EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate

Clinical Neurophysiology

Volumn 119, Issue 7, 2008, Pages 1524-1533

  Shen, Kai Quan ^a   Li, Xiao Ping ^a   Ong, Chong Jin ^a   Shao, Shi Yun ^a   Wilder Smith, Einar P V ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Automatic detection; Classification; Electroencephalogram (EEG); Mental fatigue; Support vector machines (SVM)

Indexed keywords

ACCIDENT; ACCURACY; ALERTNESS; ARTICLE; AUTOMATION; DYSTHYMIA; ELECTROENCEPHALOGRAM; ELECTROENCEPHALOGRAPHY; HUMAN; HUMAN EXPERIMENT; MEASUREMENT; NORMAL HUMAN; PRIORITY JOURNAL; PROBABILITY; SLEEP DEPRIVATION; SUPPORT VECTOR MACHINE; TASK PERFORMANCE; TRAFFIC SAFETY; VOLUNTEER;

ALGORITHMS; AROUSAL; AUDITORY PERCEPTION; CONFIDENCE INTERVALS; DATA INTERPRETATION, STATISTICAL; ELECTROENCEPHALOGRAPHY; HUMANS; MENTAL FATIGUE; MODELS, STATISTICAL; SLEEP DEPRIVATION; SOFTWARE;

EID: 52449135541     PISSN: 13882457     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.clinph.2008.03.012     Document Type: Article

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