1 |
WOLPAWJR, BIRBAUMERN, MCFARLANDDJ, et al. Brain-computer interface for communication and control[J]. Clinical Neurophysiology, 2002, 113(6): 767⁃791.
|
2 |
GOMEZ-PILARJ, CORRALEJOR, NICOLAS-ALONSOLF. Neurofeedback training with a motor imagery-based BCI: Neurocognitive improvements and EEG changes in the elderly[J]. Medical & Biological Engineering & Computing, 2016, 54(11): 1655⁃1666.
|
3 |
HAMEDIM, SALLEHS H, NOORA M. Electroencephalographic Motor imagery brain connectivity analysis for BCI: A review[J]. Neural Computation, 2016, 28(6):1⁃43.
|
4 |
RODRIGUESP G, FILHOCAS, ATTUXR, et al. Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces[J]. Medical & Biological Engineering & Computing, 2019, 57: 1709⁃1725.
|
5 |
张娜, 孙炎珺, 李明爱. 一种个性化动态脑功能网络的构建与特征提取方法[J]. 北京生物医学工程, 2020, 39(6): 551⁃560.
|
|
ZHANGN, SUNYJ, LIMA. A personalized dynamic brain functional network and feature extraction [J]. Beijing Biomedical Engineering, 2020, 39(6): 551⁃560. (in Chinese)
|
6 |
GAOZ, WANGZ, MAC, et al. A wavelet time-frequency representation based complex network method for characterizing brain activities underlying motor imagery signals[J]. IEEE Access, 2018, 6: 65796⁃65802.
|
7 |
彭丝雨, 周到, 张家琦, 王宇, 高军峰. 基于互信息的脑网络及测谎研究[J]. 电子学报, 2019, 47(7): 1551⁃1556.
|
|
PENGS Y, ZHOUD, ZHANGJ Q, WANGY, GAOJ F. Research on mutual information-based brain network and lie detection[J]. Acta Electronica Sinica, 2019, 47(7): 1551⁃1556. (in Chinese)
|
8 |
CHUNGY G, KIMM K, KIMS P. Inter-channel connectivity of motor imagery EEG signals for a noninvasive BCI application[C]//IEEE International Workshop on Pattern Recognition in Neuroimaging. Cambridge: ACM, 2011: 49⁃52.
|
9 |
GONGA, LIUJ, CHENS, et al. Time-frequency cross mutual information analysis of the brain functional networks underlying multiclass motor imagery[J]. Journal of Motor Behavior, 2018, 50(3): 254⁃267.
|
10 |
FILHOC A S, ATTUXR, CASTELLANOG. EEG sensorimotor rhythms' variation and functional connectivity measures during motor imagery: Linear relations and classification approaches[J]. PeerJ, 2017, 5: 3983.
|
11 |
HUS, WANGH, ZHANGJ, et al. Causality from Cz to C3/C4 or between C3 and C4 revealed by granger causality and new causality during motor imagery[C]//International Joint Conference on Neural Networks. Beijing: IEEE, 2014: 3178⁃3185.
|
12 |
GHOSHP, MAZUMDERA, BHATTACHARYYAS, et al. Functional connectivity analysis of motor imagery EEG signal for brain-computer interfacing application[C]//2015 7th International IEEE/EMBS Conference on Neural Engineering. Montpellier, France: IEEE Press, 2015:210⁃213.
|
13 |
BAIGM Z, KAVAKLIM. Connectivity analysis using functional brain networks to evaluate cognitive activity during 3D modelling[J]. Brain Sciences, 2019, 9(2): 24.
|
14 |
STANIEKM, LEHNERTZK. Symbolic transfer entropy[J]. Physical Review Letters, 2008, 100(15): 158101.
|
15 |
PANCHEI D L P, ALVAREZ-MEZAAM, OROZCO-GUTIERREZA. A data-driven measure of effective connectivity based on renyi's α-entropy[J]. Frontiers in Neuroscience, 2019, 13: 1277.
|
16 |
GAOZ, WANGZ, MAC, et al. A wavelet time-frequency representation based complex network method for characterizing brain activities underlying motor imagery signals[J]. IEEE Access, 2018, PP(99): 1⁃1.
|
17 |
SCHALKG, MCFARLANDDJ, HINTERBERGERT, et al. BCI2000:A general-purpose brain-computer interface(BCI) System[J]. IEEE Transactions on Biomedical Engineering. 2004, 51(6): 1034⁃1043.
|
18 |
BRUNNERC, BILLINGERM, SEEBERM, et al. Volume conduction influences scalp-based connectivity estimates[J]. Frontiers in Computational Neuroscience. 2016,10: 121.
|
19 |
NEUPERP C. Motor imagery activates primary sensorimotor area in humans[J]. Neuroscience Letters, 1997,239(2): 65⁃68.
|
20 |
ZHUX, LIP, LIC, et al. Separated channel convolutional neural network to realize the training free motor imagery BCI systems[J]. Biomedical Signal Processing and Control, 2019, 49(3): 396⁃403.
|
21 |
ROMANB, GUISANDEN, GRANADOM. Characterization of visuomotor/imaginary movements in EEG: An information theory and complex network approach[J]. Frontiers in Physics, 2019, 7: 115.
|
22 |
FILHOC A S, ATTUXR, CASTELLANOG. Can graph metrics be used for EEG-BCIs based on hand motor imagery?[J]. Biomedical Signal Processing & Control, 2018, 40(2): 359⁃365.
|