MATEC Web Conf.
Volume 160, 2018International Conference on Electrical Engineering, Control and Robotics (EECR 2018)
|Number of page(s)||5|
|Section||Information Science and Engineering|
|Published online||09 April 2018|
- A. Karagiannis, P. Constantinou, “Noise-Assisted Data Processing With Empirical Mode Decomposition in Biomedical Signals.” IEEE Engineering in Medicine & Biology Society, 2011, 15(1):11-8. [Google Scholar]
- M. Boersma, D. Smit, H. Bie, et al. “Network analysis of resting state EEG in the developing young brain: structure comes with maturation.” Human Brain Mapping, 2015, 32(3):413-425. [CrossRef] [Google Scholar]
- E. Kalunga, S. Chevallier, Q. Barthélemy, et al. “Online SSVEP-based BCI using Riemannian geometry”. Neurocomputing, 2016, 191:55-68. [CrossRef] [Google Scholar]
- E. Lukhanina, N. Mel’Nik, N.Berezetskaya, et al. “Co-rrelations between indices of P300 EEG potential, cognitive tests, and variational pulsometry in Parkin-sonian patients.” Neurophysiology, 2008, 40(1):39-47. [CrossRef] [Google Scholar]
- S. Pittaccio, F. Zappasodi, S. Viscuso, et al. “Primary sensory and motor cortex activities during voluntary and passive ankle mobilization by the SHADE orthosis. ”Human Brain Mapping, 2011, 32(1):60-70. [CrossRef] [Google Scholar]
- A. Bamdadian, C. Guan, K. Kai, et al. “Towards im-provement of MI-BCI performance of subjects with BCI deficiency.” International Ieee/embs Conference on Neural Engineering. IEEE, 2015:17-20. [Google Scholar]
- S. Huang, X. Wu.“Feature Extraction of EEG Signals Based on Mu/Beta Rhythm Imaging.” Guangzhou: School of Bioscience and Engineering, South China University of Technology, 2010. [Google Scholar]
- Çınar, Salim, N. Acır . “A novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysis”. Pergamon Press, Inc. 2017. [Google Scholar]
- D. Toledo, G. Manzano, J. Barela, et al. “Cortical corr-elates of response time slowing in older adults: ERP and ERD/ERS analyses during passive ankle movement.” 2016, 127(1):655-663. [Google Scholar]
- L. Zhao, H. Shen, S. Cui,et al. “Research of Brain-Computer Interface Technology Based on Labview.” IEEE Computer Society, 2008:72-80. [Google Scholar]
- H. Sun, Y. Xiang, Y. Sun,et al. “On-line EEG classifica-tion for brain-computer interface based on CSP and SVM.” International Congress on Image and Signal Processing. IEEE, 2010:4105-4108. [CrossRef] [Google Scholar]
- N. Zhang, X. Tang, Q. Liu. “Feature Extraction and Recognition of EEG Based on Semi-supervised Learning.” Engineering Science Edition, 2017(s2): 230-237. [Google Scholar]
- H. Lee, A. Cichocki, S. Choi. “Nonnegative Matrix Factorization for Motor Imagery EEG Classifica-tion.” Springer Berlin Heidelberg, 2006:250-259. [Google Scholar]
- M. Liu, J. Wang, C. Zheng, et al. Attention-related EEG features extracted by nonnegative matrix factorization [J]. Chinese Journal of Biophysics, 2006,22 (1): 67-72. [Google Scholar]
- S. Duan, Y. Shang, L. Pan. “Feature Extraction and Classification of EEG Signals in Multi-Kinematics Imaging.” Computer Measurement and Control, 2016,24 (2): 283-287. [Google Scholar]
- D. D. Lee, H. S. Seung. “Algorithms for nonnegative matrix factorization.” Advances in Neural Informa-tion Processing Systems, 2001, 13(6):556--562. [Google Scholar]
- B. Blankertz, K. Müller, G. Curio, et al. “The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.” IEEE Trans. biomedical engineering, 2004, 51(6):1044-51. [CrossRef] [Google Scholar]
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