Decoding EEG Data with Deep Learning for Intelligence Quotient Assessment

Abstract

Intelligence quotient (IQ) serves as a statistical gauge for evaluating an individual’s cognitive prowess. Measuring IQ is a formidable undertaking, mainly due to the intricate intricacies of the human brain’s composition. Presently, the assessment of human intelligence relies solely on conventional paper-based psychometric tests. However, these approaches suffer from inherent discrepancies arising from the diversity of test formats and language barriers. The primary objective of this study is to introduce an innovative, deep learning-driven methodology for IQ measurement using Electroencephalogram (EEG) signals. In this investigation, EEG signals are captured from participants during an IQ assessment session. Subsequently, participants' IQ levels are categorized into six distinct tiers, encompassing extremely low IQ, borderline IQ, low average IQ, high average IQ, superior IQ, and very superior IQ, based on their test results. An attention mechanism-based Convolution Neural Network-modified tanh Long-Short-term-Memory (CNN-MTLSTM) model has been meticulously devised for adeptly classifying individuals into the aforementioned IQ categories by using EEG signals. A layer named 'input enhancement layer' is proposed and incorporated in CNN-MTLSTM for enhancing its prediction accuracy. Notably, a CNN is harnessed to automate the process of extracting important information from the extracted EEG features. A new model, i.e., MTLSTM, is proposed, which works as a classifier. The paper’s contributions encompass proposing the novel MTLSTM architecture and leveraging attention mechanism to enhance the classification accuracy of the CNN-MTLSTM model. The innovative CNN-MTLSTM model, incorporating an attention mechanism within the MTLSTM network, attains a remarkable average accuracy of 97.41% in assessing a person’s IQ level.