An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm

Yuan, 2014, Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives, IEEE Trans. Biomed. Eng., 61, 1425, 10.1109/TBME.2014.2312397 Jeon, 2011, Event-related (de)synchronization (erd/ers) during motor imagery tasks: implications for brain-computer interfaces, Int. J. Ind. Ergon., 41, 428, 10.1016/j.ergon.2011.03.005 Takemi, 2013, Event-related desynchronization reflects downregulation of intracortical inhibition in human primary motor cortex, J. Neurophysiol., 110, 1158, 10.1152/jn.01092.2012 Ovbiagele, 2011, Stroke epidemiology: advancing our understanding of disease mechanism and therapy, Neurotherapeutics, 8, 319, 10.1007/s13311-011-0053-1 Mozaffarian, 2016, Heart disease and stroke statistics2016 update: a report from the american heart association, Circulation, 133, 10.1161/CIR.0000000000000350 Gadidi, 2011, Long-term outcome post stroke: predictors of activity limitation and participation restriction, Arch. Phys. Med. Rehabil., 92, 1802, 10.1016/j.apmr.2011.06.014 Chaudhary, 2016, Brain-computer interfaces for communication and rehabilitation, Nat. Rev. Neurol., 12, 513, 10.1038/nrneurol.2016.113 Teo, 2014, Is motor-imagery brain-computer interface feasible in stroke rehabilitation?, PM. R., 6, 723, 10.1016/j.pmrj.2014.01.006 Pichiorri, 2011, Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness, J. Neural. Eng., 8, 10.1088/1741-2560/8/2/025020 Chaudhary, 2015, Brain-machine interface (BMI) in paralysis, Ann. Phys. Rehabil. Med., 58, 9, 10.1016/j.rehab.2014.11.002 Ramos-Murguialday, 2013, Brain-machine interface in chronic stroke rehabilitation: a controlled study, Ann. Neurol., 74, 100, 10.1002/ana.23879 Ang, 2014, Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke, Front. Neuroeng., 7, 30, 10.3389/fneng.2014.00030 Leamy, 2014, An exploration of EEG features during recovery following stroke–implications for BCI-mediated neurorehabilitation therapy, J. Neuro. Rehabil., 11, 9, 10.1186/1743-0003-11-9 Sannelli, 2016, Ensembles of adaptive spatial filters increase BCI performance: an online evaluation, J. Neural. Eng., 13, 10.1088/1741-2560/13/4/046003 Song, 2015, Improving brain-computer interface classification using adaptive common spatial patterns, Comput. Biol. Med., 61, 150, 10.1016/j.compbiomed.2015.03.023 Lotte, 2010, Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms, IEEE Trans. Biomed. Eng., 58, 355, 10.1109/TBME.2010.2082539 Jiao, 2018, Sparse group representation model for motor imagery eeg classification, IEEE J. Biomed. Health Inform., 1, 10.1109/JBHI.2018.2832538 Zhang, 2018, Temporally constrained sparse group spatial patterns for motor imagery BCI, IEEE Trans. Cybern., 99, 1 Sun, 2006, Adaptive feature extraction for EEG signal classification, Med. Biol. Eng. Comput., 44, 931, 10.1007/s11517-006-0107-4 Chen, 2010, An adaptive feature extraction method for motor-imagery BCI systems, vol 2010 Tomioka, 2006, Adapting spatial filtering methods for nonstationary BCIs, 1 Mondini, 2016, EEG-based BCI system using adaptive features extraction and classification procedures, Comp. Intell. Neurosci., 2016, 4562601 Mobaien, 2016, ACSP: adaptive CSP filter for BCI applications, 466 Lowne, 2006, An adaptive, sparse-feedback EEG classifier for self-paced BCI Tsui, 2009, A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended kalman filter based algorithm for online training, Med. Biol. Eng. Comput., 47, 257, 10.1007/s11517-009-0459-7 Woehrle, 2015, An adaptive spatial filter for user-independent single trial detection of event-related potentials, IEEE Trans. Biomed. Eng., 62, 1696, 10.1109/TBME.2015.2402252 Wang, 2016, Discriminative feature extraction via multivariate linear regression for ssvep-based bci, IEEE Trans. Neural Syst. Rehabil. Eng., 24, 532, 10.1109/TNSRE.2016.2519350 Zhou, 2016, Linked component analysis from matrices to high-order tensors: applications to biomedical data, Proc. IEEE, 104, 310, 10.1109/JPROC.2015.2474704 Blankertz, 2008, Optimizing spatial filters for robust EEG single-trial analysis, IEEE Sig. Proc. Mag., 25, 41, 10.1109/MSP.2008.4408441 Ang, 2012, Filter bank common spatial pattern algorithm on BCI competition iv datasets 2a and 2b, Front. Neurosci., 6, 10.3389/fnins.2012.00039 Dornhege, 2004, Increase information transfer rates in BCI by csp extension to multi-class, Adv. Neural Inf. Proc. Sys., 16, 733 Ledoit, 2004, A well-conditioned estimator for large-dimensional covariance matrices, J. Multivar. Anal., 88, 365, 10.1016/S0047-259X(03)00096-4 Handiru, 2016, Multi-direction hand movement classification using EEG-based source space analysis, IEEE Eng. Med. Biol. Soc. Conf. Proc., 2016, 4551 Rao, 2001, An RLS type algorithm for generalized eigendecomposition, Neur. Net. Sig. Proc., 263 Golub, 2013 Szachewicz, 2013 Yong, 2015, EEG classification of different imaginary movements within the same limb, PloS One, 10, 1, 10.1371/journal.pone.0121896 Lotte, 2007, A review of classification algorithms for EEG-based brain–computer interfaces, J. Neural. Eng., 4, R1, 10.1088/1741-2560/4/2/R01 Clemmensen, 2011, Sparse discriminant analysis, Technometrics, 53, 406, 10.1198/TECH.2011.08118 Friedman, 1989, Regularized discriminant analysis, J. Amer. Stat. Assoc., 84, 165, 10.1080/01621459.1989.10478752 Schloegl, 2008, Biosig: a free and open source software library for BCI research, Computer, 41, 44, 10.1109/MC.2008.407 Zhang, 2016, Sparse bayesian classification of eeg for brain-computer interface, IEEE Trans. Neural Networks Learn Syst., 27, 2256, 10.1109/TNNLS.2015.2476656 Tangermann, 2012, Review of the BCI competition iv, Front. Neurosci., 6, 55 Cohen, 1960, A coefficient of agreement for nominal scales, Educat. Psych. Meas., 20, 37, 10.1177/001316446002000104 Altman, 1990 Schlogl, 2007, Evaluation criteria for BCI research, 327 Jochumsen, 2016, Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation, Med. Biol. Eng. Comput., 54, 1491, 10.1007/s11517-015-1421-5 M. Jochumsen, I. K. Niazi, D. Taylor, D. Farina, K. Dremstrup, Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG, J. Neural. Eng. 12 (5). https://doi.org/10.1088/1741-2560/12/5/056013. Xu, 2014, A closed-loop brain-computer interface triggering an active ankle-foot orthosis for inducing cortical neural plasticity, IEEE Trans. Biomed. Eng., 61, 2092, 10.1109/TBME.2014.2313867 Niazi, 2012, Peripheral electrical stimulation triggered by self-paced detection of motor intention enhances motor evoked potentials, IEEE Trans. Neur. Sys. Rehabil. Eng., 20, 595, 10.1109/TNSRE.2012.2194309 Nijboer, 2015, Technology transfer of brain-computer interfaces as assistive technology: barriers and opportunities, Ann. Phys. Rehabil. Med., 58, 35, 10.1016/j.rehab.2014.11.001 Bhattacharyya, 2015, An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement, Biomed. Sig. Proc. Cont., 21, 90, 10.1016/j.bspc.2015.05.004 Aghaei, 2016, Separable common spatio-spectral patterns for motor imagery BCI systems, IEEE Trans. Biomed. Eng., 63, 15, 10.1109/TBME.2015.2487738