International Symposium on Neural Networks

ISNN 2016: Advances in Neural Networks – ISNN 2016 pp 66-73

A Two-Stage Channel Selection Model for Classifying EEG Activities of Young Adults with Internet Addiction

  • Wenjie Li
  • Ling Zou
  • Tiantong Zhou
  • Changming Wang
  • Jiongru Zhou
Conference paper

DOI: 10.1007/978-3-319-40663-3_8

Volume 9719 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Li W., Zou L., Zhou T., Wang C., Zhou J. (2016) A Two-Stage Channel Selection Model for Classifying EEG Activities of Young Adults with Internet Addiction. In: Cheng L., Liu Q., Ronzhin A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science, vol 9719. Springer, Cham

Abstract

Full scalp electroencephalography (EEG) recording is generally used in brain computer interface (BCI) applications with multi-channel electrode cap. The data not only has comprehensive information about the application, but also has irrelevant information and noise which makes it difficult to reveal the patterns. This paper presents our preliminary research in selecting the optimal channels for the study of internet addiction with visual “Oddball” paradigm. A two-stage model was employed to select the most relevant channels about the task from the full set of 64 channels. First, channels were ranked according to power spectrum density (PSD) and Fisher ratio separately for each subject. Second, the occurrence rate of each channel among different subjects was computed. Channels whose occurrences was more than twice consisted the optimal combination. The optimal channels and other comparison combinations of channels (including the whole channels) were used to distinguish between the target and non-target stimuli with Fisher linear discriminant analysis method. Classification results showed that the channel selection method greatly reduced the abundant channels and guaranteed the classification accuracy, specificity and sensitivity. It can be concluded from the results that there is attention deficit on internet addicts.

Keywords

Channel selection Electroencephalogram (EEG) Internet addiction Oddball Power spectrum density Fisher linear discriminant analysis 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Wenjie Li
    • 1
    • 2
  • Ling Zou
    • 1
    • 2
  • Tiantong Zhou
    • 1
    • 2
  • Changming Wang
    • 3
    • 4
  • Jiongru Zhou
    • 1
    • 2
  1. 1.School of Information Science and EngineeringChangzhou UniversityChangzhouChina
  2. 2.Changzhou Key Laboratory of Biomedical Information TechnologyChangzhouChina
  3. 3.Beijing Anding HospitalCapital Medical UniversityBeijingChina
  4. 4.Beijing Institute for Brain DisordersBeijingChina