Spatial Temporal based Classification for Antebrachium and Carpus Movement of EEG Data Using Emotive Head set
Keywords:Classification, Multilayer perceptron, Support Vector Machine, Hjorth Parameters
AbstractElectroencephalographic (EEG) signals from test subjects are used for BCI analysis and classification of the four upper limb movements. Brain computer interface (BCI) system provides better neural control to the user, over two antebrachium and two carpus movement. This paper uses Epoc Emotiv head set with fourteen electrodes to acquire signal from the motor area of scalp. A particular protocol for data acquisition is followed. Our method is based on the time domain feature extraction techniques. Activity feature is applied on the signals. In order to discriminate between four upper limb movements within the data set, the paper uses Quadratic support vector machine, RBF SVM and Multilayer perceptron. The best classification accuracy of two antebrachium and two carpus movement is achieved by Quadratic SVM and RBF SVM when Gaussian window is used to calculate the activity feature vector. The number of correctly classified instants in term of percentage is 76.92% and 75.96% for quadratic SVM and RBF SVM. Current classifiers show promising results, where as to make conclusion more generalized and to enhance the classification accuracy, further experiments need to be carried out.
Kai Keng Ang; Cuntai Guan; Kok Soon Phua; Chuanchu Wang, Irvin The; Chang Wu Chen; Effie Chew:” Transcranial direct current stimulation and EEG-based motor imagery BCI for upper limb stroke rehabilitation” 34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August - 1 September, 2012.
Wei Tuck Lee; Humaira Nisar; Aamir S. Malik; Kim Ho Yeap, “A Brain Computer Interface for Smart Home Control” 2013 IEEE 17th International Symposium on Consumer Electronics (ISCE)
L. R. Hochberg; M. D. Serruya; G. M. Friehs; J. A. Mukan; M. Saleh,A. H. Caplan; A. Branner; D. Chen; R. D. Penn; and J. P. Donoghue. “Neuronal ensemble control of prosthetic devices by a human with tetraplegia.” Nature, vol. 442, no. 13, pp. 164–171, Jul. 2006
S. M. T. Müller; W. C. Celeste; T. F. Bastos-Filho; and M. Sarcinelli- Filho. “Brain-computer Interface Based on Visual Computer Interface Based on Visual Evoked Potentials to Command Autonomous Robotic Wheelchair.” Journal of Medical and Biological Engineering, vol. 30, no. 6, pp. 407–416, 2010.
Birbaumer N; Ghanayim N; Hinterberger T; Iversen I, Kotchoubey B, Ku ¨bler A, Perelmouter J, Taub E, Flor H. “A spelling device for the paralyzed”. Nature 398:297–298. doi:10.1038/18581, 1999.
M. Asghari Oskoei and H. Hu. "Support vector machine-based classification scheme for myoelectric control applied to upper limb" IEEE Transactions on Biomedical Engineering, vol. 55, no. 8, pp. 1956-1965, 2008.
Wolpaw JR; Birbaumer N; McFarland DJ; Pfurtscheller G; Vaughan TM. “Brain–computer interfaces for communication and control.” Clin Neurophysiol 113:767–791. doi: 10.1016/S1388-2457(02)00057-3, 2002.
Emotiv.com (2013). EPOC Features. [online] Retrieved from: http://www.emotiv.com/epoc/ [Accessed: 5 Mar 2013].
G. N. Garcia; T. Ebrahimi and J.-M. Vesin. “Support vector EEG classication in the Fourier and time-frequency correlation domains.” In Conference Proceedings of the First International IEEE EMBS Conference on Neural Engineering, 2003.
S. Solhjoo and M. H. Moradi. “Mental task recognition: A comparison between some of classication methods.” In BIOSIGNAL 2004 International EURASIP Conference, 2004
H. Lee and S. Choi. “Pca+hmm+svm for eeg pattern classification.” In Proceedings of the Seventh International Symposium on Signal Processing and Its Applications, 2003.
Yi et al.: “EEG feature comparison and classification of simple and compound limb motor imagery.” Journal of NeuroEngineering and Rehabilitation 2013 10:106.
Ricardo C.Caracillo and Maria Claudia F. Castro. “Classiﬁcation of Executed Upper Limb Movements by Means of EEG.” In Biosignals and Biorobotics Conference (BRC), 2013 ISSNIP.
J. Sleight, P. Pillai, and S. Mohan, “Classiﬁcation of Executed and Imagined Motor Movement EEG Signals,” Ann Arbor, p. 10, 2009.
Johnny Lee and Desney Tan. “Using a low-cost electroencephalograph for task classiﬁcation in hci research.” UIST’06: Proceedings of the 19th annual ACM symposium on User interface software and technology, Oct 2006.
G.V. Sridhar and Dr. P. Mallikarjuna Rao “A Neural Network Approach for EEG classification in BCI.” International Journal of Computer Science and Telecommunications [Volume 3, Issue 10, October 2012]