Abstract
Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0–1.875 Hz (delta low) and 1.875–3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.
Highlights
Individual differences are of wide practical importance in educational psychology and provide an opportunity to investigate concepts of cognitive functions (Gray et al, 2003)
We have proposed a system for predicting the fluid intelligence level of subjects, whether he/she belongs to LA and HA group using EEG single trial signals
We employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven’s Advanced Progressive Matrices (RAPM) test
Summary
Individual differences are of wide practical importance in educational psychology and provide an opportunity to investigate concepts of cognitive functions (Gray et al, 2003). Fluid intelligence or general fluid intelligence (gf) is a major measurement of individual differences, which reflects the ability of reasoning and solving novel problems, i.e., tasks that cannot be solved as a function of simple memorization. Various studies on cognitive tasks have linked the fluid intelligence with human learning ability and capacity (Deary et al, 2007; Van den Bos et al, 2012; Wang et al, 2013), which cannot be assessed subjectively i.e., by mere memorization and answering the questions (Primi et al, 2010). The focus of most of the previous research was on the classification of EEG signals based on different cognitive tasks and rest condition tasks, i.e., eyes open and eyes closed (baseline tasks), and no one addressed the problem of assessing the intelligence level of individuals
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