Single channel electroencephalogram (EEG) brain computer interface (BCI) feature extraction and quantization method for support vector machine classification

  • Authors

    • Tarmizi Ahmad Izzuddin Universiti Teknologi Malaysia
    • Norlaili Mat Safri Universiti Teknologi Malaysia
    • Fauzal Naim Zohedi Universiti Teknikal Malaysia Melaka
    • Mohamad Afzan Othman Universiti Teknologi Malaysia
    • Muhammad Shaufil Adha Shawkany Hazim Universiti Teknologi Malaysia
    2018-09-10
    https://doi.org/10.14419/ijet.v7i4.12843
  • Brain Computer Interface (BCI), Electroencephalogram (EEG), Mobile Robot, Support Vector Machine (SVM).

  • Abstract

    Over the recent years, there has been a huge interest towards Electroencephalogram (EEG) based brain computer interface (BCI) system. BCI system enables the extraction of meaningful information directly from the human brain via suitable signal processing and machine learning method and thus, many researches have applied this technology towards rehabilitation and assistive robotics. Such application is important towards improving the lives of people with motor diseases such as Amytrophic Lateral Scelorosis (ALS) disease or people with quadriplegia/tetraplegia. This paper introduces features extraction method based on the Fast Fourier Transform (FFT) with logarithmic bin-ning for rapid classification using Support Vector Machine (SVM) algorithm, with an application towards a BCI system with a shared con-trol scheme. In general, subjects wearing a single channel EEG electrode located at F8 (10-20 international standards) were required to syn-chronously imagine a star rotating and mind relaxation at specific time and direction. The imagination of a star would trigger a mobile robot suggesting that there exists a target object at certain direction. Based on the proposed algorithm, we showed that our algorithm can distin-guish between mind relaxation and mental star rotation with up to 80% accuracy from the single channel EEG signals.

     

     

  • References

    1. [1] L. Bi, X. A. Fan, and Y. Liu, “EEG-based brain-controlled mobile robots: A survey,†IEEE Trans. Human-Machine Syst., vol. 43, no. 2, pp. 161–176, 2013. https://doi.org/10.1109/TSMCC.2012.2219046.

      [2] R. Leeb, L. Tonin, M. Rohm, L. Desideri, T. Carlson, and J. Del R. Millan, “Towards Independence: A BCI Telepresence Robot for People with Severe Motor Disabilities,†Proc. IEEE, vol. 103, no. 6, pp. 969–982, Jun. 2015. https://doi.org/10.1109/JPROC.2015.2419736.

      [3] N.-S. Kwak, K.-R. Müller and S.-W. Lee, “A lower limb exoskeleton control system based on steady state visual evoked potentials,†J. Neural Eng., vol. 12, no. 5, p. 056009, 2015. https://doi.org/10.1088/1741-2560/12/5/056009.

      [4] M. S. A. Shawkany Hazim, N. Mat Safri, and M. A. Othman, “Single channel electroencephalogram feature extraction based on probability density function for synchronous brain computer interface,†J. Teknol., vol. 78, no. 7–5, pp. 105–111, 2016.

      [5] T. A. Izzuddin, M. A. Ariffin, Z. H. Bohari, R. Ghazali, and M. H. Jali, “Movement intention detection using neural network for quadriplegic assistive machine,†in 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2015, pp. 275–280. https://doi.org/10.1109/ICCSCE.2015.7482197.

      [6] G. Pfurtscheller and F. H. Lopes, “Event-related EEG / MEG synchronization and desynchronization : basic principles,†Clin. Neurophysiol., vol. 110, pp. 1842–1857, 1999. https://doi.org/10.1016/S1388-2457(99)00141-8.

      [7] H. Azmy and N. M. Safri, “EEG based BCI using visual imagery task for robot control,†J. Teknol. (Sciences Eng., vol. 61, no. 2 SUPPL, pp. 7–11, 2013.

      [8] G. D. Jacobs and R. Friedman, “EEG Spectral Analysis of Relaxation Techniques,†Appl. Psychophysiol. Biofeedback, vol. 29, no. 4, pp. 245–254, Dec. 2004. https://doi.org/10.1007/s10484-004-0385-2.

      [9] P. Saidi, G. K. Atia, A. Paris, and A. Vosoughi, “Motor imagery classification using multiresolution analysis and sparse representation of EEG signals,†in 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015, pp. 815–819.

      [10] H. Cao, W. G. Besio, S. Jones, and P. Zhou, “Individualization of data-segment-related parameters for improvement of EEG signal classification in brain-computer interface,†Trans. Tianjin Univ., vol. 16, no. 3, pp. 235–238, Jun. 2010. https://doi.org/10.1007/s12209-010-0041-2.

      [11] N. Merrill, T. Maillart, B. Johnson, and J. Chuang, “Improving Physiological Signal Classification Using Logarithmic Quantization and a Progressive Calibration Technique,†in Proceedings of the 2nd International Conference on Physiological Computing Systems, 2015, pp. 44–51.

      [12] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,†J. Mach. Learn. Res., vol. 12, pp. 2825–2830, Nov. 2011.

      [13] C. Jeunet, B. N’Kaoua, and F. Lotte, “Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their neural correlates.†Prog. Brain Res., vol. 228, pp. 3–35, 2016. https://doi.org/10.1016/bs.pbr.2016.04.002.

      [14] M. S. Adha, N. M. Safri, and M. A. Othman, “Real-time target selection based on electroencephalogram (EEG) signal,†ARPN J. Eng. Appl. Sci., vol. 10, no. 19, pp. 8757–8761, 2015.

  • Downloads

  • How to Cite

    Ahmad Izzuddin, T., Mat Safri, N., Naim Zohedi, F., Afzan Othman, M., & Shaufil Adha Shawkany Hazim, M. (2018). Single channel electroencephalogram (EEG) brain computer interface (BCI) feature extraction and quantization method for support vector machine classification. International Journal of Engineering & Technology, 7(4), 2095-2099. https://doi.org/10.14419/ijet.v7i4.12843

    Received date: 2018-05-15

    Accepted date: 2018-07-08

    Published date: 2018-09-10