EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

Scientific World Journal

Volumn 2014, Issue , 2014, Pages

  Jirayucharoensak, Suwicha ^a,b   Pan Ngum, Setha ^a   Israsena, Pasin ^b  

^a CHULALONGKORN UNIVERSITY   (Thailand)

^b NATIONAL ELECTRONICS AND COMPUTER TECHNOLOGY CENTER   (Thailand)

Author keywords

[No Author keywords available]

Indexed keywords

ALPHA RHYTHM; AROUSAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; BETA RHYTHM; CLASSIFICATION ALGORITHM; COVARIATE SHIFT ADAPTATION; DEEP LEARNING NETWORK; ELECTROENCEPHALOGRAM; EMOTION; EMOTION RECOGNITION; GAMMA RHYTHM; LEARNING; LEFT HEMISPHERE; NERVE CELL NETWORK; PRINCIPAL COMPONENT ANALYSIS; RECOGNITION; STACKED AUTOENCODER; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; THETA RHYTHM; VALENCE; ALGORITHM; ELECTROENCEPHALOGRAPHY; HUMAN; PHYSIOLOGY; PROCEDURES; REPRODUCIBILITY; TASK PERFORMANCE;

ALGORITHMS; AROUSAL; ELECTROENCEPHALOGRAPHY; EMOTIONS; HUMANS; NERVE NET; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; SUPPORT VECTOR MACHINES; TASK PERFORMANCE AND ANALYSIS;

EID: 84930963132     PISSN: 23566140     EISSN: 1537744X     Source Type: Journal    
DOI: 10.1155/2014/627892     Document Type: Article

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