Classification of Kolb's learning styles using EEG sub-band spectral centroid frequencies and artificial neural network

Abstract

Kolb's experiential learning theory has outlined that individuals possess unique learning preferences that comprise of diverging, converging, assimilating and accommodating styles. Conventional approach to assess the learning styles however is susceptible to reliability issues that arise from cultural and language variations. To overcome such limitation, a new learning style assessment technique is proposed using EEG sub-band spectral centroid frequencies and artificial neural network. Sixty eight participants have volunteered in for the study. Subjects are clustered into the respective learning style groups using Kolb's learning style inventory. Subsequently, resting EEG is recorded from the antero-frontal cortex and pre-processed for noise elimination. Alpha and theta spectral centroid frequencies are then extracted and analyzed. Dataset enrichment is then performed using synthetic EEG. In general, the artificial neural network is successful in classifying learning styles from the resting EEG. Network training and testing have attained 85.1 and 91.3% accuracies, respectively. Albeit yielding satisfactory performance, findings have also suggested an extended research to enhance its capabilities for learning style discrimination.