Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network

Computer Methods and Programs in Biomedicine

Volumn 114, Issue 1, 2014, Pages 50-59

  Jahidin, A H ^a   Megat Ali, M S A ^a   Taib, M N ^a   Tahir, N Md ^a   Yassin, I M ^a   Lias, S ^a  

^a UNIVERSITI TEKNOLOGI MARA   (Malaysia)

Author keywords

Artificial neural network (ANN); EEG; Intelligence quotient (IQ); Sub band power ratio; White Gaussian noise

Indexed keywords

ELECTROENCEPHALOGRAPHY; FREQUENCY BANDS; SPECTRAL DENSITY;

BEHAVIOUR MODELS; ENERGY SPECTRAL DENSITY; INTELLIGENCE QUOTIENTS; LEFT HEMISPHERE; MOMENTUM CONSTANT; POWER RATIO; TESTING ACCURACY; WHITE GAUSSIAN NOISE; SUBBANDS;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; ELECTROENCEPHALOGRAM; HUMAN; IMAGE DISPLAY; IMAGE PROCESSING; INTELLIGENCE QUOTIENT; LEFT HEMISPHERE; PARAMETERS; SIGNAL NOISE RATIO; SUPPORT VECTOR MACHINE; ADULT; ALPHA RHYTHM; BETA RHYTHM; BRAIN ASYMMETRY; BRAIN BEHAVIOR MODEL; HUMAN EXPERIMENT; INTELLIGENCE; LEARNING; NONBIOLOGICAL MODEL; NORMAL HUMAN; TASK PERFORMANCE; THETA RHYTHM; UNIVERSITY STUDENT;

ARTIFICIAL NEURAL NETWORK (ANN); EEG; INTELLIGENCE QUOTIENT (IQ); SUB-BAND POWER RATIO; WHITE GAUSSIAN NOISE;

BRAIN; HUMANS; INTELLIGENCE; NEURAL NETWORKS (COMPUTER); SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84895420930     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2014.01.016     Document Type: Article

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