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FEATURE EXTRACTION WITH DEEP BELIEF NETWORKS FOR DRIVER'S COGNITIVE STATES PREDICTION FROM EEG DATA
Mehdi Hajinoroozi
, Tzyy Ping Jung
, Chin-Teng Lin
, Yufei Huang
電機工程學系
光電系統研究所
研究成果
:
Paper
›
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46
引文 斯高帕斯（Scopus）
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Keyphrases
Feature Extraction
100%
Electroencephalography
100%
Deep Belief Network
100%
Cognitive States Prediction
100%
Discriminant Features
33%
Cognitive State
33%
Prediction Accuracy
16%
Dimensionality Reduction
16%
Classification Performance
16%
Brain-computer Interface
16%
International Cooperative Ataxia Rating Scale (ICARS)
16%
Time-frequency
16%
Spatial Correlation
16%
High Dimension
16%
Feature Dimension
16%
Effective Feature
16%
Electroencephalographic Features
16%
Computer Science
Feature Extraction
100%
Experimental Result
50%
Classification Performance
50%
Computer Interface
50%
Independent Component Analysis
50%
Frequency Component
50%
Feature Dimension
50%
Spatial Correlation
50%
High Dimensionality
50%
Engineering
Feature Extraction
100%
Deep Belief Network
100%
Experimental Result
16%
Independent Component Analysis
16%
Brain-Computer Interface
16%
Classification Performance
16%
Frequency Component
16%
Accurate Prediction
16%
Dimensionality
16%
Spatial Correlation
16%
Effective Feature
16%
Neuroscience
Cognitive State
100%
Independent Component Analysis
33%
Brain-Computer Interface
33%