Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence.
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.
Visual Computing Lab, Department of Computer Science, College of Computer and Information Sciences, King Saud University Riyadh, Saudi Arabia.
Emad-Ul-Haq
EU
Qazi
Visual Computing Lab, Department of Computer Science, College of Computer and Information Sciences, King Saud University Riyadh, Saudi Arabia.
Muhammad
M
Hussain
Visual Computing Lab, Department of Computer Science, College of Computer and Information Sciences, King Saud University Riyadh, Saudi Arabia.
Hatim
H
Aboalsamh
Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS Seri Iskandar, Malaysia.
Aamir Saeed
AS
Malik
Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS Seri Iskandar, Malaysia.
Hafeez Ullah
HU
Amin
Visual Computing Lab, Department of Computer Science, College of Computer and Information Sciences, King Saud University Riyadh, Saudi Arabia.
Saeed
S
Bamatraf
cognitive task; discrete wavelet transform (DWT); electroencephalography (EEG); fluid intelligence; machine learning classifier