Spatial Cognition Degree of Development Classification Using Artificial Neural Networks and Largest Lyapunov Exponents

Abstract

Thirty-Seven undergraduate students (23 engineering students, 14 social and human science students) had their electroencephalogram (EEG) recorded during the performing of mental rotation and recognition of virtual tridimensional geometric patterns tasks. Their spatial cognition degree of development was assessed by a BPR-5 psychological test. The Largest Lyapunov Exponent (LLE) was calculated from each of the 8 EEG channels recorded: FP1, FP2, F3, F4, T3, T4, P3, and P4. The LLEs were used as inputs for 3 different artificial neural networks topologies: i) multilayer perceptron, ii) radial base function, and iii) voted perceptron. Then the best results obtained using each topology is compared with the results obtained using the other topologies.