Hand and Leg Movement Prediction using EEG Signal by Stacked Deep Auto Encoder

  • Kulsheet Kaur Virdi P.G. Student, Department of Computer Science & Engineering, Samrat Ashok Technological Institute Vidisha, India
  • Satish Pawar Professor, Department of Computer Science & Engineering, Samrat Ashok Technological Institute Vidisha, India
Keywords: BCI, Non-Invasive, EEG, Feature Extraction, Stacked, Encoder, Deep learning, Accuracy.


Brain Computer Interface (BCI) is device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. Brain–computer interfacing is an uprising field of research wherever signals extracted from the human brain are used for deciding and generation of control signals. Selection of the most appropriate classifier to find the mental states from electroencephalography (EEG) signal is an open research area due to the signal’s non-stationary and ergodic nature.

In this research work the proposed algorithm is designed to solve an important application in BCI where left hand forward–backward movements and right hand forward-backward movements as well as left leg movement and right leg movement are needed to be classified. Features are extracted from these datasets to classify the type of movements. A staked Deepauto encoder is used for classification of hand and leg movements and compared with other classifiers. The accuracy of stacked deepauto encoder is better with respect to other classifiers in terms of classification of hand and leg movement of EEG signals.


Download data is not yet available.


[1] E. Ianez, M. C. Furio, J. M. Azorn, J. A. Huizzi, and E. Fernandez, “Bmental tasks based brain robot interface, robotics and autonomous systems,” Elsevier, 2010, pp. 1238-1245.
[2] U. Gl, Y. Gltrk, C. Loo, “Evaluation of fractal dimension estimation methods for feature extraction in motor imagery based brain computer interface,”Elsevier,2011, vol. 3,pp. 589-594.
[3] H. Sun, Y. Xiang, H. Zhu and J. Zeng, “On-line EEG classification for Brain Computer Interface based on CSP and SVM”, IEEE, 2010.
[4] Howida A. Shedeed, Mohamed F. Issa, Salah M. El-sayed, “Brain EEG Signal Processing for Controlling a Robotic Arm”, IEEE, 2013, pp. 152-157.
[5] Mohd Shuhanaz Zanar Azalan, Paulraj M P, Sazali bin Yaacob, “Classification of Hand Movement Imagery Tasks for Brain Machine Interface Using Feed-Forward Network”, IEEE, 2014, pp. 431-436.
[6] Muhammed. S. P. P.• Waseem Raza. and David Martin Wart Powers. "Brain computer Interface Classification of eeg for left and right wrist movements using AR modeling & Bhattacharya distance”, Sensor Networks and Information Processing, 2011, pp.7-10.
[7] Bhattacharyya, Saugat, Anwesha Khasnobish, Amit Konar, D. N. Tibarewala, and Atulya K. Nagar."Performance analysis of left right hand movement classification from EEG signal by intelligent algorithms." Cognitive Algorithms, Mind, and Brain (CCMB), IEEE, 2011 pp. 1-8.
[8] Hema, Chengalvarayan Radha Krishna Murthy, M. P. Paulraj, S. Yaacob, A. H. Adorn & R.Nagarajan. "Recognition of motor imagery of hand movements for BMI using PCA features." Electronic Design (ICED), IEEE, 2008, pp. 1 -4.
[9] Marquez L, Alejandro, & Munoz. "Analysis & classification of electroencephalographic signals (EEG) to identify arm movements.", Electrical Engineering, Computing Science and Automatic Control (CCE), IEEE, 2013, pp. 138- 143.
[10] Zhiwei, Li, and Shen Minfen. "Classification of mental task EEG signals using wavelet packet entropy and SYM.", IEEE, 2007, pp.906-909.
[11] Ting, Wu, Yan Guo-zheng, Yang Banghua, & Sun Hong. "EEG feature extraction based on wavelet packet decomposition for brain computer interface.", Elsevier, 2008, vol. 41, issue 6, pp. 618-625.
[12] Syed Khairul Bashar and Mohammed Imamul Hassan Bhuiyan, “Identification of Arm Movements Using Statistical Features from EEG Signals in Wavelet Packet Domain ”, IEEE, 2015.
[13] Saugat Bhattacharyya, Debabrota Basu, Amit Konara, D. N. Tibarewala, “Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm”, Elsevier, 2015.
[14] Faisal Farooq and Preben Kidmose, “Random Forest classification for p300 based brain computer interface applications,” IEEE, 2013, pp 1-5.
[15] Prasant Kumar Pattnaik, Jay Sarraf,” Brain Computer Interface issues on hand movement”, Journal of King Saud University–Computer and Information Sciences (2018) 30, 18–24.
[16] Muhammed Al-Suify, Walid Al-Atabany, Mohamed. A. A. Eldosoky, “Classification of right and left hand movement using nonlinear analysis” IEEE, 2017.
[17] https://sites.google.com/site/projectbci/

October 2019
How to Cite
Virdi, K. K., & Pawar, S. (2019). Hand and Leg Movement Prediction using EEG Signal by Stacked Deep Auto Encoder. SMART MOVES JOURNAL IJOSCIENCE, 5(10), 36-48. https://doi.org/10.24113/ijoscience.v5i10.230