Altaheri H, Muhammad G, Alsulaiman M, Amin SU, Altuwaijri GA, Abdul W, Bencherif MA, Faisal M (2023) Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review. Neural Comput Appl 35(20):14681–14722. https://doi.org/10.1007/s00521-021-06352-5
Ang KK, Chin ZY, Wang C, Guan C, Zhang H (2012) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 6:39. https://doi.org/10.3389/fnins.2012.00039
Article PubMed PubMed Central Google Scholar
Brunner C (2020) Data set 2b of the BCI competition iv. https://bnci-horizon-2020.eu/database/data-sets. Accessed 27 June 2024
Chen W, Luo Y, Wang J (2024) Three-branch temporal–spatial convolutional transformer for motor imagery EEG classification. IEEE Access. https://doi.org/10.1109/access.2024.3405652
Choi H, Park J, Yang Y-M (2022) Whitening technique based on Gram–Schmidt orthogonalization for motor imagery classification of brain–computer interface applications. Sensors 22(16):6042. https://doi.org/10.3390/s22166042
Article PubMed PubMed Central Google Scholar
Choi H, Park J, Yang Y-M (2024) Motor imagery classification improvement of two-class data with covariance decentering eigenface analysis for brain–computer interface systems. Appl Sci 14(21):10062. https://doi.org/10.3390/app142110062
Christiano LJ, Fitzgerald TJ (2003) The band pass filter. Int Econ Rev 44(2):435–465. https://doi.org/10.3386/w7257
Comon P (1994) Independent component analysis, a new concept? Signal Process 36(3):287–314. https://doi.org/10.1016/0165-1684(94)90029-9
Dai M, Zheng D, Na R, Wang S, Zhang S (2019) EEG classification of motor imagery using a novel deep learning framework. Sensors 19(3):551. https://doi.org/10.3390/s19030551
Article PubMed PubMed Central Google Scholar
Decety J (1996) The neurophysiological basis of motor imagery. Behav Brain Res 77(1–2):45–52. https://doi.org/10.1093/acprof:oso/9780199546251.003.0008
Article CAS PubMed Google Scholar
Guevara MA, Corsi-Cabrera M (1996) EEG coherence or EEG correlation? Int J Psychophysiol 23(3):145–153. https://doi.org/10.1016/s0167-8760(96)00038-4
Article CAS PubMed Google Scholar
Guragai B, AlShorman O, Masadeh M, Heyat MBB (2020) A survey on deep learning classification algorithms for motor imagery. In: 32nd international conference on microelectronics (ICM), pp 1–4. IEEE. https://doi.org/10.1109/icm50269.2020.9331503
Hameed A, Fourati R, Ammar B, Ksibi A, Alluhaidan AS, Ayed MB, Khleaf HK (2024) Temporal–spatial transformer based motor imagery classification for BCI using independent component analysis. Biomed Signal Process Control 87:105359. https://doi.org/10.1016/j.bspc.2023.105359
Han C, Liu C, Wang Y, Cai C, Wang J, Qian D (2024) A spatial–spectral and temporal dual prototype network for motor imagery brain–computer interface. arXiv preprint arXiv:2407.03177. https://doi.org/10.48550/arXiv.2407.03177
Henry JC (2006) Electroencephalography: basic principles, clinical applications, and related fields. Neurology 67(11):2092–2092. https://doi.org/10.1016/0013-4694(82)90213-9
Hu Y, Liu Y, Zhang S, Zhang T, Dai B, Peng B, Yang H, Dai Y (2023) A cross-space CNN with customized characteristics for motor imagery EEG classification. IEEE Trans Neural Syst Rehabil Eng 31:1554–1565. https://doi.org/10.1109/tnsre.2023.3249831
Hu Z, He L, Wu H (2023) A multi-feature fusion transformer neural network for motor imagery EEG signal classification. Eng Lett. https://doi.org/10.1016/j.compbiomed.2024.108727
Hua W, Zhou Y, De Sa CM, Zhang Z, Suh GE (2019) Channel gating neural networks. Adv Neural Inf Process Syst. https://doi.org/10.48550/arXiv.1805.12549
Ioffe S (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167. https://doi.org/10.48550/arXiv.1502.03167
Kessy A, Lewin A, Strimmer K (2018) Optimal whitening and decorrelation. Am Stat 72(4):309–314. https://doi.org/10.1080/00031305.2016.1277159
Ko B, Kim H-G, Heo B, Yun S, Chun S, Gu G, Kim W (2022) Group generalized mean pooling for vision transformer. arXiv preprint arXiv:2212.04114. https://doi.org/10.48550/arXiv.2212.04114
Ko W, Jeon E, Jeong S, Suk H-I (2021) Multi-scale neural network for EEG representation learning in BCI. IEEE Comput Intell Mag 16(2):31–45. https://doi.org/10.1109/mci.2021.3061875
Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ (2018) EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng 15(5):056013. https://doi.org/10.1088/1741-2552/aace8c
Li X, Song D, Zhang P, Yu G, Hou Y, Hu B (2016) Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. In: IEEE international conference on bioinformatics and biomedicine (BIBM), pp 352–359. IEEE. https://doi.org/10.1109/bibm.2016.7822545
Li Y, Zhang X-R, Zhang B, Lei M-Y, Cui W-G, Guo Y-Z (2019) A channel-projection mixed-scale convolutional neural network for motor imagery EEG decoding. IEEE Trans Neural Syst Rehabil Eng 27(6):1170–1180. https://doi.org/10.1109/tnsre.2019.2915621
Liu K, Yang T, Yu Z, Yi W, Yu H, Wang G, Wu W (2024) Msvtnet: multi-scale vision transformer neural network for EEG-based motor imagery decoding. IEEE J Biomed Health Inform. https://doi.org/10.1109/jbhi.2024.3450753
Article PubMed PubMed Central Google Scholar
Luo J, Wang Y, Xia S, Lu N, Ren X, Shi Z, Hei X (2023) A shallow mirror transformer for subject-independent motor imagery BCI. Comput Biol Med 164:107254. https://doi.org/10.1016/j.compbiomed.2023.107254
Ma X, Chen W, Pei Z, Zhang Y, Chen J (2024) Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding. Comput Biol Med 175:108504. https://doi.org/10.1016/j.compbiomed.2024.108504
Miao Z, Zhang X, Zhao M, Ming D (2023) LMDA-Net: a lightweight multi-dimensional attention network for general EEG-based brain–computer interface paradigms and interpretability. arXiv preprint arXiv:2303.16407. https://doi.org/10.1016/j.neuroimage.2023.120209
Nguyen AHP, Oyefisayo O, Pfeffer MA, Ling SH (2024) EEG–TCNTransformer: a temporal convolutional transformer for motor imagery brain–computer interfaces. Signals 5(3):605–632. https://doi.org/10.20944/preprints202408.0676.v1
Olias J, Martín-Clemente R, Sarmiento-Vega MA, Cruces S (2019) EEG signal processing in MI–BCI applications with improved covariance matrix estimators. IEEE Trans Neural Syst Rehabil Eng 27(5):895–904. https://doi.org/10.1109/tnsre.2019.2905894
Parallel Cloud (2023) Parallel cloud platform. https://www.paratera.com/. Accessed 15 Sep 2024
Pedroni A, Bahreini A, Langer N (2019) Automagic: standardized preprocessing of big EEG data. Neuroimage 200:460–473. https://doi.org/10.1016/j.neuroimage.2019.06.046
Riyad M, Khalil M, Adib A (2021) A novel multi-scale convolutional neural network for motor imagery classification. Biomed Signal Process Control 68:102747. https://doi.org/10.1016/j.bspc.2021.102747
Rosch E (2013) Prototype classification and logical classification: the two systems. In: New trends in conceptual representation. Psychology Press, pp 73–86
Sadiq MT, Aziz MZ, Almogren A, Yousaf A, Siuly S, Rehman AU (2022) Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework. Comput Biol Med 143:105242. https://doi.org/10.1016/j.compbiomed.2022.105242
Shajil N, Mohan S, Srinivasan P, Arivudaiyanambi J, Arasappan Murrugesan A (2020) Multiclass classification of spatially filtered motor imagery EEG signals using convolutional neural network for BCI based applications. J Med Biol Eng 40:663–672. https://doi.org/10.1007/s40846-020-00538-3
Song Y, Zheng Q, Liu B, Gao X (2022) EEG conformer: convolutional transformer for EEG decoding and visualization. IEEE Trans Neural Syst Rehabil Eng 31:710–719. https://doi.org/10.1109/tnsre.2022.3230250
Tangermann M, Müller K-R, Aertsen A, Birbaumer N, Braun C, Brunner C, Leeb R, Mehring C, Miller KJ, Müller-Putz GR et al (2012) Review of the BCI competition IV. Front Neurosci 6:55. https://doi.org/10.3389/fnins.2012.00055
Article PubMed PubMed Central Google Scholar
Tibrewal N, Leeuwis N, Alimardani M (2022) Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users. PLoS ONE 17(7):e0268880. https://doi.org/10.1371/journal.pone.0268880
Comments (0)