Temporal based EEG Signals Classification for Talocrural and Knee Joint Movements using Emotive Head Set


  • Anjum Naeem Malik National University of Sciences & Technology, Islamabad, Pakistan
  • Javaid Iqbal National University of Sciences & Technology, Islamabad, Pakistan
  • Mohsin I Tiwana National University of Sciences & Technology, Islamabad, Pakistan




kurtosis, Quadratic Discriminant Analysis, Naïve Bayes, Support Vector Machine.


Recent developments in Brain Computer Interfacing (BCI) and neuroprosthetics have played a vital role for disable people to expect better life quality. In this contribution Electroencephalographic (EEG) signals acquired from six healthy test subjects, are used for the offline analysis of BCI through classification of four lower limb movements including talocrural (ankle) joint dorsi-planter flexion and knee joint extension-flexion. Fourteen channel Emotive EPOC head set is used to acquire EEG signals from sensorimotor cortex area of brain, using a particular data acquisition timeline protocol. Features are extracted in time domain from raw EEG data. Power spectral density, variance, mean value and kurtosis features are applied on raw EEG signals. Multiple classification algorithms are implemented for discrimination of four lower limb movements within data set. The paper uses Quadratic discriminant analysis, Naïve Bayes and Support vector machine classifiers to stratify the movement intent of lower limb. Maximum classification accuracies achieved through various classifiers are; 86.35% with average band power & QDA, 84.38% with mean value & QDA and 78.13% with power spectral density & Quadratic-SVM. The presented findings are optimistic in making the path easier towards the development of BCIs with rich EEG based control signals using noninvasive technology.



(1) Vaughan, T.M., "Guest editorial brain-computer interface technology: a review of the second international meeting," in Neural Systems and Rehabilitation Engineering, IEEE Transactions on , vol.11, no.2, pp.94-109, June 2003

(2) Norani, N.A.M.; Mansor, W.; Khuan, L.Y., "A review of signal processing in brain computer interface system," in Biomedical Engineering and Sciences (IECBES), 2010 IEEE EMBS Conference on , vol., no., pp.443-449, Nov. 30 2010-Dec. 2 2010

(3) Dolezal, J.; St'astny, J.; Sovka, P., "Recording and recognition of movement related EEG signal," in Applied Electronics, 2009. AE 2009,

vol., no., pp.95-98, 9-10 Sept. 2009

(4) Gwin JT, Ferris DP. An EEG-based study of discrete isometric and isotonic human lower limb muscle contractions. Journal of NeuroEngineering and Rehabilitation. 2012; 9:35. Doi: 10.1186/1743-0003-9-35.

(5) Boyd LA, Vidoni ED, Daly JJ. Answering the call: The influence of neuroimaging and electrophysiological evidence on rehabilitation. Phys Ther. 2007; 87(6):684–703. Doi: 10.2522/ptj.20060164.

(6) Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 2008; 7(11):1032–1043. doi: 10.1016/S1474-4422(08)70223-0.

(7) Gwin JT. et al. Removal of movement artifact from high-density EEG recorded during walking and running. J Neurophysiol. 2010; 103(6):3526–3534. Doi: 10.1152/jn.00105.2010.

(8) Allen DP, MacKinnon CD. Time-frequency analysis of movement-related spectral power in EEG during repetitive movements: a comparison of methods. J Neurosci Methods. 2010; 186(1):107–115. Doi: 10.1016/j.jneumeth.2009.10.022.

(9) Alessandro P., Ronald G., Larry F., Jose L.,” Neural decoding of treadmill walking from noninvasive electroencephalographic signals”. Journal of Neurophysiology Published 1 Oct 2011 Vol. 106 no. 4, 1875-1887 DOI: 10.1152/jn.00104.2011

(10) Ramoser H, Muller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng. 2000; 8(4):441–446. Doi: 10.1109/86.895946.

(11) Muller-Gerking J, Pfurtscheller G, Flyvbjerg H. Designing optimal

spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol. 1999; 110(5):787–798. Doi: 10.1016/S1388-2457(98)00038-8.

(12) Khasnobish, A.; Bhattacharyya, S.; Konar, A.; Tibarewala, D.N.; Nagar, A.K., "A Two-fold classification for composite decision about localized arm movement from EEG by SVM and QDA techniques," inNeural Networks (IJCNN), The 2011 International Joint Conference on , vol., no., pp.1344-1351, July 31 2011-Aug. 5 2011

(13) Unde, S.A.; Shriram, R., "Coherence Analysis of EEG Signal Using Power Spectral Density," in Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on , vol., no., pp.871-874, 7-9 April 2014

(14) Emotiv.com (2013). EPOC Features. [online] Retrieved from: http://www.emotiv.com/epoc/ [Accessed: 5 Mar 2013

(15) Kaiyang L.; Xiadong Z.; Yuhuan Du,”A SVM based classification of EEG for predicting the movement intent of human body,” in Ubiquitous Robots and Ambient Intelligence (URAI), 2013 Tenth International Conference. Doi: 10.1109/URAI.2013.6677297

(16) Xiaoyuan Zhu; Cuntai Guan; Jiankang Wu; Yimin Cheng; Yixiao Wang, "Bayesian Method for Continuous Cursor Control in EEG-Based Brain-Computer Interface," in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of

the , vol., no., pp.7052-7055, 17-18 Jan. 2006

(17) Hosni, S.M.; Gadallah, M.E.; Bahgat, S.F.; AbdelWahab, M.S., "Classification of EEG signals using different feature extraction techniques for mental-task BCI," in Computer Engineering & Systems, 2007. ICCES '07. International Conference on , vol., no., pp.220-226, 27-29 Nov. 2007

doi: 10.1109/ICCES.2007.4447052

(18) Daud, S.S.; Sudirman, R., "Butterworth Bandpass and Stationary Wavelet Transform Filter Comparison for Electroencephalography Signal," in Intelligent Systems, Modelling and Simulation (ISMS), 2015 6th International Conference on , vol., no., pp.123-126, 9-12 Feb. 2015

doi: 10.1109/ISMS.2015.29

(19) Fathy, A.; Fahmy, A.; ElHelw, M.; Eldawlatly, S., "EEG spectral analysis for attention state assessment: Graphical versus classical classification techniques," in Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on , vol., no., pp.888-891, 17-19 Dec. 2012

doi: 10.1109/IECBES.2012.6498088




How to Cite

Malik, A. N., Iqbal, J., & Tiwana, M. I. (2016). Temporal based EEG Signals Classification for Talocrural and Knee Joint Movements using Emotive Head Set. British Journal of Healthcare and Medical Research, 2(6), 69. https://doi.org/10.14738/jbemi.26.1730