Comput Methods Programs Biomed - Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network.

Tópicos

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Resumo

This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.

Resumo Limpo

paper elabor novel intellig assess method use brainwav subband power ratio featur studi focus left hemispher brainwav relax state distinct intellig quotient group establish earlier score raven progress matric subband power ratio calcul energi spectral densiti theta alpha beta frequenc band synthet data generat increas dataset featur use input artifici neural network subsequ brain behaviour model develop use artifici neural network train optim learn rate momentum constant hidden node find indic distinct intellig quotient group can classifi brainwav subband power ratio train test accuraci

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