Altaheri H, Muhammad G, Alsulaiman M (2023) Physics-informed attention temporal convolutional network for EEG-based motor imagery classification. IEEE Trans Ind Inform 19(2):2249–2258

Article  Google Scholar 

Altuwaijri GA, Muhammad G (2022) A multibranch of convolutional neural network models for electroencephalogram-based motor imagery classification. Biosensors 12(1):22

Article  PubMed  PubMed Central  Google Scholar 

Amin SU, Alsulaiman M, Muhammad G, Mekhtiche MA, Shamim Hossain M (2019) Deep learning for EEG motor imagery classifcation based on multi-layer CNNs feature fusion. Fut Gener Comput Syst 101:542–554

Article  Google Scholar 

Ang KK, Chin ZY, Zhang H, and Guan C (2008) Filter bank common spatial pattern (FBCSP) in brain-computer interface. In: Proceedings IEEE International joint conference. Neural Networks (IEEE world congress on computational intelligence), Hong Kong, China, pp 2390–2397

Bai S, Kolter JZ, and Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv. 1803.01271

Barachant A, Bonnet S, Congedo M, Jutten C (2012) Multiclass brain-computer interface classification by Riemannian geometry. IEEE Trans Biomed Eng 59(4):920–928

Article  PubMed  Google Scholar 

Barmpas K, Panagakis Y, Adamos DA, Laskaris N, Zafeiriou S (2023) BrainWave-scattering Net: a lightweight network for EEG-based motor imagery recognition. J Neural Eng 20(5):056014

Article  Google Scholar 

Barmpas K, Panagakis Y, Zoumpourlis G, Adamos DA, Laskaris N, Zafeiriou S (2024) A causal perspective on brainwave modeling for brain–computer interfaces. J Neural Eng 21(3):036001

Article  Google Scholar 

Bentlemsan M, Zemouri E-T, Bouchaffra D, Yahya-Zoubir B, and Ferroudji K (2014) Random forest and filter bank common spatial patterns for EEG-based motor imagery classification. In: Proceedings 5th international conference on intelligent systems, modelling and simulation, Langkawi, Malaysia, pp 235–238

Chatterjee R, Maitra T, Haffzul Islam S, Hassan MM, Alamri A, Fortino G (2019) A novel machine learning based feature selection for motor imagery EEG signal classification in Internet of medical things environment. Futur Gener Comp Syst 98:419–434

Article  Google Scholar 

Chaudhary S, Taran S, Bajaj V, Sengur A (2019) Convolutional neural network based approach towards motor imagery tasks EEG signals classification. IEEE Sens J 19(12):4494–4500

Article  Google Scholar 

Chen W, Luo Y, Wang J (2024) Three-branch temporal-spatial convolutional transformer for motor imagery EEG classification. IEEE Access 12:79754–79764

Article  Google Scholar 

Chen Y, Yang R, Huang M, Wang Z, Liu X (2022a) Single-source to single-target cross-subject motor imagery classification based on multisubdomain adaptation network. IEEE Trans Neural Syst Rehabil Eng 30:1992–2002

Article  PubMed  Google Scholar 

Chen L, Yu Z, Yang J (2022) SPD-CNN: a plain CNN-based model using the symmetric positive definite matrices for cross-subject EEG classification with meta-transfer-learning. Front Neurorobotics 16:958052

Article  Google Scholar 

Dai G, Zhou J, Huang J, Wang N (2020) HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification. J Neural Eng 17(1):016025

Article  PubMed  Google Scholar 

Ding Y, Robinson N, Zhang S, Zeng Q, Guan C (2022) TSception: capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition. IEEE Trans Affect Comput 14(3):2238–2250

Article  Google Scholar 

Gaur P, Gupta H, Chowdhury A, McCreadie K, Pachori RB, Wang H (2021) A sliding window common spatial pattern for enhancing motor imagery classification in EEG-BCI. IEEE Trans Instrum Meas 70:4002709

Article  Google Scholar 

Guger C, Ramoser H, Pfurtscheller G (2000) Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). IEEE Trans Rehabil Eng 8(4):447–456

Article  CAS  PubMed  Google Scholar 

Ingolfsson TM, Hersche M, Wang X, Kobayashi N, Cavigelli L, and Benini, L (2020) EEG-TCNet: an accurate temporal convolutional network for embedded motor-imagery brain–machine interfaces. In: Proceedings IEEE International conference systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, pp 2958–2965

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

Article  PubMed  Google Scholar 

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

Article  CAS  PubMed  Google Scholar 

Li Z, Hwang K, Li K, Wu J, Ji T (2022) Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity. Sci Rep 12:18998

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liu K, Yang M, Yu Z, Wang G, Wu W (2022) FBMSNet: a filter-bank multi-scale convolutional neural network for EEG-based motor imagery decoding. IEEE Trans Biomed Eng 70(2):436–445

Article  Google Scholar 

Lotte F (2015) Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based brain-computer interfaces. Proc IEEE 103(6):871–890

Article  Google Scholar 

Lu N, Yin T, and Jing X (2019) A temporal convolution network solution for EEG motor imagery classification. In: Proceedings 19th international conference bioinformatics and bioengineering (BIBE), IEEE, pp 796–799

Luo T, Zhou C, Chao F (2018) Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network. BMC Bioinform 19:344

Article  Google Scholar 

Qin L, He B (2005) A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications. J Neural Eng 2(4):65

Article  PubMed  Google Scholar 

Qin Y, Li B, Wang W, Shi X, Wang H, Wang X (2024) ETCNet: an EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network. Brain Res 1823:148673

Article  CAS  PubMed  Google Scholar 

Sakhavi S, Guan C, Yan S (2018) Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans Neural Netw Learn Syst 29(11):5619–5629

Article  PubMed  Google Scholar 

Salami A, Andreu-Perez J, Gillmeister H (2022) EEG-ITNet: an explainable inception temporal convolutional network for motor imagery classification. IEEE Access 10:36672–36685

Article  Google Scholar 

Santamaría-Vázquez E, Martínez-Cagigal V, Vaquerizo-Villar F, Hornero R (2020) EEG-inception: a novel deep convolutional neural network for assistive ERP-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 28(12):2773–2782

Article  PubMed  Google Scholar 

Schirrmeister R, Gemein L, Eggensperger K, Hutter F, Ball T (2017) Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 38(11):5391–5420

Article  PubMed  PubMed Central  Google Scholar 

Shi J et al (2024) A brain topography graph embedded convolutional neural network for EEG-based motor imagery classification. Biomed Signal Process Control 95:106401

Article  Google Scholar 

Song Y, Zheng Q, Liu B, Gao X (2023) EEG Conformer: Convolutional transformer for EEG decoding and visualization. IEEE Trans Neural Syst Rehabil Eng 31:710–719

Article  PubMed  Google Scholar 

Song Y, Jia X, Yang L, Xie L (2011) Transformer-based spatial-temporal feature learning for EEG decoding. ArXiv./abs/2106.11170

Tao W et al (2024) ADFCNN: attention-based dual-scale fusion convolutional neural network for motor imagery brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 32:154–165

Article  PubMed  Google Scholar 

Vaswani A et al (2017) Attention is all you need. In: Proc. Adv. Neural Inf. Process. Syst., vol 30. Curran Associates, Red Hook, NY, USA, pp 1–11.

Wang Z, Cao L, Zhang Z, Gong X, Sun Y, Wang H (2018) Short time Fourier transformation and deep neural networks for motor imagery brain computer interface recognition. Concurr Comput-Pract Exp 30(23):e4413

Article  Google Scholar 

Wang P, Jiang A, Liu X, Shang J, Zhang L (2018) LSTM-based EEG classification in motor imagery tasks. IEEE Trans Neural Syst Rehabil Eng 26(11):2086–2095

Article  PubMed  Google Scholar 

Willett FR et al (2023) A high-performance speech neuroprosthesis. Nature 620(7976):1031–1036

Article  CAS  PubMed  PubMed Central  Google Scholar 

Xie J et al (2022) A transformer-based approach combining deep learning network and spatial-temporal information for raw EEG classification. IEEE Trans Neural Syst Rehabil Eng 30:2126–2136

Article  PubMed  Google Scholar 

Xu F et al (2022) A framework for motor imagery with LSTM neural network. Comput Methods Programs Biomed 21:106692

Article  Google Scholar 

Zhang B et al (2020) Cross-subject seizure detection in EEGs using deep transfer learning. Comput Math Methods Med 2020:7902072

Article  PubMed  PubMed Central  Google Scholar 

Zhang D, Li H, Xie J (2023) MI-CAT: a transformer-based domain adaptation network for motor imagery classification. Neural Netw 165:451–462

Article  PubMed