EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 140 (2017) MATEC Web Conf., 140 (2017) 01024 References
Open Access
Issue
MATEC Web Conf.
Volume 140, 2017
2017 International Conference on Emerging Electronic Solutions for IoT (ICEESI 2017)
Article Number 01024
Number of page(s) 6
DOI https://doi.org/10.1051/matecconf/201714001024
Published online 11 December 2017
  1. S. A. Sprague, M. T. McBee, E. W. Sellers, The effects of working memory on brain-computer interface performance, Clin. Neurophysiol. 127, 1331–1341 (2016) [CrossRef] [Google Scholar]
  2. X. Tang, N. Zhang, J. Zhou, Q. Liu, Hidden-layer visible deep stacking network optimized by PSO for motor imagery EEG recognition, Neurocomputing 234, 1–10 (2016) [CrossRef] [Google Scholar]
  3. Denis R. de Moraes Piazentin and J. L. G. Rosa, Motor imagery classification for Brain-Computer Interfaces through a chaotic neural network through a chaotic neural network, Int. Jt. Conf. Neural Networks (2014) [Google Scholar]
  4. M. E. A. Abdel-Hadi, R. A. El-Khoribi, M. I. Shoman, M. M. Refaey, Classification of motor imagery tasks with LS-SVM in EEG-based self-paced BC, 5th Int. Conf. Digit. Inf. Process. Commun. (CDIPC), 244–249 (2015) [Google Scholar]
  5. M. Ming, L. Shaona, M. Haitao, M. Yuliang, G. Yunyuan, Feature extraction method of motor imagery EEG based on DTCWT sample entropy, 34th Chinese Control Conference (CCC), 3964–3968 (2015) [CrossRef] [Google Scholar]
  6. J. Hong, X. Qin, J. Bai, P. Zhang, Y. Cheng, A combined feature extraction method for left-right hand motor imagery in BCI, IEEE Int. Conf. on Mechatronics and Automation (ICMA), 2621–2625 (2015) [Google Scholar]
  7. Z. Qiu, J. Jin, H. K. Lam, Y. Zhang, X. Wang, A. Cichocki, Improved SFFS method for channel selection in motor imagery based BCI, Neurocomputing (2015) [Google Scholar]
  8. B. Yang, H. Li, Q. Wang, Y. Zhang, Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces, Comput. Methods Programs Biomed. 129, 21–28 (2016) [CrossRef] [Google Scholar]
  9. J. Birjandtalab, M. B. Pouyan, D. Cogan, M. Nourani, J. Harvey, Automated seizure detection using limited-channel EEG and non-linear dimension reduction, Computers in Biology and Medicine 82, 49-58 (2017) [CrossRef] [Google Scholar]
  10. J. Kevric, A. Subasi, Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system, Biomedical Signal Processing and Control 31, 398-406 (2017) [CrossRef] [Google Scholar]
  11. T. Kayikcioglu, O. Aydemir, A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data, Pattern Recognit. Lett. 31, 1207–1215 (2010) [CrossRef] [Google Scholar]
  12. J. Fan, J. W. Wade, D. Bian, A. P. Key, Z. E. Warren, L. C. Mion, N Sarkar, A Step towards EEG-based brain computer interface for autism intervention, Proceedings of the Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBS), 3767–3770 (2015) [Google Scholar]
  13. K. Sabancı, M. Köklü, The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals, Int. J. Intell. Syst. Appl. Eng. 3, 127 (2015) [CrossRef] [Google Scholar]
  14. A. Raheel, S. M. Anwar, M. Majid, B. Khan, Ehatisham-ul-Haq. Real time text speller based on eye movement classification using wearable EEG sensors, Proceedings of 2016 SAI Computing Conf., 161–164 (2016) [CrossRef] [Google Scholar]
  15. R. Aldea, M. Fira, A. Lazǝr, Classifications of motor imagery tasks using k-nearest neighbors, 12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014, 115–120 (2015) [Google Scholar]
  16. F. A. Mousa, R. A. El-Khoribi, M. E. Shoman, An integrated classification method for brain computer interface system, 5th Int. Conf. on Digit. Inf. Process. and Commun., ICDIPC 2015, 141–146 (2015) [Google Scholar]
  17. M. Maleki, T. Kayıkçıoğlu, Classification of EEG signal during gaze on the different rotating vanes, 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings, 593–596 (2016) [CrossRef] [Google Scholar]
  18. J. T. Oliva, J. L. G. Rosa, How an epileptic EEG segment, used as reference, can influence a cross-correlation classifier?, Applied Intelligence, 1-19 (2017) [Google Scholar]
  19. S. Raschka, Python Machine Learning. (Packt Publishing Ltd., (2016) [Google Scholar]
  20. R. Bose, A. Khasnobish, S. Bhaduri, D. N. Tibarewala, Performance Analysis of Left and Right Lower Limb Movement Classification from EEG, 3rd Int. Conf. on Signal Processing and Integrated Networks (SPIN), 174–179 (2016) [Google Scholar]
  21. M. Mohammadpour, M. Ghorbanian, S. Mozaffari, Comparison of EEG Signal Features and Ensemble Learning Methods for Motor Imagery Classification, 8th Int. Conf. on Information and Knowledge Technology (IKT), Hamedan, Iran, 288–292 (2016) [Google Scholar]
  22. K. Chomboon, P. Chujai, P. Teerarassammee, K. Kerdprasop, N. Kerdprasop, An Empirical Study of Distance Metrics for k-Nearest Neighbor Algorithm, The Proceedings of the 2nd Int. Conf. on Industrial Application Engineering 2015, 280–285 (2015) [CrossRef] [Google Scholar]
  23. B. Blankertz, G. Dornhege, M. Krauledat, K. R. Müller, G. Curio, The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects, Neuroimage 37, 539–550 (2007) [CrossRef] [Google Scholar]
  24. M. Tangermann, K. R. Müller, A. Aertsen, N. Birbaumer, C. Braun, C. Brunner, R. Leeb, C. Mehring, K. J. Miller, G. R. Müller-Putz, G. Nolte, G. Pfurtsscheller, H. Preissl, G. Schalk, A. Schlögl, C. Vidaurre, S. Waldert, B. Blankertz, Review of the BCI competition IV. Frontiers in Neuroscience (2012) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.