Study of inter-session variability of long term memory and complexity of EEG signals

Somsirsa Chatterjee, Saugat Bhattacharyya, Anwesha Khasnobish, Amit Konar, D. N. Tibarewala, R. Janarthanan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hurst exponent is used to evaluate the presence or absence of long-range dependence and its degree in a time series, and hence is known as the long term memory of the time series. Fractal Dimension on the other hand is a measure of data complexity. Hurst Exponent and Fractal Dimension were used as features for nonlinear classification by QDA and SVM with a polynomial kernel of order 3. Since both Hurst Exponent and Fractal Dimension has a large inter individual variability, we used these features of consecutive sessions to study the intersession variability of classification accuracy of the proposed classifiers. QDA provided better classification for the trials trained by motor execution, while SVM with the polynomial kernel differentiated better when the training was done by motor imagery data.

Original languageEnglish
Title of host publicationProceedings - 2012 3rd International Conference on Emerging Applications of Information Technology, EAIT 2012
Pages106-109
Number of pages4
DOIs
Publication statusPublished - 11 Jan 2013
Event2012 3rd International Conference on Emerging Applications of Information Technology, EAIT 2012 - Kolkata, India
Duration: 30 Nov 20121 Dec 2012

Publication series

NameProceedings - 2012 3rd International Conference on Emerging Applications of Information Technology, EAIT 2012

Conference

Conference2012 3rd International Conference on Emerging Applications of Information Technology, EAIT 2012
CountryIndia
CityKolkata
Period30/11/121/12/12

Keywords

  • Fractal Dimension
  • Hurst Exponent
  • Intersession variability
  • polynomial kernel
  • QDA
  • SVM

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    Chatterjee, S., Bhattacharyya, S., Khasnobish, A., Konar, A., Tibarewala, D. N., & Janarthanan, R. (2013). Study of inter-session variability of long term memory and complexity of EEG signals. In Proceedings - 2012 3rd International Conference on Emerging Applications of Information Technology, EAIT 2012 (pp. 106-109). [6407873] (Proceedings - 2012 3rd International Conference on Emerging Applications of Information Technology, EAIT 2012). https://doi.org/10.1109/EAIT.2012.6407873