Czasopismo
2023
|
R. 99, nr 6
|
196--203
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Ulepszona wydajność maszyny wektorów nośnych do klasyfikacji obrazów motorycznych EEG w oparciu o asymetrię różnicową
Języki publikacji
Abstrakty
One challenge in EEG motor imaging is th e low signal-to-noise ratio of brain signals. Its emergence in the accurate rendition of brain signals varies significantly from person to person. Here, we propose a framework to classify tasks based on fusion features using a Support Vector Machine. Our features are acquired from Discrete Wavelet Transform and Empirical Mode Decomposition. Subsequently, the disparity between measurements of left and right brain signals was calculated. Our proposed work significantly improves accuracy from 83.29 % to 93.16 % compared to previous work.
Jednym z wyzwań w obrazowaniu motorycznym EEG jest niski stosunek sygnału do szumu sygnałów mózgowych. Jego pojawienie się w dokładnym przekazywaniu sygnałów mózgowych różni się znacznie w zależności od osoby. Tutaj proponujemy ramy do klasyfikowania zadań w oparciu o funkcje fuzji przy użyciu maszyny wektorów nośnych. Nasze funkcje są uzyskiwane z dyskretnej transformacji falkowej i dekompozycji trybu empirycznego. Następnie obliczono rozbieżność między pomiarami sygnałów lewego i prawego mózgu. Nasza proponowana praca znacznie poprawia dokładność z 83,29% do 93,16% w porównaniu z poprzednią pracą.
Czasopismo
Rocznik
Tom
Strony
196--203
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
- Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, lianto15@mhs.ee.its.ac.id
- Universitas Katolik Soegijapranata
autor
- Department of Informatics, Universitas Darussalam Gontor, oddy@unida.gontor.ac.id
autor
- Department of Electrical Engineering, Institut Teknologi Telkom Surabaya, isa@ittelkom-sby.ac.id
autor
- Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, jono@bme.its.ac.id
autor
- Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, mochar@ee.its.ac.id
autor
- Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, hery@ee.its.ac.id
- University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS)
Bibliografia
- [1] M. Clerc, L. Bougrain, and F. Lotte, Brain-computer interfaces 1: foundations and methods. ISTE ; Wiley, 2016. OCLC: ocn969907619.
- [2] T. Prauzner, K. Prauzner, P. Ptak, H. Noga, P. Migo, and J. De pešová, “The influence of environmental conditions on the ac curacy of qeeg electroencephalography,” Przeglad Elektrotech niczny, vol. 96, pp. 86–89, 2020.
- [3] S. Paszkiel, R. Rojek, N. Lei, and M. A. Castro, “Review of so lutions for the application of example of machine learning meth ods for motor imagery in correlation with brain-computer inter faces,” Przeglad Elektrotechniczny, vol. 97, pp. 111–116, 2021.
- [4] M. Bentlemsan, E.-T. Zemouri, D. Bouchaffra, B. Yahya-Zoubir, and K. Ferroudji, “Random forest and filter bank common spatial patterns for eeg-based motor imagery classification,” pp. 235– 238, IEEE, 12 2014.
- [5] S.-L. Wu, Y.-T. Liu, T.-Y. Hsieh, Y.-Y. Lin, C.-Y. Chen, C.-H. Chuang, and C.-T. Lin, “Fuzzy integral with particle swarm opti mization for a motor-imagery-based brain–computer interface,” IEEE Transactions on Fuzzy Systems, vol. 25, pp. 21–28, 12 2017.
- [6] S.-M. Park, X. Yu, P. Chum, W.-Y. Lee, and K.-B. Sim, “Sym metrical feature for interpreting motor imagery eeg signals in the brain–computer interface,” Optik, vol. 129, pp. 163–171, 12 2017.
- [7] W.-Y. Hsu, “Motor imagery electroencephalogram analysis us ing adaptive neural-fuzzy classification,” International Journal of Fuzzy Systems, vol. 16, 2014.
- [8] M. ai Li, X. yong Luo, and J. fu Yang, “Extracting the nonlinear features of motor imagery eeg using parametric t-sne,” Neuro computing, vol. 218, pp. 371–381, 12 2016.
- [9] S. U. Kumar and H. H. Inbarani, “Pso-based feature selection and neighborhood rough set-based classification for bci multi class motor imagery task,” Neural Computing and Applications, vol. 28, pp. 3239–3258, 12 2017.
- [10] A. Subasi, A. Alkan, E. Koklukaya, and M. K. Kiymik, “Wavelet neural network classification of eeg signals by using ar model with mle preprocessing,” Neural Networks, vol. 18, pp. 985–997, 12 2005.
- [11] H. Baali, A. Khorshidtalab, M. Mesbah, and M. J. E. Salami, “A transform-based feature extraction approach for motor imagery tasks classification,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 3, pp. 1–8, 2015.
- [12] S. Theodoridis and K. Koutroumbas, Pattern recognition. Aca demic Press, 4th ed ed., 2009.
- [13] F. Ok and R. Rajesh, “Empirical mode decomposition of eegsignals for the effectual classification of seizures,” 12 2020.
- [14] V. Bajaj and R. B. Pachori, “Eeg signal classification using em pirical mode decomposition and support vector machine,” 2012. Series Title: Advances in Intelligent and Soft Computing.
- [15] A. Pradhan and M. I. of Technology-World Peace University,“A survey of classification of eeg signals using emd and vmd for epileptic seizure detection,” International Journal of Engineer ing Research and, vol. V9, p. IJERTV9IS050414, 12 2020.
- [16] N. Zhuang, Y. Zeng, L. Tong, C. Zhang, H. Zhang, and B. Yan, “Emotion recognition from eeg signals using multidimensional information in emd domain,” BioMed Research International, vol. 2017, pp. 1–9, 2017.
- [17] F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Ar naldi, “A review of classification algorithms for eeg-based brain–computer interfaces,” Journal of Neural Engineering, vol.4, p. R1–R13, 12 2007.
- [18] Q. Zhao, T. M. Rutkowski, L. Zhang, and A. Cichocki, “General ized optimal spatial filtering using a kernel approach with appli cation to eeg classification,” Cognitive Neurodynamics, vol. 4, pp. 355–358, 12 2010.
- [19] Y. Yamasari, A. Qoiriah, N. Rochmawati, I. M. Suartana, O. V. Putra, and A. I. Nurhidayat, “Exploring the kernel on svm to enhance the classification performance of students’ academic performance,” pp. 42–46, Institute of Electrical and Electronics Engineers (IEEE), 12 2022.
- [20] N. Z. Fanani, A. G. Sooai, K. Khamid, F. Y. Rahmanawati, A. Tormasi, L. T. Koczy, S. Sumpeno, and M. H. Purnomo, “Two stages outlier removal as pre-processing digitizer data on finemotor skills (fms) classification using covariance estimator and isolation forest,” International Journal of Intelligent Engineering and Systems, vol. 14, pp. 571–582, 12 2021.
- [21] S. Sanei and J. A. Chambers, EEG Signal Processing and Ma chine Learning. Wiley, 2nd ed., 2022.
- [22] G. K. Verma and U. S. Tiwary, “Multimodal fusion framework: A multiresolution approach for emotion classification and recogni tion from physiological signals,” NeuroImage, vol. 102, pp. 162– 172, 12 2014.
- [23] M. K. M. Rahman and M. A. M. Joadder, “A review on the com ponents of eeg-based motor imagery classification with quanti tative comparison,” Application and Theory of Computer Tech nology, vol. 2, p. 1, 12 2017.
- [24] Y. Weibo, “Eeg data of simple and compound limb motor im agery,” 2014.
- [25] Z. Tang, S. Sun, S. Zhang, Y. Chen, C. Li, and S. Chen, “Abrain-machine interface based on erd/ers for an upper-limb ex oskeleton control,” Sensors, vol. 16, p. 2050, 12 20
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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