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Tongue-Operated Biosignal over EEG and Processing with Decision Tree and kNN

Yıl 2021, Cilt: 9 Sayı: 1, 112 - 125, 29.01.2021

Kutlucan Görür Mehmet Recep Bozkurt Muhammed Serdar Bascıl Feyzullah Temurtas

https://doi.org/10.21541/apjes.583049

Öz

Kaynakça

  • [1] X. Huo, M. Ghovanloo, “Tongue Drive: A wireless tongue-operated means for people with severe disabilities to communicate their intentions”, IEEE Comm. Magaz., vol.50, no.10, pp.128-135, 2012.
  • [2] L.N.S. Andreasen Struijk, “An inductive tongue computer interface for control of computers and assistive devices,” IEEE Trans on Biomed Engin., vol. 53, no.12, pp. 2594-2597, 2006.
  • [3] Y. Nam, Q. Zhao, A. Cichocki, S. Choi, “Tongue-Rudder: A Glossokinetic-Potential-Based tongue–machine interface,” IEEE Trans. on Bio Engin., vol.59, no.1, pp.290-299, 2012.
  • [4] Y. Nam, B. Koo, A. Cichocki, S. Choi, “GOM-Face: GKP, EOG, and EMG-Based multimodal interface with application to humanoid robot control,” IEEE Trans. on Biomed. Engin. vol.61, no.2, pp.453-462, 2014.
  • [5] Y. Nam, B. Koo, A. Cichocki, S. Choi, “Glossokinetic Potentials for a tongue–machine interface,” IEEE Systems, Man, & Cybernetics Magaz., vol.2, no.1, pp.6-13, 2016.
  • [6] H. Tang, D.J. Beebe, “An oral tactile interface for blind navigation,” IEEE Trans On Neural Sys. and Rehab. Engin., vol.14, no.1, pp.116-123, 2006.
  • [7] X. Bao, J. Wang, J. Hu, “Method of individual identification based on electroencephalogram analysis,” Inter Conf on New Trends in Infor and Ser Sci. pp.390-393 (DOI: 10.1109/NISS.2009.44. 2009).
  • [8] K.J. Miller, P. Shenoy, M. Nijs, L.B. Sorensen, et.al,. ”Beyond the Gamma Band: The role of high-frequency features in movement classification,” IEEE Trans. on Biomed. Engin. vol.55, no.5, pp.1634-1637, 2008.
  • [9] D. Xiao, J. Hu, “Identification of motor imagery EEG signal,” Inter Conference on Biomedical Eng and Computer Science, 2010; Wuhan, China.
  • [10] B. Reuderink, M. Poel, A. Nijholt, “The impact of loss of control on movement BCIs,” IEEE Trans on Neural Syst. and Reha. Engin., vol.19, no.6, pp.628-637, 2011.
  • [11] X. Huo, J. Wang, M. Ghovanloo, “A magneto-inductive sensor based wireless tongue-computer interface,” IEEE Trans on Neural Syst. and Reha. Engin., vol.16, no.5, pp.497-504, 2008.
  • [12] R. Rupp, M. Rohm, M. Schneiders, A. Kreilinger, G.R. Müller-Putz. “Functional rehabilitation of the paralyzed upper extremity after spinal cord injury by noninvasive hybrid neuroprostheses,” Proceedings of the IEEE, vol.103, no.6, pp.954-968, 2015.
  • [13] L.M. Alonso-Valerdi, F. Sepulveda, “Development of a simulated living environment platform: Design of BCI assistive software and modelling of a virtual dwelling place,” Computer Aided Design, vol,54, pp.39-50, 2014.
  • [14] X. Huo, J. Wang, M. Ghovanloo, “Using magneto-inductive sensors to detect tongue position in a wireless assistive technology for people with severe disabilities,” IEEE Sensor Conf; 2007, Atlanta, USA.
  • [15] X. Huo, J. Wang, M. Ghovanloo, “A wireless tongue-computer interface using stereo differential magnetic field measurement,” Proceedings of the 29th Ann Inter Conf of the IEEE EMBS Cité Internationale, 2007, Lyon, France.
  • [16] X. Huo, J. Wang, M. Ghovanloo, “A magnetic wireless tongue-computer interface,” Proceed of the 3rd Inter IEEE EMBS Conf on Neural Engineering, 2007, Kohala Coast, Hawaii, USA.
  • [17] G. Krishnamurthy, M. Ghovanloo, “Tongue Drive: A tongue operated magnetic sensor based wireless assistive technology for people with severe disabilities,” IEEE Inter Sym on Circuits and Systems (ISCAS), pp.5551-5554, 2006.
  • [18] R. Vaidyanathan, B. Chung, L. Gupta et.al., “Tongue-movement communication and control concept for hands-free human–machine interfaces,” IEEE Trans. on Sys. Man and Cybernetics. vol.37, no.4, pp.533-546, 2007.
  • [19] R.Vaidyanathan, C.J. James, “Independent component analysis for extraction of critical features from tongue movement ear pressure signals,” Proceed of the 29th Ann Inter Conf of the IEEE EMBS Cité Internationale; 2007; Lyon, France.
  • [20] R. Vaidyanathan, L. Gupta, H. Kook, J. West, “A decision fusion classification architecture for mapping of tongue movements based on aural flow monitoring,” Proceed of the IEEE International Conference on Robotics and Automation, 2006; Orlando, Florida.
  • [21] R. Vaidyanathan, M. Fargues, L. Gupta et.al., “A dual-mode human-machine interface for robotic control based on acoustic sensitivity of the aural cavity,” IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob’06, 2006, Pisa, Italy.
  • [22] R. Vaidyanathan, H. Kook, L. Gupta, J. West, “Parametric and non-parametric signal analysis for mapping air flow in the ear-canal to tongue movements: A new strategy for hands-free human-machine interfaces,” IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings, 2004, Montreal, Canada.
  • [23] H. Jasper, “The ten twenty electrode system of the international federation,” Electro Clin Neuro., vol.10, no.2, pp.370-375, 1958.
  • [24] M.S. Bascil, A.Y. Tesneli, F. Temurtas, “Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN,” Australas Phys. Eng. Sci Med. vol. 39, no.3, pp.665-676, 2016.
  • [25] N. Yalcın, G. Tezel, C. Karakuzu, “Epilepsy diagnosis using artificial neural network learned by PSO,” Turk J. Elec. Eng & Comp. Sci. vol.23,pp.421-432, 2015.
  • [26] K.D. Desai, M.S. Sankhe, “A Real-Time Fetal ECG Feature Extraction Using Multiscale Discrete Wavelet Transform,” 5th Int Conf. on Biomedical Eng. and Infor., pp. 407-412, 2012.
  • [27] A. Hamad, E.H. Houssein, A.E. Hassanien, A.A. Fahmy, “Feature Extraction of Epilepsy EEG using Discrete Wavelet Transform,” 12th Int. Computer Engineering Conf., pp.109-195, 2016.
  • [28] T.K. Patel, P.C.Panda, S.C. Swain, “Mohanty SK. A Fault Detection Technique in Transmission Line By using Discrete Wavelet Transform,” 2nd Int. Conf. on Electrical, Computer and Communication Tech., 2017.
  • [29] E.J. Rechy-Ramirez, H. Hu, “Bio-signal based control in assistive robots: a survey,” Digital Communications and Networks, vol.1, no.2, pp.85-101, 2015.
  • [30] J.G. Proakis, D.G. Manolakis, “Digital signal processing principles, algorithms and applications,” 3rd edn Prentice-Hall, New York [chapter 12]; 1996.
  • [31] P. Stoica, R. Moses, “Spectral analysis of signals,” Prentice Hall International, New York. 2005.
  • [32] E. Alpaydın, “Introduction to Machine Learning,” MIT Press, Cambridge, Massachusetts, Second Edition. 2010.
  • [33] M. Kavita, M.R. Vargantwar, M.R. Sangita, “Classification of EEG using PCA, ICA and neural network,” Int. J. Eng. Adv. Technol., vol. 1, pp.1–4, 2011.
  • [34] R. Vigário, J. Särelä, V. Jousmäki, et.al. “Independent component approach to the analysis of EEG and MEG recordings,” IEEE Trans. on Biomed. Engin. vol.47, no.5, pp.589-593, 2000.
  • [35] R.Chai, R.G. Naik, N.T. Nguyen, et.al., “Selecting optimal EEG channels for mental tasks classification: An approach using ICA,” IEEE Congress on Evolutionary Computation (CEC), pp.1331-1335, 2016.
  • [36] B. Şen, M. Peker, “Novel approaches for automated epileptic diagnosis using fcbf selection and classification algorithms,” Turk J. Elec. Eng & Comp. Sci. vol.21, pp.2092-2109, 2013.
  • [37] R.A. Ramadan, A.V. Vasilakos, “Brain computer interface: control signals review,” Neurocomputing. vol.223, pp.26-44, 2017.
  • [38] B. Obermaier, C. Neuper, C. Guger, G. Pfurtscheller, “Information transfer rate in a five-classes brain–computer interface,” IEEE Trans. on Neural Syst. and Reha., vol.9, no. 3, pp.283-288, 2001.
  • [39] M. Sengelmann, A.K. Engel, A. Maye, “Maximizing information transfer in ssvep-based brain–computer interfaces,” IEEE Trans. on Biomedical Engin. vol.64, no.2, pp.381-394, 2017.
  • [40] B. Wang, C.M. Wong, F. Wan et.al., “Comparison of Different Classification Methods for EEG-Based Brain Computer Interfaces: A Case Study,” IEEE Int. Conf on Infor and Automation, Zhuhai/Maca, China, pp.1416-1421, 2009.
  • [41] K. Gorur, M.S. Bascil, M.R. Bozkurt, F. Temurtas, “Classification of Thyroid Data Using Decision Trees, kNN and SVM Methods,” International Artificial Intelligence and Data Processing Symposium, IDAP’16, Malatya, Turkey, pp. 130-134, 2016.
  • [42] Ö. Aydemir, T. Kayıkçıoğlu, “Investigation of the most appropriate mother wavelet for characterizing imaginary EEG signals used in BCI systems,” Turk J. Elec. Eng. & Comp. Sci. vol.24, pp.38-49, 2016.
  • [43] S. Vanhatalo, J. Voipio, A. Dewaraja, et.al., “Topography and elimination of slow EEG responses related to tongue movements,” NeuroImage, vol. 20, pp.1419-1423, 2003.
  • [44] Y. Nam, K. Bonkon, S. Choi, “Language-related glossokinetic potentials on scalp,” IEEE International conference on systems, Man, and Cybernetics, San Diego, USA, 2014.
  • [45] R. Leeb, F. Lee, C. Keinrath, R. Scherer, et.al., “Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment,” IEEE Trans. on Neural Syst. and Reha., vol.15, no.4, pp.473-481, 2007.
  • [46] V. Jayaram, M. Alamgir, Y. Altun, B. Schölkopf, “Grosse-Wentrup M. Transfer learning in brain-computer interfaces,” IEEE Computational Intelligence Magazine, pp.20-31, 2016.
  • [47] J.C. Kao, S.D. Stavisky, et.al., “Information systems opportunities in brain–machine interface decoders,”, Proceedings of the IEEE; vol.102, no.5, pp.666-682, 2014.
  • [48] A.B. Barreto, A.M. Taberner, L.M. Vicente, “Classification of spatio-temporal EEG readiness potentials towards the development of a brain-computer interface, bringing together education, science and technology,” Proceedings of the IEEE, Tampa, USA, 1996.
  • [49] S.Cerutti, “In the Spotlight: Biomedical signal processing,” IEEE Reviews In Biomedical Engin. vol.2, pp.9-11, 2009.
  • [50] X. Chen, C. He, J. Z .Wang et.al., “An IC-PLS framework for group corticomuscular coupling analysis,” IEEE Trans on Biomed Engin. vol.60, no.7, pp.2022-2033, 2013.
  • [51] J.J. Daly, Y. Fang, et.al., “Prolonged cognitive planning time, elevated cognitive effort, and relationship to coordination and motor control following stroke,” IEEE Trans on Neural Syst. and Reha. Engin. vol.14, no.2, pp.168-171, 2006.
  • [52] Y. Li, C. Guan, J. Qin, “Enhancing feature extraction with sparse component analysis for brain-computer interface,” Proceed. of the IEEE Engin. in Med. and Bio. 27th Annual Conference Shanghai, China, 2005.
  • [53] H.M. Genc, Z Cataltepe, T. Pearson, “A New PCA/ICA based feature selection method,” IEEE Signal Processing and Comm. App. 15th (SIU); 2007.
  • [54] M.J. McKeown, R. Saab, R. Abu-Gharbieh, “A combined independent component analysis (ICA)/ empirical mode decomposition (EMD) method to infer corticomuscular coupling,” IEEE Neural Engin Conf Proceed 2nd Int (EMBS), pp.1-8, 2005.
  • [55] K. Gorur, M.R. Bozkurt, M.S. Bascil, “Temurtas F. Glossokinetic potential based tongue–machine interface for 1-D extraction,” Australasian Physical & Engineering Sciences in Medicine, vol.41, no.2, pp.379-391, 2018. [56] K. Gorur, M.R. Bozkurt, M.S. Bascil, F. Temurtas, “Glossokinetic Potential Based Tongue–Machine Interface For 1-D Extraction Using Neural Networks,” Biocybernetics And Biomedical Engineering. Vol.38, No.3, pp.745-759, 2018.
  • [57] V.Schetinin, C. Maple, “A Bayesian Model Averaging Methodology For Detecting Eeg,” 15th International Conference On Digital Signal Processing, pp. 499-502, 2007.
  • [58] K. Gorur, M.R. Bozkurt, M.S. Bascil, F. Temurtas,” GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface”, Traitement du Signal, Vol:36, No:4, pp.319-329, 2019.
  • [59] O.Cetin, F. Temurtas, “Classification of Magnetoencephalography Signals Regarding Visual Stimuli by Generalized Regression Neural Network,” Dicle Med J.Vol.46, No.1, pp.19-25, 2019.
  • [60] K. Gorur, M.R. Bozkurt, M.S. Bascil, F. Temurtas,”Comparative Evaluation for PCA and ICA on Tongue-Machine Interface Using Glossokinetic Potential Responses”, Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Vol:16, No:1, pp.35-46, 2020.

Tongue-Operated Biosignal over EEG and Processing with Decision Tree and kNN

Yıl 2021, Cilt: 9 Sayı: 1, 112 - 125, 29.01.2021

Kutlucan Görür Mehmet Recep Bozkurt Muhammed Serdar Bascıl Feyzullah Temurtas

https://doi.org/10.21541/apjes.583049

Öz

Tongue-machine interface (TMI) is a feasible way between the assistive technologies and paralyzed individuals who have lost their abilities to communicate with the environment. Researchers have presented equipment based tongue-machine interfaces to reach a reliable and speedy system. However, this kind of interfaces may occur a way of obtrusive, unattractive and unhygienic for disabled persons. In this research, we intended to propose a natural, unobtrusive and robust glossokinetic potential signals (GKP) based TMI exploring the success of the novel machine learning algorithms. The tongue is bound up with cranial nerves to the brain, which can escape from the spinal cord injuries in general. Moreover, the tongue has highly capable of sophisticated manipulation tasks with less perceived exertion in the oral cavity and gives degrees of privacy. In this study, ten naive healthy subjects have attended who were between 22-34 ages. Decision Tree (DT) and k-Nearest Neighbors (kNN) algorithms were used with Mean-Absolute Value (MAV) and Power Spectral Density (PSD) methods. Moreover, Discrete Wavelet Transform (DWT) was implemented to reveal the theta and delta subbands. In the study, the highest value was provided as 96.77% by the k-Nearest Neighbor algorithm for the best participant. Furthermore, the GKP-based TMI may be an alternative system for the limitations of the brain-computer interfaces. It is well-known that EEG deficits are major concerns for brain-computer interfaces.

Anahtar Kelimeler

Glossokinetic Potential Tongue-Machine Interface Brain-Computer Interface Decision Tree k-Nearest Neighbors Discrete Wavelet Transform

Kaynakça

  • [1] X. Huo, M. Ghovanloo, “Tongue Drive: A wireless tongue-operated means for people with severe disabilities to communicate their intentions”, IEEE Comm. Magaz., vol.50, no.10, pp.128-135, 2012.
  • [2] L.N.S. Andreasen Struijk, “An inductive tongue computer interface for control of computers and assistive devices,” IEEE Trans on Biomed Engin., vol. 53, no.12, pp. 2594-2597, 2006.
  • [3] Y. Nam, Q. Zhao, A. Cichocki, S. Choi, “Tongue-Rudder: A Glossokinetic-Potential-Based tongue–machine interface,” IEEE Trans. on Bio Engin., vol.59, no.1, pp.290-299, 2012.
  • [4] Y. Nam, B. Koo, A. Cichocki, S. Choi, “GOM-Face: GKP, EOG, and EMG-Based multimodal interface with application to humanoid robot control,” IEEE Trans. on Biomed. Engin. vol.61, no.2, pp.453-462, 2014.
  • [5] Y. Nam, B. Koo, A. Cichocki, S. Choi, “Glossokinetic Potentials for a tongue–machine interface,” IEEE Systems, Man, & Cybernetics Magaz., vol.2, no.1, pp.6-13, 2016.
  • [6] H. Tang, D.J. Beebe, “An oral tactile interface for blind navigation,” IEEE Trans On Neural Sys. and Rehab. Engin., vol.14, no.1, pp.116-123, 2006.
  • [7] X. Bao, J. Wang, J. Hu, “Method of individual identification based on electroencephalogram analysis,” Inter Conf on New Trends in Infor and Ser Sci. pp.390-393 (DOI: 10.1109/NISS.2009.44. 2009).
  • [8] K.J. Miller, P. Shenoy, M. Nijs, L.B. Sorensen, et.al,. ”Beyond the Gamma Band: The role of high-frequency features in movement classification,” IEEE Trans. on Biomed. Engin. vol.55, no.5, pp.1634-1637, 2008.
  • [9] D. Xiao, J. Hu, “Identification of motor imagery EEG signal,” Inter Conference on Biomedical Eng and Computer Science, 2010; Wuhan, China.
  • [10] B. Reuderink, M. Poel, A. Nijholt, “The impact of loss of control on movement BCIs,” IEEE Trans on Neural Syst. and Reha. Engin., vol.19, no.6, pp.628-637, 2011.
  • [11] X. Huo, J. Wang, M. Ghovanloo, “A magneto-inductive sensor based wireless tongue-computer interface,” IEEE Trans on Neural Syst. and Reha. Engin., vol.16, no.5, pp.497-504, 2008.
  • [12] R. Rupp, M. Rohm, M. Schneiders, A. Kreilinger, G.R. Müller-Putz. “Functional rehabilitation of the paralyzed upper extremity after spinal cord injury by noninvasive hybrid neuroprostheses,” Proceedings of the IEEE, vol.103, no.6, pp.954-968, 2015.
  • [13] L.M. Alonso-Valerdi, F. Sepulveda, “Development of a simulated living environment platform: Design of BCI assistive software and modelling of a virtual dwelling place,” Computer Aided Design, vol,54, pp.39-50, 2014.
  • [14] X. Huo, J. Wang, M. Ghovanloo, “Using magneto-inductive sensors to detect tongue position in a wireless assistive technology for people with severe disabilities,” IEEE Sensor Conf; 2007, Atlanta, USA.
  • [15] X. Huo, J. Wang, M. Ghovanloo, “A wireless tongue-computer interface using stereo differential magnetic field measurement,” Proceedings of the 29th Ann Inter Conf of the IEEE EMBS Cité Internationale, 2007, Lyon, France.
  • [16] X. Huo, J. Wang, M. Ghovanloo, “A magnetic wireless tongue-computer interface,” Proceed of the 3rd Inter IEEE EMBS Conf on Neural Engineering, 2007, Kohala Coast, Hawaii, USA.
  • [17] G. Krishnamurthy, M. Ghovanloo, “Tongue Drive: A tongue operated magnetic sensor based wireless assistive technology for people with severe disabilities,” IEEE Inter Sym on Circuits and Systems (ISCAS), pp.5551-5554, 2006.
  • [18] R. Vaidyanathan, B. Chung, L. Gupta et.al., “Tongue-movement communication and control concept for hands-free human–machine interfaces,” IEEE Trans. on Sys. Man and Cybernetics. vol.37, no.4, pp.533-546, 2007.
  • [19] R.Vaidyanathan, C.J. James, “Independent component analysis for extraction of critical features from tongue movement ear pressure signals,” Proceed of the 29th Ann Inter Conf of the IEEE EMBS Cité Internationale; 2007; Lyon, France.
  • [20] R. Vaidyanathan, L. Gupta, H. Kook, J. West, “A decision fusion classification architecture for mapping of tongue movements based on aural flow monitoring,” Proceed of the IEEE International Conference on Robotics and Automation, 2006; Orlando, Florida.
  • [21] R. Vaidyanathan, M. Fargues, L. Gupta et.al., “A dual-mode human-machine interface for robotic control based on acoustic sensitivity of the aural cavity,” IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob’06, 2006, Pisa, Italy.
  • [22] R. Vaidyanathan, H. Kook, L. Gupta, J. West, “Parametric and non-parametric signal analysis for mapping air flow in the ear-canal to tongue movements: A new strategy for hands-free human-machine interfaces,” IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings, 2004, Montreal, Canada.
  • [23] H. Jasper, “The ten twenty electrode system of the international federation,” Electro Clin Neuro., vol.10, no.2, pp.370-375, 1958.
  • [24] M.S. Bascil, A.Y. Tesneli, F. Temurtas, “Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN,” Australas Phys. Eng. Sci Med. vol. 39, no.3, pp.665-676, 2016.
  • [25] N. Yalcın, G. Tezel, C. Karakuzu, “Epilepsy diagnosis using artificial neural network learned by PSO,” Turk J. Elec. Eng & Comp. Sci. vol.23,pp.421-432, 2015.
  • [26] K.D. Desai, M.S. Sankhe, “A Real-Time Fetal ECG Feature Extraction Using Multiscale Discrete Wavelet Transform,” 5th Int Conf. on Biomedical Eng. and Infor., pp. 407-412, 2012.
  • [27] A. Hamad, E.H. Houssein, A.E. Hassanien, A.A. Fahmy, “Feature Extraction of Epilepsy EEG using Discrete Wavelet Transform,” 12th Int. Computer Engineering Conf., pp.109-195, 2016.
  • [28] T.K. Patel, P.C.Panda, S.C. Swain, “Mohanty SK. A Fault Detection Technique in Transmission Line By using Discrete Wavelet Transform,” 2nd Int. Conf. on Electrical, Computer and Communication Tech., 2017.
  • [29] E.J. Rechy-Ramirez, H. Hu, “Bio-signal based control in assistive robots: a survey,” Digital Communications and Networks, vol.1, no.2, pp.85-101, 2015.
  • [30] J.G. Proakis, D.G. Manolakis, “Digital signal processing principles, algorithms and applications,” 3rd edn Prentice-Hall, New York [chapter 12]; 1996.
  • [31] P. Stoica, R. Moses, “Spectral analysis of signals,” Prentice Hall International, New York. 2005.
  • [32] E. Alpaydın, “Introduction to Machine Learning,” MIT Press, Cambridge, Massachusetts, Second Edition. 2010.
  • [33] M. Kavita, M.R. Vargantwar, M.R. Sangita, “Classification of EEG using PCA, ICA and neural network,” Int. J. Eng. Adv. Technol., vol. 1, pp.1–4, 2011.
  • [34] R. Vigário, J. Särelä, V. Jousmäki, et.al. “Independent component approach to the analysis of EEG and MEG recordings,” IEEE Trans. on Biomed. Engin. vol.47, no.5, pp.589-593, 2000.
  • [35] R.Chai, R.G. Naik, N.T. Nguyen, et.al., “Selecting optimal EEG channels for mental tasks classification: An approach using ICA,” IEEE Congress on Evolutionary Computation (CEC), pp.1331-1335, 2016.
  • [36] B. Şen, M. Peker, “Novel approaches for automated epileptic diagnosis using fcbf selection and classification algorithms,” Turk J. Elec. Eng & Comp. Sci. vol.21, pp.2092-2109, 2013.
  • [37] R.A. Ramadan, A.V. Vasilakos, “Brain computer interface: control signals review,” Neurocomputing. vol.223, pp.26-44, 2017.
  • [38] B. Obermaier, C. Neuper, C. Guger, G. Pfurtscheller, “Information transfer rate in a five-classes brain–computer interface,” IEEE Trans. on Neural Syst. and Reha., vol.9, no. 3, pp.283-288, 2001.
  • [39] M. Sengelmann, A.K. Engel, A. Maye, “Maximizing information transfer in ssvep-based brain–computer interfaces,” IEEE Trans. on Biomedical Engin. vol.64, no.2, pp.381-394, 2017.
  • [40] B. Wang, C.M. Wong, F. Wan et.al., “Comparison of Different Classification Methods for EEG-Based Brain Computer Interfaces: A Case Study,” IEEE Int. Conf on Infor and Automation, Zhuhai/Maca, China, pp.1416-1421, 2009.
  • [41] K. Gorur, M.S. Bascil, M.R. Bozkurt, F. Temurtas, “Classification of Thyroid Data Using Decision Trees, kNN and SVM Methods,” International Artificial Intelligence and Data Processing Symposium, IDAP’16, Malatya, Turkey, pp. 130-134, 2016.
  • [42] Ö. Aydemir, T. Kayıkçıoğlu, “Investigation of the most appropriate mother wavelet for characterizing imaginary EEG signals used in BCI systems,” Turk J. Elec. Eng. & Comp. Sci. vol.24, pp.38-49, 2016.
  • [43] S. Vanhatalo, J. Voipio, A. Dewaraja, et.al., “Topography and elimination of slow EEG responses related to tongue movements,” NeuroImage, vol. 20, pp.1419-1423, 2003.
  • [44] Y. Nam, K. Bonkon, S. Choi, “Language-related glossokinetic potentials on scalp,” IEEE International conference on systems, Man, and Cybernetics, San Diego, USA, 2014.
  • [45] R. Leeb, F. Lee, C. Keinrath, R. Scherer, et.al., “Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment,” IEEE Trans. on Neural Syst. and Reha., vol.15, no.4, pp.473-481, 2007.
  • [46] V. Jayaram, M. Alamgir, Y. Altun, B. Schölkopf, “Grosse-Wentrup M. Transfer learning in brain-computer interfaces,” IEEE Computational Intelligence Magazine, pp.20-31, 2016.
  • [47] J.C. Kao, S.D. Stavisky, et.al., “Information systems opportunities in brain–machine interface decoders,”, Proceedings of the IEEE; vol.102, no.5, pp.666-682, 2014.
  • [48] A.B. Barreto, A.M. Taberner, L.M. Vicente, “Classification of spatio-temporal EEG readiness potentials towards the development of a brain-computer interface, bringing together education, science and technology,” Proceedings of the IEEE, Tampa, USA, 1996.
  • [49] S.Cerutti, “In the Spotlight: Biomedical signal processing,” IEEE Reviews In Biomedical Engin. vol.2, pp.9-11, 2009.
  • [50] X. Chen, C. He, J. Z .Wang et.al., “An IC-PLS framework for group corticomuscular coupling analysis,” IEEE Trans on Biomed Engin. vol.60, no.7, pp.2022-2033, 2013.
  • [51] J.J. Daly, Y. Fang, et.al., “Prolonged cognitive planning time, elevated cognitive effort, and relationship to coordination and motor control following stroke,” IEEE Trans on Neural Syst. and Reha. Engin. vol.14, no.2, pp.168-171, 2006.
  • [52] Y. Li, C. Guan, J. Qin, “Enhancing feature extraction with sparse component analysis for brain-computer interface,” Proceed. of the IEEE Engin. in Med. and Bio. 27th Annual Conference Shanghai, China, 2005.
  • [53] H.M. Genc, Z Cataltepe, T. Pearson, “A New PCA/ICA based feature selection method,” IEEE Signal Processing and Comm. App. 15th (SIU); 2007.
  • [54] M.J. McKeown, R. Saab, R. Abu-Gharbieh, “A combined independent component analysis (ICA)/ empirical mode decomposition (EMD) method to infer corticomuscular coupling,” IEEE Neural Engin Conf Proceed 2nd Int (EMBS), pp.1-8, 2005.
  • [55] K. Gorur, M.R. Bozkurt, M.S. Bascil, “Temurtas F. Glossokinetic potential based tongue–machine interface for 1-D extraction,” Australasian Physical & Engineering Sciences in Medicine, vol.41, no.2, pp.379-391, 2018. [56] K. Gorur, M.R. Bozkurt, M.S. Bascil, F. Temurtas, “Glossokinetic Potential Based Tongue–Machine Interface For 1-D Extraction Using Neural Networks,” Biocybernetics And Biomedical Engineering. Vol.38, No.3, pp.745-759, 2018.
  • [57] V.Schetinin, C. Maple, “A Bayesian Model Averaging Methodology For Detecting Eeg,” 15th International Conference On Digital Signal Processing, pp. 499-502, 2007.
  • [58] K. Gorur, M.R. Bozkurt, M.S. Bascil, F. Temurtas,” GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface”, Traitement du Signal, Vol:36, No:4, pp.319-329, 2019.
  • [59] O.Cetin, F. Temurtas, “Classification of Magnetoencephalography Signals Regarding Visual Stimuli by Generalized Regression Neural Network,” Dicle Med J.Vol.46, No.1, pp.19-25, 2019.
  • [60] K. Gorur, M.R. Bozkurt, M.S. Bascil, F. Temurtas,”Comparative Evaluation for PCA and ICA on Tongue-Machine Interface Using Glossokinetic Potential Responses”, Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Vol:16, No:1, pp.35-46, 2020.
Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Kutlucan Görür 0000-0003-3578-0150

Mehmet Recep Bozkurt 0000-0003-0673-4454

Muhammed Serdar Bascıl 0000-0002-6327-854X

Feyzullah Temurtas 0000-0002-3158-4032

Yayımlanma Tarihi 29 Ocak 2021
Gönderilme Tarihi 3 Temmuz 2019
Yayımlandığı Sayı Yıl 2021 Cilt: 9 Sayı: 1

Kaynak Göster

IEEE K. Görür, M. R. Bozkurt, M. S. Bascıl, ve F. Temurtas, “Tongue-Operated Biosignal over EEG and Processing with Decision Tree and kNN”, APJES, c. 9, sy. 1, ss. 112–125, 2021, doi: 10.21541/apjes.583049.
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