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Comparison of Multi-Class Methods of Features Extraction and Classification to Recognize EEGs Related with the Imagination of Two Vowels


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DOI: https://doi.org/10.15866/irecap.v8i5.12709

Abstract


One of the most recent applications for the Brain Compute Interfaces (BCI) is the recognition of Electroencephalograms (EEG) related with the imagination of specific words or letters, with the purpose to develop speech prosthesis or communication devices for people with neurological disorders or communication difficulties. This paper’s aim is to acquire signals related with the imagination of the pronunciation of two phonetically opposite vowels (/a/ and /u/), then process these EEGs through the multiclass features extraction methods as the common spatial patterns (CSP) in cascade and the Independent Components Analysis (ICA), with the purpose of comparing the percentage of classification obtained from these features through the Linear Discriminant Analysis (LDA) and through the Support Vector Machines (SVM) with multiclass classification technique of One Vs One. As a result, the features extracted with ICA and classified with SVM with the One vs One technique were the 24 % more accurate than the other combinations of multiclass methods; this combination of methods recognized the 80% of the signals related with the imagination of the vowel /u/ and the 70% of the signals corresponding to the imagination of the vowel /a/.
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Keywords


Electroencephalogram; Emotiv; Common Spatial Patterns; Independent Components Analysis; Linear Discriminant Analysis; Support Vector Machine

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References


J. van Erp, F. Lotte, and M. Tangermann, Brain-Computer Interfaces: Beyond Medical Applications, Computer, vol. 45, no. 4, pp. 26–34, Apr. 2012.
http://dx.doi.org/10.1109/mc.2012.107

C. Kabdebon, F. Leroy, H. Simmonet, M. Perrot, J. Dubois, and G. Dehaene-Lambertz, Anatomical correlations of the international 10–20 sensor placement system in infants, NeuroImage, vol. 99, pp. 342–356, Oct. 2014.
http://dx.doi.org/10.1016/j.neuroimage.2014.05.046

S. N. Abdulkader, A. Atia, and M.-S. M. Mostafa, Brain computer interfacing: Applications and challenges, Egypt. Inform. J., vol. 16, no. 2, pp. 213–230, Jul. 2015.
http://dx.doi.org/10.1016/j.eij.2015.06.002

C. S. Ang, M. Sakel, M. G. Pepper, and M. P. Phillips, Use of brain computer interfaces in neurological rehabilitation, Br J Neurosci Nurs, vol. 7, no. 3, pp. 523–528, 2011.
http://dx.doi.org/10.12968/bjnn.2011.7.3.523

S. Ruiz, K. Buyukturkoglu, M. Rana, N. Birbaumer, and R. Sitaram, Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks, Biol. Psychol., vol. 95, pp. 4–20, Jan. 2014.
http://dx.doi.org/10.1016/j.biopsycho.2013.04.010

Y. Punsawad, S. Ngamrussameewong, and Y. Wongsawat, On the development of BCI and its neurofeedback training system for assistive communication device in persons with severe disability, in 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016, pp. 1–4.
http://dx.doi.org/10.1109/apsipa.2016.7820836

C. Grau et al., Conscious Brain-to-Brain Communication in Humans Using Non-Invasive Technologies, PLOS ONE, vol. 9, no. 8, p. e105225, ago 2014.
http://dx.doi.org/10.1371/journal.pone.0105225

J. H. Han, S. Ji, C. Shi, S. B. Yu, and J. Shin, Recent progress of non-invasive optical modality to brain computer interface: A review study, in The 3rd International Winter Conference on Brain-Computer Interface, 2015, pp. 1–2.
http://dx.doi.org/10.1109/iww-bci.2015.7073037

A. E. Hassanien and A. A. T. A. Az Ar, Brain-Computer Interfaces. Springer, 2015.
http://dx.doi.org/10.1007/978-3-319-10978-7

A. Kübler et al., The User-Centered Design as Novel Perspective for Evaluating the Usability of BCI-Controlled Applications, PLOS ONE, vol. 9, no. 12, p. e112392, dic 2014.
http://dx.doi.org/10.1371/journal.pone.0112392

A. T. Azar, V. E. Balas, and T. Olariu, Classification of EEG-Based Brain–Computer Interfaces, in Advanced Intelligent Computational Technologies and Decision Support Systems, B. Iantovics and R. Kountchev, Eds. Springer International Publishing, 2014, pp. 97–106.
http://dx.doi.org/10.1007/978-3-319-00467-9_9

D. Chauhan and V. Jaiswal, An efficient data mining classification approach for detecting lung cancer disease, in 2016 International Conference on Communication and Electronics Systems (ICCES), 2016, pp. 1–8.
http://dx.doi.org/10.1109/cesys.2016.7889872

B. Min, J. Kim, H. Park, and B. Lee, Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram, BioMed Res. Int., vol. 2016, p. e2618265, Dec. 2016.
http://dx.doi.org/10.1155/2016/2618265

T. S. Hai, L. H. Thai, and N. T. Thuy, Facial Expression Classification Using Artificial Neural Network and K-Nearest Neighbor, Int. J. Inf. Technol. Comput. Sci., vol. 7, no. 3, p. 27, Feb. 2015.
http://dx.doi.org/10.5815/ijitcs.2015.03.04

J. Martínez, C. Iglesias, J. M. Matías, J. Taboada, and M. Araújo, Solving the slate tile classification problem using a DAGSVM multiclassification algorithm based on SVM binary classifiers with a one-versus-all approach, Appl. Math. Comput., vol. 230, pp. 464–472, Mar. 2014.
http://dx.doi.org/10.1016/j.amc.2013.12.087

A. Rocha and S. K. Goldenstein, Multiclass From Binary: Expanding One-Versus-All, One-Versus-One and ECOC-Based Approaches, IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 2, pp. 289–302, Feb. 2014.
http://dx.doi.org/10.1109/tnnls.2013.2274735

S. Kumar, A. Sharma, K. Mamun, and T. Tsunoda, Application of cepstrum analysis and linear predictive coding for motor imaginary task classification, in 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), 2015, pp. 1–6.
http://dx.doi.org/10.1109/apwccse.2015.7476214

C. S. DaSalla, H. Kambara, M. Sato, and Y. Koike, Single-trial classification of vowel speech imagery using common spatial patterns, Neural Netw., vol. 22, no. 9, pp. 1334–1339, Nov. 2009.
http://dx.doi.org/10.1016/j.neunet.2009.05.008

R. V. Sharan and T. J. Moir, Comparison of multiclass SVM classification techniques in an audio surveillance application under mismatched conditions, in 2014 19th International Conference on Digital Signal Processing, 2014, pp. 83–88.
http://dx.doi.org/10.1109/icdsp.2014.6900805

V. B. Waghmare, Ratnadeep R. Deshmukh, Pukhraj Shrishrimal, Ganesh B. Janvale, Emotion Recognition System from Artificial Marathi Speech using MFCC and LDA Techniques Proc. of Int. Conf. on Advances in Communication, Network, and Computing, CNC, Elsevier, 2014.

J. Kim, S.-K. Lee, and B. Lee, EEG classification in a single-trial basis for vowel speech perception using multivariate empirical mode decomposition, J. Neural Eng., vol. 11, no. 3, p. 036010, 2014.
http://dx.doi.org/10.1088/1741-2560/11/3/036010

F. Shiman et al., Classification of different reaching movements from the same limb using EEG, J. Neural Eng., May 2017.

W. Zhu, H. Zhang, W. Ni, X. Xu, and J. Wu, Image classification based on ICA-WP feature of EEG signal, Technol. Health Care, vol. 24, no. s2, pp. S551–S559, Jun. 2016.
http://dx.doi.org/10.3233/thc-161181

M. Grosse-Wentrup, S. Harmeling, T. Zander, J. Hill, and B. Schölkopf, How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data, in 2013 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2013, pp. 102–105.
http://dx.doi.org/10.1109/prni.2013.35

M. Grosse-Wentrup and M. Buss, Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction, IEEE Trans. Biomed. Eng., vol. 55, no. 8, pp. 1991–2000, Aug. 2008.
http://dx.doi.org/10.1109/tbme.2008.921154

B. A. Pearlmutter and L. C. Parra, A Context-Sensitive Generalization of ICA, Adv. Neural Inf. Process. Syst., vol. 151, 1996.

M. Grosse-Wentrup and M. Buss, Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction, IEEE Trans. Biomed. Eng., vol. 55, no. 8, pp. 1991–2000, Aug. 2008.
http://dx.doi.org/10.1109/tbme.2008.921154

M. Salloum, A. Alexanderian, O. P. Le Maître, H. N. Najm, and O. M. Knio, Simplified CSP analysis of a stiff stochastic ODE system, Comput. Methods Appl. Mech. Eng., vol. 217–220, pp. 121–138, Apr. 2012.
http://dx.doi.org/10.1016/j.cma.2012.01.001

R. Lemuz-López, W. Gómez-López, I. Ayaquica-Martínez, and C. Guillén-Galván, Selección de Electrodos Basada en k-means para la Clasificación de Actividad Motora en EEG, Rev. Mex. Ing. Bioméd., vol. 35, no. 2, pp. 107–114, 2014.

S. Sun and C. Zhang, Adaptive feature extraction for EEG signal classification, Med. Biol. Eng. Comput., vol. 44, no. 10, pp. 931–935, Oct. 2006.
http://dx.doi.org/10.1007/s11517-006-0107-4

J. Milgram, M. Cheriet, and R. Sabourin, 'One Against One’ or ‘One Against All’: Which One is Better for Handwriting Recognition with SVMs?, in Tenth International Workshop on Frontiers in Handwriting Recognition, La Baule (France), 2006.
http://dx.doi.org/10.1109/iwfhr.2004.95

X. Wang, L. Ma, H. Li, and M. Wu, CSP Based Extraction and F-Score Based Optimization of Time-Frequency Power Features for EEG Mental Task Classification, in 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2015, pp. 820–824.
http://dx.doi.org/10.1109/imccc.2015.179

Q. Le, EEG-Controlling Robotic Car and Alphabetic Display by Support Vector Machine for Aiding Amyotrophic Lateral Sclerosis Patients, Electr. Eng. Undergrad. Honors Theses, Dec. 2015.

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, A review of classification algorithms for EEG-based brain–computer interfaces, J. Neural Eng., vol. 4, no. 2, p. R1, 2007.
http://dx.doi.org/10.1088/1741-2560/4/2/r01

S. Bhattacharyya, A. Khasnobish, S. Chatterjee, A. Konar, and D. N. Tibarewala, Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data, in 2010 International Conference on Systems in Medicine and Biology, 2010, pp. 126–131.
http://dx.doi.org/10.1109/icsmb.2010.5735358

A. Subasi and M. Ismail Gursoy, EEG signal classification using PCA, ICA, LDA and support vector machines, Expert Syst. Appl., vol. 37, no. 12, pp. 8659–8666, Dec. 2010.
http://dx.doi.org/10.1016/j.eswa.2010.06.065

Salguero, J., Avilés Sánchez, O., Mauledoux Monroy, M., Design of a Personal Communication Device, Based in EEG Signals, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (2), pp. 88-94.
http://dx.doi.org/10.15866/irecap.v7i2.10927


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