network, 
      we conduct extensive experiments on public dataset III of BCI competition II, and 
      the experimental results demonstrate that our proposed dynamic multi-scale 
      network could achieve promising classification performance in this task.
CI  - Copyright (c) 2021 Zhang, Luo, Han, Lu, Hua, Chen and Che.
FAU - Zhang, Guokai
AU  - Zhang G
AD  - School of Optical-Electrical and Computer Engineering, University of Shanghai for 
      Science and Technology, Shanghai, China.
FAU - Luo, Jihao
AU  - Luo J
AD  - School of Software Engineering, Tongji University, Shanghai, China.
FAU - Han, Letong
AU  - Han L
AD  - School of Software Engineering, Tongji University, Shanghai, China.
FAU - Lu, Zhuyin
AU  - Lu Z
AD  - School of Software Engineering, Tongji University, Shanghai, China.
FAU - Hua, Rong
AU  - Hua R
AD  - College of Computer Science and Engineering, Shandong University of Science and 
      Technology, Qingdao, China.
FAU - Chen, Jianqing
AU  - Chen J
AD  - Department of Otolaryngology, Head & Neck Surgery, Shanghai Ninth People's 
      Hospital, Affiliated to Shanghai Jiaotong University School of Medicine, 
      Shanghai, China.
FAU - Che, Wenliang
AU  - Che W
AD  - Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University 
      School of Medicine, Shanghai, China.
LA  - eng
PT  - Journal Article
DEP - 20210113
PL  - Switzerland
TA  - Front Neurosci
JT  - Frontiers in neuroscience
JID - 101478481
PMC - PMC7838674
OTO - NOTNLM
OT  - Fourier transform
OT  - brain-computer interface
OT  - dynamic learning
OT  - electroencephalography
OT  - multi-scale
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2021/02/02 06:00
MHDA- 2021/02/02 06:01
PMCR- 2020/01/01
CRDT- 2021/02/01 05:54
PHST- 2020/06/30 00:00 [received]
PHST- 2020/11/09 00:00 [accepted]
PHST- 2021/02/01 05:54 [entrez]
PHST- 2021/02/02 06:00 [pubmed]
PHST- 2021/02/02 06:01 [medline]
PHST- 2020/01/01 00:00 [pmc-release]
AID - 10.3389/fnins.2020.578255 [doi]
PST - epublish
SO  - Front Neurosci. 2021 Jan 13;14:578255. doi: 10.3389/fnins.2020.578255. 
      eCollection 2020.