Visual attention recognition based on nonlinear dynamical parameters of EEG.

Varieties of neurophysiological measures have been utilized in visual attention studies. The linear parameters like power spectrum are the most commonly used features in the existing studies. In this paper, however, nonlinear parameters including approximate entropy, sample entropy and multiscale entropy were tested. All subjects were instructed to perform tasks with three different attention levels (i.e. attention, no attention and rest) in two experiments. Nonlinear features were extracted from the EEG signals. Then, statistical analyses and classification with support vector machine (SVM) were performed. A comparison between the classification results based on the linear feature / and the sample entropy was performed for further analysis. The results suggest that sample entropy stands out in the dynamical parameters with the accuracies of 76.19% and 85.24% in recognition of three levels of attention for the two experiments respectively. And the further comparison shows that the sample entropy performs even better than the / power ratio. It is suggested that nonlinear dynamical parameters may be indispensable for a robust attention recognition system.