Classification of students’ IQ level using EEG-based intelligence classifier model

Abstract

Intelligence quotient (IQ) is used to measure the composite mental constituents of cognitive performance. Conventional methods for An alternative solution to intelligence classifier model participated in the pilot study. signal is initially pre-processed for noise removal. fed to the previously established classification model, enabling it to sample. Majority of the samples alpha and beta ratio have provide Hypothesis of intelligence and alpha suppression th Intelligence quotient (IQ) is used to measure the composite mental constituents of cognitive methods for measuring IQ however are exposed to biasness issues. to classify IQ levels via electroencephalogram (EEG) based model is proposed. 50 students from foundation program have study. Resting EEG is recorded from the left prefrontal cortex. The ed for noise removal. Next, the extracted power ratio features are fed to the previously established classification model, enabling it to classify sample. Majority of the samples are classified as high IQ. Nevertheless, the pattern of t provided means of output validation via the Neural Efficiency Hypothesis of intelligence and alpha suppression theory. Intelligence quotient (IQ) is used to measure the composite mental constituents of cognitive exposed to biasness issues. electroencephalogram (EEG) based . 50 students from foundation program have from the left prefrontal cortex. The power ratio features are ify IQ level for each , the pattern of theta, means of output validation via the Neural Efficiency Research Article Special Issue M. M. Anoor et al.