Total views : 218

Classification of EEG Signals for Prosthetic Limb Movements with ARMA Features Using C4.5 Decision Tree Algorithm

Affiliations

  • Department of Computer Science and Engineering, S. R. M University, Kattankulathur – 603203, Tamil Nadu, India
  • CCIS, AL Yamamah University, Riyadh, Kingdom of Saudi Arabia
  • Department of Computer Applications, Faculty of Science and Humanities, S. R. M University, Kattankulathur – 603203, Tamil Nadu, India
  • School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Chennai-600127, Tamil Nadu,, India

Abstract


Objectives: This paper presented a novel approach with a set of Auto Regressive Moving Average (ARMA) features for the best classification of different hand moments in Electroencephalogram (EEG) signals using C4.5 Decision tree algorithm. Methods/Analysis: The characteristics of EEG signals can be represented through the best features is the most prominent and significant role in the classification systems. The classification is more flawless when the specimen is streamlined through the feature extraction and feature selection process. Findings: In this study, there are four kinds of EEG signals recorded from strong volunteers with finger open, finger close, wrist clockwise and wrist counterclockwise. The well performing statistical features are acquired from the EEG signals. C4.5 Decision tree classifier is used to identify the changes in the EEG signals. The yield of the classifier confirmed that the proposed C4.5 Decision tree classifier has potential to classify the EEG signals of the specific hand movements. Improvement: The proposed work is contributed to manage the right hand movements through the EEG signals. The efficient techniques are required to process the complex EEG signals to achieve the better classification result. To improve the classification accuracy, an efficient feature extraction technique may be applied.

Keywords

ARMA Features, C4.5 Decision Tree, Classification, Electroencephalogram (EEG) Signals.

Full Text:

 |  (PDF views: 276)

References


  • Fadzal FCW, Mansor W, Khuan LY. Analysis of EEG signals from right and left hand writing movements. Institute of Electrical and Electronics Engineers (IEEE) Control and System Graduate Research Colloquium; 2012. p. 354–58.
  • Elif DÜ. Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals. Expert Systems with Applications. 2010 Jan; 37(1):233–39.
  • Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transformation. Journal of Neuro Science Methods. 2003 Feb; 123(1):69–87.
  • Agarwal R, Gotman J, Flanagan D, Rosenblatt B. Automatic EEG analysis during long-term monitoring in ICU.Electroencephalography and Clinical Neurophysiology.1998 Jul; 107(1):44–58.
  • Hazarika N, Chen JZ, Tsoi AC, Sergejew A. Classification of EEG signals using wavelet transform. Signal Processing, Elsevier. 1997 May; 59(1):61–72.
  • Elif DU. Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Computers in Biology and Medicine. 2008 Jan; 38(1):14–22.
  • Ocak H. Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Processing. 2008 Jul; 88(7):1858–67.
  • Ubeyli ED. Statistics over features: EEG signals analysis.Computers in Biology and Medicine. 2009 Aug; 39(1):733– 41.
  • Ubeyli ED, Guler I. Features extracted by eigenvector methods for detecting variability of EEG signals.Pattern Recognition Letters. 2007 Apr; 28(5):592–603.
  • Aishwarya R, Prabhu M, Sumithra G, Anusiya M. Feature extraction for EMG based prosthesis control.ICTACT Journal on Soft Computing. 2013Jan; 3(2):472–7.
  • Dornhege G, Millan JR, Hinterberger T, McFarland DJ, Muller KR. Toward brain - computer interfacing. 1st edition, Massachusetts Institute of Technology (MIT) Press, Massachusetts; 2007.
  • Bhattacharyya S, Konar A, Tibarewala DN. A differential evolution based energy trajectory planner for artificial limb control using motor imagery EEG signal. Biomedical Signal Processing and Control. 2014 May; 11(1):107–13.
  • Electroencephalography (EEG) [Internet]. 2016 [cited 2016 Aug 2]. Available from: https://en.wikipedia.org/wiki/ Electroencephalography.
  • Kashyap, Rangasami L. Optimal choice of AR and MA parts in autoregressive moving average models. Institute of Electrical and Electronics Engineers (IEEE) Transactions on Pattern Analysis and Machine Intelligence. 1982 Feb; 4(2):99–104.
  • Ramalingam VV, Mohan S, Sugumaran S. Prosthetic arm control using Clonal Selection Classification Algorithm (CSCA) - a statistical learning approach. Indian Journal of Science and Technology. 2016 Apr; 9(16):1–8.
  • Rani BJA, Umamakeswari A. Electroencephalogram-based brain controlled robotic wheel chair. Indian Journal of Science and Technology. 2015May; 8(S9):188–97.
  • Sugumaran V, Muralidharan V, Ramachandran KI. Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mechanical Systems and Signal Processing.2007 Feb; 21(2):930–42.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.