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

  • Wenjie Li
  • Ling ZouEmail author
  • Tiantong Zhou
  • Changming Wang
  • Jiongru Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9719)


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.


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



This work has been partially supported by the National Natural Science Foundation of China (61201096, 81501155), Science and Technology Program of Changzhou City (CE20145055) and Qing Lan Project of Jiangsu Province.


  1. 1.
    Kuss, D.J., van Rooij, A.J., Shorter, G.W., Griffiths, M.D., van de Mheen, D.: Internet addiction in adolescents: prevalence and risk factors. Comput. Hum. Behav. 29(5), 1987–1996 (2013)CrossRefGoogle Scholar
  2. 2.
    Kuss, D.J., Griffiths, M.D.: Internet and gaming addiction: a systematic literature review of neuroimaging studies. Brain Sci. 2(3), 347–374 (2012)CrossRefGoogle Scholar
  3. 3.
    Wang, Y.J., Gao, S.K., Gao, X.R.: Common spatial pattern method for channel selection in motor imagery based brain-computer interface. In: Proceedings of IEEE Engineering in Medicine and Biology Society, pp. 5392–5395. IEEE Press, New York (2005)Google Scholar
  4. 4.
    Fattahi, D., Nasihatkon, B., Boostani, R.: A general framework to estimate spatial and spatio-spectral filters for EEG signal classification. Neurocomputing 119(7), 165–174 (2013)CrossRefGoogle Scholar
  5. 5.
    Zou, L., Pu, H., Sun, Q., Su, W.: Analysis of attention deficit hyperactivity disorder and control participants in EEG using ICA and PCA. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds.) ISNN 2012, Part I. LNCS, vol. 7367, pp. 403–410. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Zou, L., Xu, S.K., Ma, Z.H.: Automatic removal of artifacts from attention deficit hyperactivity disorder electroencephalograms based on independent component analysis. Cogn. Comput. 5(2), 225–233 (2013)CrossRefGoogle Scholar
  7. 7.
    Fan, J., Shao, C., Ouyang, Y., Wang, J., Li, S., Wang, Z.-C.: Automatic seizure detection based on support vector machines with genetic algorithms. In: Wang, T.-D., Li, X., Chen, S.-H., Wang, X., Abbass, H.A., Iba, H., Chen, G.-L., Yao, X. (eds.) SEAL 2006. LNCS, vol. 4247, pp. 845–852. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Schröder, M., Lal, T.N., Hinterberger, T., Bogdan, M., Hill, N.J., Birbaumer, N., Rosenstiel, W., Schölkopf, B.: Robust EEG channel selection across subjects for brain-computer interfaces. EURASIP J. Adv. Sig. Process. 2005, 3103–3112 (2005)CrossRefzbMATHGoogle Scholar
  9. 9.
    He, L., Hu, Y.P., Li, Y.Q., Li, D.L.: Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG. Neurocomputing 121(9), 422–433 (2013)Google Scholar
  10. 10.
    Yang, J.H., Singh, H., Hines, E.L., Schlaghecken, F., Iliescu, D.D., Leeson, M.S., Stocks, N.G.: Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artif. Intell. Med. 55(2), 117–126 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Wenjie Li
    • 1
    • 2
  • Ling Zou
    • 1
    • 2
    Email author
  • 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

Personalised recommendations