Evaluation of brainwave sub-band spectral centroid in human intelligence

Sub-band spectral centroid (SC) has been widely applied in audio and speech processing field. This paper highlights the SC feature as a new approach to evaluate human intelligence quotient (IQ). The study focuses on resting EEG of the left brain hemisphere. The SC feature is derived from Discrete Fourier Transform (DFT) of electroencephalogram (EEG) signals. Sub-band SCs are obtained for delta, theta, alpha and beta frequency bands. IQ scores from the Raven Progressive Matrices (RPM) have been utilized to categorize dataset into three distinct groups. The SC features are then evaluated for significant pattern among the different intelligence levels. Results on theta and beta sub-bands indicate a trending pattern. Hence by implementing SC features of the theta and beta sub-bands, distinct IQ groups can be recognized.