Spatial Cognition Degree of Development Classification Using Largest Lyapunov Exponents

The goal in the present paper is to propose, develop and show a method for classifying the spatial cognition degree of development on different individuals. Thirty-Seven undergraduate students had their electroencephalogram (EEG) recorded while engaged in 3-D images mental rotation tasks. Their spatial cognition degree of development was assessed using a BPR-5 psychological test. The Largest Lyapunov Exponent (LLE) was calculated from each of the 8 electrodes recorded. The LLEs were used as input for three different classifiers: i) multi-layer perceptron artificial neural network, ii) support vector machines, and iii) K-Nearest Neighbors. The best result was achieved by the MLP using the tansig transfer function. The proposed classification method can contribute and interact with other processes in the analysis and study of human spatial cognition, as in the understanding of the human intelligence at all. A better understanding and evaluation of the cognitive capabilities of an individual could suggest him elements of motivation, ease or natural inclinations, possibly affecting the decisions of his carrier positively.