Single trial EEG patterns for the prediction of individual differences in fluid intelligence

Emad ul Haq Qazi*, Muhammad Hussain, Hatim Aboalsamh, Aamir Saeed Malik, Hafeez Ullahamin, Saeed Bamatraf

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

8 Citations (Scopus)


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.

Original languageEnglish
Article number687
Pages (from-to)1-19
Number of pages19
JournalFrontiers in Human Neuroscience
Publication statusPublished - 20 Jan 2017


  • Cognitive task
  • Discrete wavelet transform (DWT)
  • Electroencephalography (EEG)
  • Fluid intelligence
  • Machine learning classifier


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