Reducing Electrodes based on Decision Tree Classification for EEG Motor Movement Data

  • Authors

    • Jayesh Deep Dubey
    • Deepak Arora
    • Pooja Khanna
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16102
  • BCI, EEG, Annotations, EDF Browser, Physionet, Random Forest

  • Abstract

    Analysis of EEG data is one of the most important parts of Brain Computer Interface systems because EEG data consists of a substantial amount of crucial information that can be used for better study and improvements in BCI system. One of the problems with the analysis of EEG is the large amount of data that is produced, some of which might not be useful for the analysis. Therefore identifying the relevant data from the large amount of EEG data is important for better analysis. The objective of this study is to find out the performance of Random Forest classifier on the motor movement EEG data and reducing the number of electrodes that are considered in the EEG recording and analysis so that the amount of data that is produced through EEG recording is reduced and only relevant electrodes are considered in the analysis. The dataset used in the study is Physionet motor movement/imagery data which consists of EEG recordings obtained using 64 electrodes. These 64 electrodes were ranked based on their information gain with respect to the class using Info Gain attribute selection algorithm. The electrodes were then divided into 4 lists. List 1 consists of top 18 ranked electrodes and number of electrodes was increased by 15 [in ranked order] in each subsequent list. List 2, 3 and 4 consists of top 33, 48 and 64 electrodes respectively. The accuracy of random forest classifier for each of the list was compared with the accuracy of the classifier for the List 4 which consists of all the 64 electrodes. The additional electrodes in the List 4 were rejected because the accuracy of the classifier was almost same for List 4 and List3. Through this method we were able to reduce the electrodes from 64 to 48 with an average decrease of only 0.9% in the accuracy of the classifier. This reduction in the electrode can substantially reduce the time and effort required for analysis of EEG data.

         

     

  • References

    1. [1] Vidal, J. J. "Real-time detection of brain events in EEG." Proceedings of the IEEE, Vol. 65, Iss. 5, pp. 633-641. (1977).

      [2] R. P. N. Rao and R. Scherer, "Brain-Computer Interfacing [In the Spotlight]," in IEEE Signal Processing Magazine, vol. 27, no. 4, pp. 152-150, July 2010. doi: 10.1109/MSP.20 10.936774 URL: http://i eeexplore.ieee .org/stamp/s tamp. jsp?tp=& arnumber=54 84181&isnu mber= 5 484155

      [3] Brain Computer Interface Technology: A review of the first international meeting by Jonathon R. Wolpaw, Niels Birbaumer, William J Heetderks, Dennis J.McFarland, P. Hunter Peckhalm, Gerwin Schalk, Emanuel Donchin, Louis A.Quatrano, Charles J. Robinson, Theresa M. Vaughan

      [4] Darius A. Rohani, William S. Henning, Carsten E. Thomsen, Troels W.Kjaer, S. Puthusserypady, Helge B.D. “Sorensen BCI using imaginary movements: The simulator,†Computer Methods and Programs in Biomedicine, vol. 111, pp. 300-307, August 2013

      [5] Thomas A. Deuel, Juan Pampin, Jacob Sundstrom, Felix Darvas. The Encephalophone: A Novel Musical Biofeedback Device using Conscious Control of Electroencephalogram (EEG). Frontiers in Human Neuroscience, 2017; 11OI: 10.3389/fnhum.2017.00213

      [6] F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi. A review of classification algorithms for EEGbased brain-computer interfaces. Journalof Neural Engineering, 4:R1–R13, 2007

      [7] Ali Bashashati, Mehrdad Fatourechi, Rabab K Ward and Gary E Birch. A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals: J. Neural Eng. 4 (2007) R32– R57.

      [8] C. Guger, H. Ramoser, and G. Pfurtscheller. Real-Time EEG Analysis with Subject-Specific Spatial Patterns for a Brain–Computer Interface (BCI): IEEE TRANSACTIONS ON REHABILITATION ENGINEERING, VOL. 8, NO. 4, DECEMBER 2000

      [9] YI Fang, LI Hao and JIN Xiaojie. Improved Classification Methods for Brain Computer Interface System: I. J. Computer Network and Information Security, 2012, 2, 15-21.Published Online March 2012 in MECS (http://www.mecs-press.org/).

      [10] GertPfurtscheller et al. “Current trends in Graz braincomputer interface BCI researchâ€. IEEE Transactions on Rehabilitation Engineering, 8(2):216–219, 2000

      [11] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.

      [12] Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R. BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE Transactions on Biomedical Engineering 51(6):1034-1043, 2004.

      [13] Brieman L, Random Forests, (2001): Machine Learning, 45, 5-32.

  • Downloads

  • How to Cite

    Deep Dubey, J., Arora, D., & Khanna, P. (2018). Reducing Electrodes based on Decision Tree Classification for EEG Motor Movement Data. International Journal of Engineering & Technology, 7(3.12), 344-347. https://doi.org/10.14419/ijet.v7i3.12.16102

    Received date: 2018-07-23

    Accepted date: 2018-07-23

    Published date: 2018-07-20