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The Survey of Image Generation from EEG Signals based on De..:

, In: 2021 International Symposium on Biomedical Engineering and Computational Biology,

Yang, Delong ; Su, Dongnan ; Luo, Zhaohui.. - p. 1-5 , 2021

Link: https://dl.acm.org/doi/10.1145/3502060.3502151

RT 
T1 <cTitle>2021 International Symposium on Biomedical Engineering and Computational Biology</cTitle>: T1 <Title>The Survey of Image Generation from EEG Signals based on Deep Learning</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3502151&Exemplar=1&LAN=DE
				
A1 Yang, Delong
A1 Su, Dongnan
A1 Luo, Zhaohui
A1 Shang, Peng
A1 Hu, Zhigang
PB ACM
YR 2021
K1 China has become a high-risk region of stroke. Most patients with stroke suffer regular bouts of post-stroke limb dyskinesia. Nowadays
K1 Electroencephalogram (EEG)
K1 cannot be represented accurately by other algorithms. With the development of deep learning techniques
K1 one of the mainly brain activity recordings
K1 the topic of EEG signals' representation by image generation technique has become an important research area. This paper we introduced the basic concepts of BCI systems first
K1 then we give a survey of image generation techniques from EEG signals. At last
K1 there isn't an effective treatment for these patients. Brain computer interface (BCI) establishes a new pathway to connect human brains and device
K1 we proposed an experimental scheme of dataset establishment which is used for post-stroke patients with upper limb dyskinesia
K1 which provide an innovation method to repair the human brain nervous systems through rehabilitation training. However
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Neural networks
SP 1
OP 5
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3502060.3502151
DO https://dl.acm.org/doi/10.1145/3502060.3502151
SF ELIB - SuUB Bremen

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