Aggarwal S, Chugh N (2019) Signal processing techniques for motor imagery brain computer interface: a review. Array 1:100003
Alotaiby T, El-Samie FEA, Alshebeili SA, Ahmad I (2015) A review of channel selection algorithms for EEG signal processing. EURASIP J Adv Signal Process 2015:1–21
Arsigny V, Fillard P, Pennec X, Ayache N (2007) Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM J Matrix Anal Appl 29(1):328–347
Article MathSciNet Google Scholar
Baig MZ, Aslam N, Shum HP (2020) Filtering techniques for channel selection in motor imagery EEG applications: a survey. Artif Intell Rev 53:1207–1232
Barachant A, Bonnet S (2011) Channel selection procedure using riemannian distance for bci applications. In: 2011 5th international IEEE/EMBS conference on neural engineering, pp 348–351. IEEE
Bendat JS, Piersol AG (2011) Random data: analysis and measurement procedures. Wiley, Dublin
Boto E, Holmes N, Leggett J, Roberts G, Shah V, Meyer SS, Muñoz LD, Mullinger KJ, Tierney TM, Bestmann S et al (2018) Moving magnetoencephalography towards real-world applications with a wearable system. Nature 555(7698):657–661
Article ADS CAS PubMed PubMed Central Google Scholar
Cao M, Galvis D, Vogrin SJ, Woods WP, Vogrin S, Wang F, Woldman W, Terry JR, Peterson A, Plummer C et al (2022) Virtual intracranial EEG signals reconstructed from meg with potential for epilepsy surgery. Nat Commun 13(1):994
Article ADS CAS PubMed PubMed Central Google Scholar
Chan AM, Halgren E, Marinkovic K, Cash SS (2011) Decoding word and category-specific spatiotemporal representations from MEG and EEG. Neuroimage 54(4):3028–3039
Chu Y, Zhao X, Zou Y, Xu W, Song G, Han J, Zhao Y (2020) Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression. J Neural Eng 17(4):046029
Cichy RM, Pantazis D (2017) Multivariate pattern analysis of MEG and EEG: a comparison of representational structure in time and space. Neuroimage 158:441–454
Congedo M, Barachant A, Bhatia R (2017) Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Comput Interfaces 4(3):155–174
Corsi M-C, Chavez M, Schwartz D, Hugueville L, Khambhati AN, Bassett DS, De Vico Fallani F (2019) Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interface. Int J Neural Syst 29(01):1850014
Dash D, Wisler A, Ferrari P, Davenport EM, Maldjian J, Wang J (2020) Meg sensor selection for neural speech decoding. IEEE Access 8:182320–182337
Article PubMed PubMed Central Google Scholar
Fang H, Jin J, Daly I, Wang X (2022) Feature extraction method based on filter banks and Riemannian tangent space in motor-imagery BCI. IEEE J Biomed Health Inform 26(6):2504–2514
Halme H-L, Parkkonen L (2018) Across-subject offline decoding of motor imagery from MEG and EEG. Sci Rep 8(1):10087
Article ADS PubMed PubMed Central Google Scholar
Hansen P, Kringelbach M, Salmelin R (2010) MEG: an introduction to methods. Oxford University Press, Oxford
Jin J, Miao Y, Daly I, Zuo C, Hu D, Cichocki A (2019) Correlation-based channel selection and regularized feature optimization for mi-based BCI. Neural Netw 118:262–270
Jin J, Qu T, Xu R, Wang X, Cichocki A (2022) Motor imagery EEG classification based on Riemannian sparse optimization and dempster-Shafer fusion of multi-time-frequency patterns. IEEE Trans Neural Syst Rehabilit Eng 31:58–67
Kim S-G, Overath T, Sedley W, Kumar S, Teki S, Kikuchi Y, Patterson R, Griffiths TD (2022) Meg correlates of temporal regularity relevant to pitch perception in human auditory cortex. Neuroimage 249:118879
Li X, Chen J, Shi N, Yang C, Gao P, Chen X, Wang Y, Gao S, Gao X (2023) A hybrid steady-state visually evoked response-based brain-computer interface with MEG and EEG. Expert Syst Appl 223:119736
Liu L, Wang F, Zhou K, Ding N, Luo H (2017) Perceptual integration rapidly activates dorsal visual pathway to guide local processing in early visual areas. PLoS Biol 15(11):2003646
Mellinger J, Schalk G, Braun C, Preissl H, Rosenstiel W, Birbaumer N, Kübler A (2007) An meg-based brain-computer interface (BCI). Neuroimage 36(3):581–593
Moakher M (2005) A differential geometric approach to the geometric mean of symmetric positive-definite matrices. SIAM J Matrix Anal Appl 26(3):735–747
Article MathSciNet Google Scholar
Pennec X, Fillard P, Ayache N (2006) A Riemannian framework for tensor computing. Int J Comput Vision 66:41–66
Pires G, Nunes U, Castelo-Branco M (2009) P300 spatial filtering and coherence-based channel selection. In: 2009 4th international IEEE/EMBS conference on neural engineering, pp 311–314. IEEE
Qiu Z, Jin J, Lam H-K, Zhang Y, Wang X, Cichocki A (2016) Improved SFFS method for channel selection in motor imagery based BCI. Neurocomputing 207:519–527
Qu T, Jin J, Xu R, Wang X, Cichocki A (2022) Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and Riemannian tangent space for MI-BCIS. J Neural Eng 19(5):056025
Rathee D, Raza H, Roy S, Prasad G (2021) A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface. Sci Data 8(1):1–10
Reichert C, Dürschmid S, Heinze H-J, Hinrichs H (2017) A comparative study on the detection of covert attention in event-related EEG and meg signals to control a BCI. Front Neurosci 11:575
Article PubMed PubMed Central Google Scholar
Rivet B, Souloumiac A, Attina V, Gibert G (2009) xdawn algorithm to enhance evoked potentials: application to brain-computer interface. IEEE Trans Biomed Eng 56(8):2035–2043
Roy S, Rathee D, Chowdhury A, McCreadie K, Prasad G (2020) Assessing impact of channel selection on decoding of motor and cognitive imagery from meg data. J Neural Eng 17(5):056037
Roy S, Rathee D, McCreadie K, Prasad G (2019) Channel selection improves meg-based brain-computer interface. In: 2019 9th i nternational IEEE/EMBS conference on neural engineering (NER), pp. 295–298. IEEE
Shi R, Zhao Y, Cao Z, Liu C, Kang Y, Zhang J (2021) Categorizing objects from meg signals using EEGnet. Cognit Neurodyn 16:1–13
Srinivasan R, Winter WR, Ding J, Nunez PL (2007) EEG and meg coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J Neurosci Methods 166(1):41–52
Article PubMed PubMed Central Google Scholar
Sun H, Jin J, Kong W, Zuo C, Li S, Wang X (2021) Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm. Cogn Neurodyn 15:141–156
Tam W-K, Ke Z, Tong K-Y (2011) Performance of common spatial pattern under a smaller set of eeg electrodes in brain-computer interface on chronic stroke patients: a multi-session dataset study. In: 2011 annual international conference of the IEEE engineering in medicine and biology society, pp 6344–6347. IEEE
Tang C, Gao T, Li Y, Chen B (2022) EEG channel selection based on sequential backward floating search for motor imagery classification. Front Neuroscince 16:1828
Vapnik V (1999) The nature of statistical learning theory. Springer, New York
Varsehi H, Firoozabadi SMP (2021) An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using granger causality. Neural Netw 133:193–206
Wakeman DG, Henson RN (2015) A multi-subject, multi-modal human neuroimaging dataset. Sci Data 2(1):1–10
Xiao R, Huang Y, Xu R, Wang B, Wang X, Jin J (2022) Coefficient-of-variation-based channel selection with a new testing framework for MI-based BCI. Cognit Neurodyn 16:1–13
Yger F, Berar M, Lotte F (2016) Riemannian approaches in brain-computer interfaces: a review. IEEE Trans Neural Syst Rehabil Eng 25(10):1753–1762
Yu J, Yu ZL (2021) Cross-correlation based discriminant criterion for channel selection in motor imagery BCI systems. J Neural Eng 18(4):046083
Zanini P, Congedo M, Jutten C, Said S, Berthoumieu Y (2017) Transfer learning: a Riemannian geometry framework with applications to brain-computer interfaces. IEEE Trans Biomed Eng 65(5):1107–1116
Zhang W, Ding N (2017) Time-domain analysis of neural tracking of hierarchical linguistic structures. Neuroimage 146:333–340
Zhang B, Wang F, Zhang Q, Naya Y (2022) Distinct networks coupled with parietal cortex for spatial representations inside and outside the visual field. Neuroimage 252:119041
Zubarev I, Zetter R, Halme H-L, Parkkonen L (2019) Adaptive neural network classifier for decoding MEG signals. Neuroimage 197:425–434
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