title: Visual attention recognition based on nonlinear dynamical parameters of EEG.
abstract: 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.
authors.affiliation: College of Precision Instrument and Optoelectronics Engineering & Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, P.R. China.
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Ke
authors.forename: Long
authors.initials: L
authors.lastname: Chen
authors.forename: Lan
authors.initials: L
authors.lastname: Fu
authors.forename: Yihong
authors.initials: Y
authors.lastname: Jia
authors.forename: Penghai
authors.initials: P
authors.lastname: Li
authors.forename: Xin
authors.initials: X
authors.lastname: Zhao
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Qi
authors.forename: Peng
authors.initials: P
authors.lastname: Zhou
authors.forename: Lixin
authors.initials: L
authors.lastname: Zhang
authors.forename: Baikun
authors.initials: B
authors.lastname: Wan
authors.forename: Dong
authors.initials: D
authors.lastname: Ming
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001288:Attention; D003198:Computer Simulation; D004569:Electroencephalography; D019277:Entropy; D064368:Healthy Volunteers; D006801:Humans; D016014:Linear Models; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D060388:Support Vector Machine; D014785:Vision, Ocular; D055815:Young Adult
keywords: EEG; Visual attention; approximate entropy; multiscale entropy; sample entropy; support vector machine