International Conference on Brain Informatics and Health

BIH 2015: Brain Informatics and Health pp 13-22 | Cite as

Cognitive Task Classificaiton from Wireless EEG

  • Shuvo Kumar Paul
  • M. S. Q. Zulkar Nine
  • Mahady Hasan
  • M. Ashraful Amin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9250)

Abstract

Human brain uses a complex electro-chemical signaling pattern that creates our imagination, memory and self-consciousness. It is said that Electroencephalography better known as EEG contains signatures of various tasks that we perform. In this paper we study the possibility of categorizing tasks conducted by humans from EEG recordings. The novelty of this study mainly lies in the use of very cost effective consumer grade wireless EEG devices. Three cognitive tasks were considered: text reading and writing, Math problem solving and watching videos. Twelve subjects were used in this experiment. Initial features were calculated from Discrete Wavelet Transform (DWT) of raw EEG signals. After application of appropriate dimensionality reduction, Support Vector Machine (SVM) was used for classification of tasks. DWT + Kernel PCA with SVM based classifier showed 86.09 % accuracy.

Keywords

BCI HCI EEG Signal processing DWT 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Obermaier, B., Neuper, C., Guger, C., Pfurtscheller, G.: Information transfer rate in a five-class brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 9(3), 283–288 (2001)CrossRefGoogle Scholar
  2. 2.
    Hsu, W.Y., Lin, C.H., Hsu, H.J., Chen, P.H., Chen, I.R.: Wavelet-based envelope features with automatic EOG artifact removal: Application to single-trial EEG data. Expert Systems with Applications 39(3), 2743–2749 (2012)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Nigam, V.P., Graupe, D.: A neural-network-based detection of epilepsy. Neurological Research 26(1), 55–60 (2004)CrossRefGoogle Scholar
  4. 4.
    Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Computer methods and programs in biomedicine 80(3), 187–194 (2005)CrossRefGoogle Scholar
  5. 5.
    Sadati, N., Mohseni, H.R., Maghsoudi, A.: Epileptic seizure detection using neural fuzzy networks. In: 2006 IEEE International Conference on Fuzzy Systems. IEEE (2006)Google Scholar
  6. 6.
    Subasi, A.: EEG Signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications, pp. 1084–1093 (2007)Google Scholar
  7. 7.
    Polat, K., Güneş, S.: Classification of epileptic form EEG using a hybrid systems based on decision tree classifier and fast Fourier transform. Applied Mathematics and Computation 32(2), 625–631 (2007)Google Scholar
  8. 8.
    Srinivasan, V., Eswaren, C., Sriraam, N.: Approximate entropy-based epileptic EEG detection using artificial neural network. IEEE Transaction on Information Technology in Biomedicine 11(3), 288–295 (2007)CrossRefGoogle Scholar
  9. 9.
    Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 417–441 (1933)Google Scholar
  10. 10.
    Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1299–1319 (1998)Google Scholar
  11. 11.
    Balasubramanian, M., Schwartz, E.L.: The Isomap algorithm and topological stability. Science, 7 (2002)Google Scholar
  12. 12.
    Roweis, S.T., Lawrence, K.S.: Nonlinear dimensionality reduction by Locally Linear Embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  13. 13.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. NIPS 14 (2001)Google Scholar
  14. 14.
    Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Transactions on computers 18(5), 401–409 (1969)CrossRefGoogle Scholar
  15. 15.
    The, Y.W., Roweis, S.T.: Automatic alignment of hidden representations. In: Advances in Neural Information Processing Systems, pp. 841–848 (2002)Google Scholar
  16. 16.
    MindWave Headset. Neurosky Inc., San Jose (2014). http://store.neurosky.com/products/mindwave-1 (accessed 3 Jan 2014)

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shuvo Kumar Paul
    • 1
  • M. S. Q. Zulkar Nine
    • 1
  • Mahady Hasan
    • 1
  • M. Ashraful Amin
    • 1
  1. 1.Computer Vision and Cybernetics Group, CSEIndependent UniversityDhakaBangladesh

Personalised recommendations