Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 27 Sayı: 2, 259 - 270, 30.04.2023
https://doi.org/10.16984/saufenbilder.1190493

Öz

Destekleyen Kurum

Selçuk Üniversitesi Öğretim Görevlisi Yetiştirme Programı Koordinatörlüğü

Proje Numarası

2017-ÖYP-045

Kaynakça

  • [1] S. Kumar, A. Sharma, T. Tsunoda, "An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information," BMC bioinformatics, vol. 18, no. 16, pp. 125-137, 2017.
  • [2] L. Yang, Y. Song, K. Ma, L. Xie, "Motor imagery EEG decoding method based on a discriminative feature learning strategy," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 368-379, 2021.
  • [3] D. Y. Lee, J. H. Jeong, B. H. Lee, S. W. Lee, "Motor Imagery Classification Using Inter-Task Transfer Learning via a Channel-Wise Variational Autoencoder-Based Convolutional Neural Network," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 226-237, 2022.
  • [4] X. Zhu, P. Li, C. Li, D. Yao, R. Zhang, P. Xu, "Separated channel convolutional neural network to realize the training free motor imagery BCI systems," Biomedical Signal Processing and Control, vol. 49, pp. 396-403, 2019.
  • [5] G. Xu, X. Shen, S. Chen, Y. Zong, C. Zhang, H. Yue, M. Liu, F. Chen, W. Che, "A deep transfer convolutional neural network framework for EEG signal classification," IEEE Access, vol. 7, pp. 112767-112776, 2019.
  • [6] X. Zhao, H. Zhang, G. Zhu, F. You, S. Kuang, L. Sun, "A multi-branch 3D convolutional neural network for EEG-based motor imagery classification," IEEE transactions on neural systems and rehabilitation engineering, vol. 27, no. 10, pp. 2164-2177, 2019.
  • [7] K. W. Ha, J. W. Jeong, "Decoding two-class motor imagery EEG with capsule networks," in 2019 IEEE International Conference on Big Data and Smart Computing, 2019: IEEE, pp. 1-4.
  • [8] Z. Jin, G. Zhou, D. Gao, Y. Zhang, "EEG classification using sparse Bayesian extreme learning machine for brain–computer interface," Neural Computing and Applications, vol. 32, no. 11, pp. 6601-6609, 2020.
  • [9] O. Y. Kwon, M. H. Lee, C. Guan, S. W. Lee, "Subject-independent brain–computer interfaces based on deep convolutional neural networks," IEEE transactions on neural networks and learning systems, vol. 31, no. 10, pp. 3839-3852, 2019.
  • [10] K. Zhang, N. Robinson, S. W. Lee, C. Guan, "Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network," Neural Networks, vol. 136, pp. 1-10, 2021.
  • [11] S. Pérez-Velasco, E. Santamaria-Vazquez, V. Martinez-Cagigal, D. Marcos-Martinez, R. Hornero, "EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1766-1775, 2022.
  • [12] I. Dolzhikova, B. Abibullaev, R. Sameni, A. Zollanvari, "Subject-Independent Classification of Motor Imagery Tasks in EEG Using Multisubject Ensemble CNN," IEEE Access, vol. 10, pp. 81355-81363, 2022.
  • [13] H. Raza, H. Cecotti, Y. Li, G. Prasad, "Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface," Soft Computing, vol. 20, no. 8, pp. 3085-3096, 2016.
  • [14] X. Xie, Z. L. Yu, H. Lu, Z. Gu, Y. Li, "Motor imagery classification based on bilinear sub-manifold learning of symmetric positive-definite matrices," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 6, pp. 504-516, 2016.
  • [15] S. Sakhavi, C. Guan, S. Yan, "Learning temporal information for brain-computer interface using convolutional neural networks," IEEE transactions on neural networks and learning systems, vol. 29, no. 11, pp. 5619-5629, 2018.
  • [16] R. Fu, Y. Tian, T. Bao, Z. Meng, P. Shi, "Improvement motor imagery EEG classification based on regularized linear discriminant analysis," Journal of medical systems, vol. 43, no. 6, pp. 1-13, 2019.
  • [17] Y. You, W. Chen, T. Zhang, "Motor imagery EEG classification based on flexible analytic wavelet transform," Biomedical Signal Processing and Control, vol. 62, p. 102069, 2020.
  • [18] Y. Liang, Y. Ma, "Calibrating EEG features in motor imagery classification tasks with a small amount of current data using multisource fusion transfer learning," Biomedical Signal Processing and Control, vol. 62, p. 102101, 2020.
  • [19] S. Afrakhteh, M. R. Mosavi, "Applying an efficient evolutionary algorithm for EEG signal feature selection and classification in decision-based systems," in Energy efficiency of medical devices and healthcare applications: Elsevier, 2020, pp. 25-52.
  • [20] D. R. Edla, M. F. Ansari, N. Chaudhary, S. Dodia, "Classification of facial expressions from eeg signals using wavelet packet transform and svm for wheelchair control operations," Procedia computer science, vol. 132, pp. 1467-1476, 2018.
  • [21] K. W. Ha, J. W. Jeong, "Motor imagery EEG classification using capsule networks," Sensors, vol. 19, no. 13, p. 2854, 2019.
  • [22] M. Z. Yusoff, N. Kamel, A. Malik, M. Meselhy, "Mental task motor imagery classifications for noninvasive brain computer interface," in 2014 5th International Conference on Intelligent and Advanced Systems, 2014: IEEE, pp. 1-5.
  • [23] S. Tiwari, S. Goel, A. Bhardwaj, "MIDNN-a classification approach for the EEG based motor imagery tasks using deep neural network," Applied Intelligence, pp. 1-20, 2021.
  • [24] S. D. Muthukumaraswamy, "High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations," Frontiers in human neuroscience, vol. 7, p. 138, 2013.
  • [25] M. H. Alomari, E. A. Awada, A. Samaha, K. Alkamha, "Wavelet-based feature extraction for the analysis of EEG signals associated with imagined fists and feet movements," Computer and Information Science, vol. 7, no. 2, p. 17, 2014.
  • [26] N. E. Huang, Z. Wu, "A review on Hilbert‐Huang transform: Method and its applications to geophysical studies," Reviews of geophysics, vol. 46, no. 2, 2008.
  • [27] K. Dragomiretskiy, D. Zosso, "Variational mode decomposition," IEEE transactions on signal processing, vol. 62, no. 3, pp. 531-544, 2013.
  • [28] B. Hjorth, "EEG analysis based on time domain properties," Electroencephalography and clinical neurophysiology, vol. 29, no. 3, pp. 306-310, 1970.
  • [29] J. Istas, G. Lang, "Quadratic variations and estimation of the local Hölder index of a Gaussian process," in Annales de l'Institut Henri Poincare (B) probability and statistics, 1997, vol. 33, no. 4: Elsevier, pp. 407-436.
  • [30] Y. Ma, W. Shi, C. K. Peng, A. C. Yang, "Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches," Sleep medicine reviews, vol. 37, pp. 85-93, 2018.
  • [31] A. S. Ashour, Y. Guo, A. R. Hawas, G. Xu, "Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images," Health information science and systems, vol. 6, no. 1, pp. 1-10, 2018.
  • [32] I. Hossain, A. Khosravi, S. Nahavandhi, "Active transfer learning and selective instance transfer with active learning for motor imagery based BCI," in 2016 International Joint Conference on Neural Networks, 2016: IEEE, pp. 4048-4055.
  • [33] The Mathworks Inc. (2022, Oct. 10). Feature Selection [Online]. Available:https://www.mathworks.com/discovery/feature-selection.html

Feature Analysis for Motor Imagery EEG Signals with Different Classification Schemes

Yıl 2023, Cilt: 27 Sayı: 2, 259 - 270, 30.04.2023
https://doi.org/10.16984/saufenbilder.1190493

Öz

A Brain-Computer Interface (BCI) is a communication system that decodes and transfers information directly from the brain to external devices. The electroencephalogram (EEG) technique is used to measure the electrical signals corresponding to commands occurring in the brain to control functions. The signals used for control applications in BCI are called Motor Imagery (MI) EEG signals. EEG signals are noisy, so it is important to use the right methods to recognize patterns correctly. This study examined the performances of different classification schemes to train networks using Ensemble Subspace Discriminant classifier. Also, the most efficient feature space was found using Neighborhood Component Analysis. The maximum average accuracy in classifying MI signals corresponding to right-direction and left-direction was 80.4% with a subject-specific classification scheme and 250 features.

Proje Numarası

2017-ÖYP-045

Kaynakça

  • [1] S. Kumar, A. Sharma, T. Tsunoda, "An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information," BMC bioinformatics, vol. 18, no. 16, pp. 125-137, 2017.
  • [2] L. Yang, Y. Song, K. Ma, L. Xie, "Motor imagery EEG decoding method based on a discriminative feature learning strategy," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 368-379, 2021.
  • [3] D. Y. Lee, J. H. Jeong, B. H. Lee, S. W. Lee, "Motor Imagery Classification Using Inter-Task Transfer Learning via a Channel-Wise Variational Autoencoder-Based Convolutional Neural Network," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 226-237, 2022.
  • [4] X. Zhu, P. Li, C. Li, D. Yao, R. Zhang, P. Xu, "Separated channel convolutional neural network to realize the training free motor imagery BCI systems," Biomedical Signal Processing and Control, vol. 49, pp. 396-403, 2019.
  • [5] G. Xu, X. Shen, S. Chen, Y. Zong, C. Zhang, H. Yue, M. Liu, F. Chen, W. Che, "A deep transfer convolutional neural network framework for EEG signal classification," IEEE Access, vol. 7, pp. 112767-112776, 2019.
  • [6] X. Zhao, H. Zhang, G. Zhu, F. You, S. Kuang, L. Sun, "A multi-branch 3D convolutional neural network for EEG-based motor imagery classification," IEEE transactions on neural systems and rehabilitation engineering, vol. 27, no. 10, pp. 2164-2177, 2019.
  • [7] K. W. Ha, J. W. Jeong, "Decoding two-class motor imagery EEG with capsule networks," in 2019 IEEE International Conference on Big Data and Smart Computing, 2019: IEEE, pp. 1-4.
  • [8] Z. Jin, G. Zhou, D. Gao, Y. Zhang, "EEG classification using sparse Bayesian extreme learning machine for brain–computer interface," Neural Computing and Applications, vol. 32, no. 11, pp. 6601-6609, 2020.
  • [9] O. Y. Kwon, M. H. Lee, C. Guan, S. W. Lee, "Subject-independent brain–computer interfaces based on deep convolutional neural networks," IEEE transactions on neural networks and learning systems, vol. 31, no. 10, pp. 3839-3852, 2019.
  • [10] K. Zhang, N. Robinson, S. W. Lee, C. Guan, "Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network," Neural Networks, vol. 136, pp. 1-10, 2021.
  • [11] S. Pérez-Velasco, E. Santamaria-Vazquez, V. Martinez-Cagigal, D. Marcos-Martinez, R. Hornero, "EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1766-1775, 2022.
  • [12] I. Dolzhikova, B. Abibullaev, R. Sameni, A. Zollanvari, "Subject-Independent Classification of Motor Imagery Tasks in EEG Using Multisubject Ensemble CNN," IEEE Access, vol. 10, pp. 81355-81363, 2022.
  • [13] H. Raza, H. Cecotti, Y. Li, G. Prasad, "Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface," Soft Computing, vol. 20, no. 8, pp. 3085-3096, 2016.
  • [14] X. Xie, Z. L. Yu, H. Lu, Z. Gu, Y. Li, "Motor imagery classification based on bilinear sub-manifold learning of symmetric positive-definite matrices," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 6, pp. 504-516, 2016.
  • [15] S. Sakhavi, C. Guan, S. Yan, "Learning temporal information for brain-computer interface using convolutional neural networks," IEEE transactions on neural networks and learning systems, vol. 29, no. 11, pp. 5619-5629, 2018.
  • [16] R. Fu, Y. Tian, T. Bao, Z. Meng, P. Shi, "Improvement motor imagery EEG classification based on regularized linear discriminant analysis," Journal of medical systems, vol. 43, no. 6, pp. 1-13, 2019.
  • [17] Y. You, W. Chen, T. Zhang, "Motor imagery EEG classification based on flexible analytic wavelet transform," Biomedical Signal Processing and Control, vol. 62, p. 102069, 2020.
  • [18] Y. Liang, Y. Ma, "Calibrating EEG features in motor imagery classification tasks with a small amount of current data using multisource fusion transfer learning," Biomedical Signal Processing and Control, vol. 62, p. 102101, 2020.
  • [19] S. Afrakhteh, M. R. Mosavi, "Applying an efficient evolutionary algorithm for EEG signal feature selection and classification in decision-based systems," in Energy efficiency of medical devices and healthcare applications: Elsevier, 2020, pp. 25-52.
  • [20] D. R. Edla, M. F. Ansari, N. Chaudhary, S. Dodia, "Classification of facial expressions from eeg signals using wavelet packet transform and svm for wheelchair control operations," Procedia computer science, vol. 132, pp. 1467-1476, 2018.
  • [21] K. W. Ha, J. W. Jeong, "Motor imagery EEG classification using capsule networks," Sensors, vol. 19, no. 13, p. 2854, 2019.
  • [22] M. Z. Yusoff, N. Kamel, A. Malik, M. Meselhy, "Mental task motor imagery classifications for noninvasive brain computer interface," in 2014 5th International Conference on Intelligent and Advanced Systems, 2014: IEEE, pp. 1-5.
  • [23] S. Tiwari, S. Goel, A. Bhardwaj, "MIDNN-a classification approach for the EEG based motor imagery tasks using deep neural network," Applied Intelligence, pp. 1-20, 2021.
  • [24] S. D. Muthukumaraswamy, "High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations," Frontiers in human neuroscience, vol. 7, p. 138, 2013.
  • [25] M. H. Alomari, E. A. Awada, A. Samaha, K. Alkamha, "Wavelet-based feature extraction for the analysis of EEG signals associated with imagined fists and feet movements," Computer and Information Science, vol. 7, no. 2, p. 17, 2014.
  • [26] N. E. Huang, Z. Wu, "A review on Hilbert‐Huang transform: Method and its applications to geophysical studies," Reviews of geophysics, vol. 46, no. 2, 2008.
  • [27] K. Dragomiretskiy, D. Zosso, "Variational mode decomposition," IEEE transactions on signal processing, vol. 62, no. 3, pp. 531-544, 2013.
  • [28] B. Hjorth, "EEG analysis based on time domain properties," Electroencephalography and clinical neurophysiology, vol. 29, no. 3, pp. 306-310, 1970.
  • [29] J. Istas, G. Lang, "Quadratic variations and estimation of the local Hölder index of a Gaussian process," in Annales de l'Institut Henri Poincare (B) probability and statistics, 1997, vol. 33, no. 4: Elsevier, pp. 407-436.
  • [30] Y. Ma, W. Shi, C. K. Peng, A. C. Yang, "Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches," Sleep medicine reviews, vol. 37, pp. 85-93, 2018.
  • [31] A. S. Ashour, Y. Guo, A. R. Hawas, G. Xu, "Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images," Health information science and systems, vol. 6, no. 1, pp. 1-10, 2018.
  • [32] I. Hossain, A. Khosravi, S. Nahavandhi, "Active transfer learning and selective instance transfer with active learning for motor imagery based BCI," in 2016 International Joint Conference on Neural Networks, 2016: IEEE, pp. 4048-4055.
  • [33] The Mathworks Inc. (2022, Oct. 10). Feature Selection [Online]. Available:https://www.mathworks.com/discovery/feature-selection.html
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka, Yazılım Testi, Doğrulama ve Validasyon, Yazılım Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Esra Kaya 0000-0003-1401-9071

Ismail Sarıtas 0000-0002-5743-4593

Proje Numarası 2017-ÖYP-045
Yayımlanma Tarihi 30 Nisan 2023
Gönderilme Tarihi 17 Ekim 2022
Kabul Tarihi 10 Ocak 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 27 Sayı: 2

Kaynak Göster

APA Kaya, E., & Sarıtas, I. (2023). Feature Analysis for Motor Imagery EEG Signals with Different Classification Schemes. Sakarya University Journal of Science, 27(2), 259-270. https://doi.org/10.16984/saufenbilder.1190493
AMA Kaya E, Sarıtas I. Feature Analysis for Motor Imagery EEG Signals with Different Classification Schemes. SAUJS. Nisan 2023;27(2):259-270. doi:10.16984/saufenbilder.1190493
Chicago Kaya, Esra, ve Ismail Sarıtas. “Feature Analysis for Motor Imagery EEG Signals With Different Classification Schemes”. Sakarya University Journal of Science 27, sy. 2 (Nisan 2023): 259-70. https://doi.org/10.16984/saufenbilder.1190493.
EndNote Kaya E, Sarıtas I (01 Nisan 2023) Feature Analysis for Motor Imagery EEG Signals with Different Classification Schemes. Sakarya University Journal of Science 27 2 259–270.
IEEE E. Kaya ve I. Sarıtas, “Feature Analysis for Motor Imagery EEG Signals with Different Classification Schemes”, SAUJS, c. 27, sy. 2, ss. 259–270, 2023, doi: 10.16984/saufenbilder.1190493.
ISNAD Kaya, Esra - Sarıtas, Ismail. “Feature Analysis for Motor Imagery EEG Signals With Different Classification Schemes”. Sakarya University Journal of Science 27/2 (Nisan 2023), 259-270. https://doi.org/10.16984/saufenbilder.1190493.
JAMA Kaya E, Sarıtas I. Feature Analysis for Motor Imagery EEG Signals with Different Classification Schemes. SAUJS. 2023;27:259–270.
MLA Kaya, Esra ve Ismail Sarıtas. “Feature Analysis for Motor Imagery EEG Signals With Different Classification Schemes”. Sakarya University Journal of Science, c. 27, sy. 2, 2023, ss. 259-70, doi:10.16984/saufenbilder.1190493.
Vancouver Kaya E, Sarıtas I. Feature Analysis for Motor Imagery EEG Signals with Different Classification Schemes. SAUJS. 2023;27(2):259-70.

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