跳至主導覽
跳至搜尋
跳過主要內容
國立陽明交通大學研發優勢分析平台 首頁
English
中文
在 國立陽明交通大學研發優勢分析平台 搜尋內容
首頁
人員
單位
研究成果
計畫
獎項
活動
貴重儀器
影響
Spatial Component-wise Convolutional Network (SCCNet) for Motor-Imagery EEG Classification
Chun-Shu Wei
, Toshiaki Koike-Akino
, Ye Wang
資訊工程學系
研究成果
:
Conference contribution
›
同行評審
48
引文 斯高帕斯（Scopus）
總覽
指紋
指紋
深入研究「Spatial Component-wise Convolutional Network (SCCNet) for Motor-Imagery EEG Classification」主題。共同形成了獨特的指紋。
排序方式
重量
按字母排序
Keyphrases
Component-wise
100%
Spatial Component
100%
Convolutional Networks
100%
MI-EEG
100%
Electroencephalography
66%
Signal-to-noise
33%
Classification Accuracy
33%
Brain-computer Interface
33%
Noise Reduction
33%
Convolutional Neural Network
33%
Spatial Filtering
33%
Motor Imagery
33%
Electroencephalography Data
33%
Signal Enhancement
33%
Convolutional Layer
33%
Motor Imagery Brain-computer Interface
33%
Art Performance
33%
Electroencephalography Analysis
33%
Motor Imagery Classification
33%
Neuromonitoring
33%
Computer Science
Convolutional Neural Network
100%
Convolutional Network
100%
Computer Interface
66%
Classification Accuracy
33%
Signal Enhancement
33%
Convolutional Layer
33%
Art Performance
33%
Engineering
Motor Imagery
100%
Convolutional Neural Network
60%
Brain-Computer Interface
40%
Filtration
20%
Classification Accuracy
20%
Convolutional Layer
20%
Medicine and Dentistry
Motor Imagery
100%
Brain-Computer Interface
40%
Neuromonitoring
20%
Noise Reduction
20%
Neuroscience
Brain-Computer Interface
100%
Neural Network
50%
Biochemistry, Genetics and Molecular Biology
Brain Computer Interface
100%
Filtration
50%