Comparative analysis of the computational geometry and neural network classification methods for person identification purposes via the EEG : Part 1

Abstract

In this paper we address the problem of person identification via features extracted from the electroencephalogram (EEG). For this problem, our work investigates appropriate feature extraction and classification methods. Our previous research into feature extraction produced a high number variety of specific EEG features, of varying dimensionalities. However, it is known that traditional classification methods such as neural networks are inefficient at classifying this type of feature. For this reason, in the present paper, we develop a heuristic classifier based on Computational Geometry algorithms. In order to exhibit the potential of the proposed method we compare it with a conventional Radial Basis Function neural classifier using the same (pre-) processing feature extraction methods in both cases.

Keywords:

• EEG
• person identification
• computational geometry algorithms
• classification
• neural networks
• radial basis function (RBF)