Towards correlation-based time window selection method for motor imagery BCIs
暂无分享,去创建一个
Xingyu Wang | Rami Saab | Andrzej Cichocki | Dewen Hu | Ian Daly | Erwei Yin | Jing Jin | Jiankui Feng | A. Cichocki | D. Hu | Xingyu Wang | Jing Jin | I. Daly | E. Yin | R. Saab | Jiankui Feng
[1] Yu Zhang,et al. Analysis and classification of speech imagery EEG for BCI , 2013, Biomed. Signal Process. Control..
[2] Chang-Hwan Im,et al. Evaluation of feature extraction methods for EEG-based brain–computer interfaces in terms of robustness to slight changes in electrode locations , 2012, Medical & Biological Engineering & Computing.
[3] Bin He,et al. Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives , 2014, IEEE Transactions on Biomedical Engineering.
[4] Cuntai Guan,et al. Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs , 2012, Pattern Recognit..
[5] G Pfurtscheller,et al. Frequency component selection for an EEG-based brain to computer interface. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[6] Klaus-Robert Müller,et al. The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.
[7] D J McFarland,et al. An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.
[8] Reza Boostani,et al. An efficient hybrid linear and kernel CSP approach for EEG feature extraction , 2009, Neurocomputing.
[9] Rajesh P. N. Rao,et al. Towards adaptive classification for BCI , 2006, Journal of neural engineering.
[10] Javier Gomez-Pilar,et al. Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain-computer interfaces , 2015, Neurocomputing.
[11] Pedro J. García-Laencina,et al. Efficient Automatic Selection and Combination of EEG Features in Least Squares Classifiers for Motor Imagery Brain-Computer Interfaces , 2013, Int. J. Neural Syst..
[12] Bin He,et al. Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms , 2015, Proceedings of the IEEE.
[13] Gernot R. Müller-Putz,et al. Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability , 2015, PloS one.
[14] Pengfei Wei,et al. A novel EMD-based Common Spatial Pattern for motor imagery brain-computer interface , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.
[15] Clemens Brunner,et al. Analysis of sensorimotor rhythms for the implementation of a brain switch for healthy subjects , 2010, Biomed. Signal Process. Control..
[16] Bernhard Graimann,et al. A comparison approach toward finding the best feature and classifier in cue-based BCI , 2007, Medical & Biological Engineering & Computing.
[17] Sergio Cruces,et al. Optimization of Alpha-Beta Log-Det Divergences and their Application in the Spatial Filtering of Two Class Motor Imagery Movements , 2017, Entropy.
[18] C Grozea,et al. On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up , 2012, Spinal Cord.
[19] F. L. D. Silva,et al. Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.
[20] Kristin P. Bennett,et al. Support vector machines: hype or hallelujah? , 2000, SKDD.
[21] Keng Peng Tee,et al. EEG-Based Classification of Fast and Slow Hand Movements Using Wavelet-CSP Algorithm , 2013, IEEE Transactions on Biomedical Engineering.
[22] Keinosuke Fukunaga,et al. Introduction to Statistical Pattern Recognition , 1972 .
[23] Jasmin Kevric,et al. Biomedical Signal Processing and Control , 2016 .
[24] Qianqian Wang,et al. Adaptive Semi-Supervised Classification by Joint Global and Local Graph , 2019, IEEE Access.
[25] Clemens Brunner,et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.
[26] Shengjin Wang,et al. Filter ensemble regularized common spatial pattern for EEG classification , 2015, Digital Image Processing.
[27] Klaus-Robert Müller,et al. Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms , 2004, IEEE Transactions on Biomedical Engineering.
[28] Yuanqing Li,et al. A comparison study of two P300 speller paradigms for brain–computer interface , 2013, Cognitive Neurodynamics.
[29] Xingyu Wang,et al. Improved SFFS method for channel selection in motor imagery based BCI , 2016, Neurocomputing.
[30] Z J Koles,et al. The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. , 1991, Electroencephalography and clinical neurophysiology.
[31] Chuanzhen Li,et al. Evaluation of Feature Extraction Methods for Face Recognition , 2013, 2013 Sixth International Symposium on Computational Intelligence and Design.
[32] Andrzej Cichocki,et al. An improved P300 pattern in BCI to catch user’s attention , 2017, Journal of neural engineering.
[33] Klaus-Robert Müller,et al. Spatio-spectral filters for improving the classification of single trial EEG , 2005, IEEE Transactions on Biomedical Engineering.
[34] Cuntai Guan,et al. Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.
[35] Ian Daly,et al. Single tap identification for fast BCI control , 2011, Cognitive Neurodynamics.
[36] Andrés Marino Álvarez-Meza,et al. Time-series discrimination using feature relevance analysis in motor imagery classification , 2015, Neurocomputing.
[37] Xingyu Wang,et al. A P300 Brain-Computer Interface Based on a Modification of the Mismatch Negativity Paradigm , 2015, Int. J. Neural Syst..
[38] Konstantinos N. Plataniotis,et al. Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems , 2016, IEEE Transactions on Biomedical Engineering.
[39] Xingyu Wang,et al. An adaptive P300-based control system , 2011, Journal of neural engineering.
[40] Heung-No Lee,et al. Sparse representation-based classification scheme for motor imagery-based brain–computer interface systems , 2012, Journal of neural engineering.
[41] G. Pfurtscheller,et al. Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[42] Cuntai Guan,et al. Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).