Monitoring human information processing via intelligent data analysis of EEG recordings

Article type: Research Article

Authors: Flexer, Arthur^{a; *} | Bauer, Herbert^b

Affiliations: [a] The Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010 Vienna, Austria | [b] Department of Psychology, University of Vienna, Liebiggasse 5, A-1010 Vienna, Austria

Correspondence: [*] Corresponding author. E-mail: arthur@ai.univie.ac.at

Abstract: Human information processing can be monitored by analysing cognitive evoked potentials (EP) measurable in the electro encephalogram (EEG) during cognitive activities. In technical terms, both visualization of multi-dimensional sequential data and unsupervised discovery of patterns within this multivariate set of real valued time series is needed. Our approach towards visualization is to discretize the sequences via vector quantization and to perform a Sammon mapping of the codebook. Instead of having to conduct a time-consuming search for common subsequences in the set of multivariate sequential data, a multiple sequence alignment procedure can be applied to the set of one-dimensional discrete time series. The methods are described in detail and results obtained for spatial and verbal information processing are shown to be statistically valid, to yield an improvement in terms of noise attenuation and to be well in line with psychophysiological literature.

Keywords: sequence alignment, visualization, dimensionality reduction, EEG

DOI: 10.3233/IDA-2000-4203

Journal: Intelligent Data Analysis, vol. 4, no. 2, pp. 113-128, 2000