Prediction of the Following BCI Performance by Means of Spectral EEG Characteristics in the Prior Resting State |
Kang, Jae-Hwan
(동의대학교 인공지능 그랜드ICT 연구센터)
Kim, Sung-Hee (동의대학교 산업ICT기술공학과) Youn, Joosang (동의대학교 산업ICT기술공학과) Kim, Junsuk (동의대학교 산업ICT기술공학과) |
1 | P. Stegman, C. S. Crawford, M. Andujar, A. Nijholt, and J. E. Gilbert, "Brain-Computer Interface Software: A Review and Discussion," IEEE Transactions on Human-Machine Systems, Vol.50, No.2, pp.101-115, Feb. 2020. DOI |
2 | C. Vidaurre and B. Blankertz, "Towards a Cure for BCI Illiteracy," Brain Topography, Vol.23, No.2, pp.194-198, Nov. 2009. DOI |
3 | B. Blankertz, C. Sannelli, S. Halder, E. M. Hammer, A. Kubler, K.-R. Muller, G. Curio, and T. Dickhaus, "Neurophysiological Predictor of SMR-based BCI Performance," NeuroImage, Vol.51, No.4, pp.1303-1309, Jul. 2010. DOI |
4 | M. C. Thompson, "Critiquing the Concept of BCI Illiteracy," Science and Engineering Ethics, Vol.25, No.4, pp.1217-1233, Aug. 2018. DOI |
5 | C. Jeunet, E. Jahanpour and F. Lotte, "Why Standard Braincomputer Interface (BCI) Training Protocols Should be Changed: an Experimental Study," Journal of Neural Engineering, Vol. 13, No.3, pp.036024, Jun. 2016. DOI |
6 | A. Bamdadian, C. Guan, K. K. Ang and J. Xu, "The Predictive Role of Pre-cue EEG Rhythms on MI-based BCI Classification Performance," Journal of Neuroscience Methods, Vol.235, pp.138-144, Sep. 2014. DOI |
7 | E. M. Hammer, S. Halder, B. Blankertz, C. Sannelli, T. Dickhaus, S. Kleih, K.-R. Muller, and A. Kubler, "Psychological Predictors of SMR-BCI Performance," Biological Psychology, Vol.89, No.1, pp.80-86, Jan. 2012. DOI |
8 | M. Ahn, H. Cho, S. Ahn, and S. C. Jun, "High Theta and Low Alpha Powers May Be Indicative of BCI-Illiteracy in Motor Imagery," PLoS ONE, Vol.8, No.11, pp.e80886-11, Nov. 2013. DOI |
9 | M. Kwon, H. Cho, K. Won, M. Ahn, and S. C. Jun, "Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance," Electronics, Vol.9, No.4, pp.690-14, Apr. 2020. DOI |
10 | R. Zhang, P. Xu, R. Chen, F. Li, L. Guo, P. Li, T. Zhang, and D. Yao, "Predicting Inter-session Performance of SMR-Based Brain- Computer Interface Using the Spectral Entropy of Resting-State EEG," Brain Topography, Vol.28, No.5, pp.1-11, Apr. 2015. DOI |
11 | G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, "BCI2000: A General-Purpose BrainComputer Interface (BCI) System," IEEE Transactions on Biomedical Engineering, Vol.51, No.6, pp.1034-1043, Jun. 2004. DOI |
12 | V. J. Lawhern, A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, and B. J. Lance, "EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-computer Interfaces," Journal of Neural Engineering, Vol.15, No.5, pp.056013, Oct. 2018. DOI |
13 | J. L. Reichert, S. E. Kober, C. Neuper, and G. Wood, "Resting-state Sensorimotor Rhythm (SMR) Power Predicts the Ability to Up-regulate SMR in an EEG-instrumental Conditioning Paradigm," Clinical Neurophysiology, Vol. 126, No.11, pp.2068-2077, Nov. 2015 DOI |
14 | G. Muller-Putz, R. Scherer, and C. B. I. Journal, 2008, "Better than Random: A Closer Look on BCI Results," International Journal of Bioelectromagnetism, Vol.10, No.1, pp.52-55, Jan. 2008. |