Subject Areas : Image Processing
Masoud Sistaninezhad
1
,
Habib Rasi
2
,
Aliakbar Abdollahinezhad fard
3
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2 -
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Keywords:
Abstract :
[1] C. M. Yilmaz, “Classification of eeg-based motor imagery tasks using 2-d features and quasi-probabilistic distribution models,” Doctoral dissertation),, The Graduate School of Natural and Applied Sciences, Karadeniz Technical University, Trabzon, Turkey, 2021.
[2] X. An, D. Kuang, X. Guo, Y. Zhao, and L. He, “A deep learning method for classification of eeg data based on motor imagery,” in INTELLIGENT COMPUTING IN BIOINFORMATICS, ser. Lecture Notes in Bioinformatics, D. Huang, K. Han, and M. Gromiha, Eds., vol. 8590, 2014, pp. 203–210.
[3] C. Uyulan, “Development of lstm&cnn based hybrid deep learning model to classify motor imagery tasks,” COMMUNICATIONS IN MATHEMATICAL BIOLOGY AND NEUROSCIENCE, 2021.
[4] M. S. Gouri and K. S. V. Grace, “Lpoa-drn: Deep learning based feature fusion and optimization enabled deep residual network for classification of motor imagery eeg signals,” SIGNAL IMAGE AND VIDEO PROCESSING, vol. 17, no. 5, pp. 2167–2175, JUL 2023.
[5] D.-H. Kim, D.-H. Shin, and T.-E. Kam, “Bridging the bci illiteracy gap: a subject-to-subject semantic style transfer for eeg-based motor imagery classification,” FRONTIERS IN HUMAN NEUROSCIENCE, vol. 17, MAY 15 2023.
[6] M. Kaur, R. Upadhyay, and V. Kumar, “E-cnnet: Time-reassigned multisynchrosqueezing transform-based deep learning framework for mi-bci task classification,” INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2023 FEB 23 2023.
[7] F. M. Garcia-Moreno, M. Bermudez-Edo, M. Jose Rodriguez-Fortiz, and J. Luis Garrido, “A cnn-lstm deep learning classifier for motor imagery eeg detection using a low-invasive and low-cost bci headband,” in PROCEEDINGS OF THE 2020 16TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENVIRONMENTS (IE), ser. International Conference on Intelligent Environments, 2020, pp. 84 -91.
[8] Y. Kwak, W.-J. Song, and S.-E. Kim, “Fganet: fnirs-guided attention network for hybrid eeg-fnirs brain-computer interfaces,” IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, vol. 30, pp. 329–339, 2022.
[9] G. R. M.-P. A. S. R. Leeb, C. Brunner and G. Pfurtscheller, “Bci competition iv,” 2008. [Online]. Available: https://www.bbci.de/competition/iv/#datasets
[10] M. Tangermann, K.-R. Mueller, A. Aertsen, N. Birbaumer, C. Braun, C. Brunner, R. Leeb, C. Mehring, K. J. Miller, G. R. Mueller-Putz, G. Nolte, G. Pfurtscheller, H. Preissl, G. Schalk, A. Schoegl, C. Vidaurre, S. Waldert, and B. Blankertz, “Review of the bci competition iv,” FRONTIERS IN NEUROSCIENCE, vol. 6, 2012.
[11] G. Pfurtscheller and F. da Silva, “Event-related eeg/meg synchronization and desynchronization: basic principles,” CLINICAL NEUROPHYSIOLOGY, vol. 110, no. 11, pp. 1842–1857, NOV 1999.
[12] Y. R. Tabar and U. Halici, “A novel deep learning approach for classification of eeg motor imagery signals,” JOURNAL OF NEURAL ENGINEERING, vol. 14, no. 1, FEB 2017.
[13] M. Dai, D. Zheng, R. Na, S. Wang, and S. Zhang, “Eeg classification of motor imagery using a novel deep learning framework,” SENSORS, vol. 19, no. 3, FEB 1 2019.
[14] I. The MathWorks, “spectrogram,” 2023. [Online]. Available:https://www.mathworks.com/help/signal/ref/spectrogram.html
[15] Y. Han, B. Wang, J. Luo, L. Li, and X. Li, “A classification method for eeg motor imagery signals based on parallel convolutional neural network,” BIOMEDICAL SIGNAL PROCESSING AND CONTROL, vol. 71, no. B, JAN 2022.
[16] M. T. D. Nguyen, N. Y. Phan Xuan, B. M. Pham, H. T. M. Do, T. N. M. Phan, Q. T. T. Nguyen, A. H. L. Duong, V. K. Huynh, B. D. C. Hoang, and H. T. T. Ha, “Optimize temporal configuration for motor imagery-based multiclass performance and its relationship with subject-specific frequency,” Informatics in Medicine Unlocked, vol. 36, p. 101141, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2352914822002787
[17] Z. Chen, Y. Wang, and Z. Song, “Classification of motor imagery electroencephalography signals based on image processing method,” SENSORS, vol. 21, no. 14, JUL 2021.
[18] Z. Dokur and T. Olmez, “Classification of motor imagery electroencephalogram signals by using a divergence based convolutional neural network,” APPLIED SOFT COMPUTING, vol. 113, no. A, DEC 2021.
[19] M. H. Annaby, M. H. Said, A.M. Eldeib, M. A. Rushdi, “EEG-based motor imagery classification using digraph Fourier transforms and extreme learning machines”, Biomedical Signal Processing and Control, Volume 69, August 2021, 102831.
