Time–frequency–space transformer EEG decoding for spinal cord injury

Amin SU, Altaheri H, Muhammad G et al (2022) Attention-inception and long- short-term memory-based electroencephalography classification for motor imagery tasks in rehabilitation. IEEE Trans Industr Inf 18:5412–5421. https://doi.org/10.1109/tii.2021.3132340

Article  Google Scholar 

Ang KK, Chin ZY, Wang C et al (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 

Attallah O, Abougharbia J, Tamazin M, Nasser AA (2020) A BCI system based on motor imagery for assisting people with motor deficiencies in the limbs. Brain Sci 10:864. https://doi.org/10.3390/brainsci10110864

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bousseta R, Tayeb S, El Ouakouak I, Gharbi M, Regragui F, Himmi MM. (2016) EEG efficient classification of imagined hand movement using RBF kernel SVM. In2016 11th International Conference on Intelligent Systems Theories and Applications (SITA). https://doi.org/10.1109/sita.2016.7772278

Cheng M, Lu Z, Wang H (2016) Regularized common spatial patterns with subject-to-subject transfer of EEG signals. Cogn Neurodyn 11:173–181. https://doi.org/10.1007/s11571-016-9417-x

Article  PubMed  PubMed Central  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 17:016025. https://doi.org/10.1088/1741-2552/ab405f

Article  PubMed  Google Scholar 

Deng X, Zhang B, Yu N et al (2021) Advanced TSGL-eegnet for motor imagery EEG-based brain-computer interfaces. IEEE Access 9:25118–25130. https://doi.org/10.1109/access.2021.3056088

Article  Google Scholar 

Duan K, Wu Q, Liao Y et al (2020) Discrimination of tourette syndrome based on the spatial patterns of the resting–state EEG Network. Brain Topogr 34:78–87. https://doi.org/10.1007/s10548-020-00801-5

Article  PubMed  Google Scholar 

Dutta KK (2019) Multi-class time series classification of EEG signals with recurrent neural networks. 2019 9th International Conference on Cloud Computing, Data Science & Engineering 337–341. https://doi.org/10.1109/confluence.2019.8776889

Fu R, Tian Y, Bao T et al (2019) Improvement motor imagery EEG classification based on regularized linear discriminant analysis. J Med Syst 43:169. https://doi.org/10.1007/s10916-019-1270-0

Article  PubMed  Google Scholar 

Grangeon M, Revol P, Guillot A et al (2012) Could motor imagery be effective in upper limb rehabilitation of individuals with spinal cord injury? A case study. Spinal Cord 50:766–771. https://doi.org/10.1038/sc.2012.41

Article  CAS  PubMed  Google Scholar 

Han J, Wang H (2021) Transformer based network for open information extraction. Eng Appl Artif Intell 102:104262. https://doi.org/10.1016/j.engappai.2021.104262

Article  Google Scholar 

Hu Y, Liu Y, Zhang S et al (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

Article  PubMed  Google Scholar 

Hwang H-J, Kwon K, Im C-H (2009) Neurofeedback-based motor imagery training for brain-computer Interface (BCI). J Neurosci Methods 179:150–156. https://doi.org/10.1016/j.jneumeth.2009.01.015

Article  PubMed  Google Scholar 

Höller Y, Thomschewski A, Uhl A et al (2018) HD-EEG based classification of motor-imagery related activity in patients with spinal cord injury. Front Neurol 9:955. https://doi.org/10.3389/fneur.2018.00955

Article  PubMed  PubMed Central  Google Scholar 

Imran SM, Talukdar MT, Sakib SK et al (2014) Motor imagery EEG signal classification scheme based on wavelet domain statistical features. Int Conf Electr Eng Inf Commun Technol 2014:1–4. https://doi.org/10.1109/iceeict.2014.6919172

Article  Google Scholar 

Jiang Z, Liu P, Xia Y, Zhang J (2021) Application of CNN in EEG Image Classification of AD patients. The 2nd International Conference on Computing and Data Science 1–5. https://doi.org/10.1145/3448734.3450473

Kim D-K, Kim Y-T, Jung H-R, et al (2021) Sequential Transfer Learning via segment after cue enhances the motor imagery-based brain-computer interface. 2021 9th International Winter Conference on Brain-Computer Interface (BCI) 1–5. https://doi.org/10.1109/bci51272.2021.9385340

King CE, Wang PT, Chui LA et al (2013) Operation of a brain-computer interface walking simulator for individuals with spinal cord injury. J Neuroeng Rehabil 10:77. https://doi.org/10.1186/1743-0003-10-77

Article  PubMed  PubMed Central  Google Scholar 

Klepl D, He F, Wu M et al (2022) EEG-based graph neural network classification of alzheimer’s disease: An empirical evaluation of functional connectivity methods. IEEE Trans Neural Syst Rehabil Eng 30:2651–2660. https://doi.org/10.1109/tnsre.2022.3204913

Article  PubMed  Google Scholar 

Lawhern VJ, Solon AJ, Waytowich NR et al (2018) EEGNet: A compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng 15:056013. https://doi.org/10.1088/1741-2552/aace8c

Article  PubMed  Google Scholar 

Lee J, Lee S, Cho W et al (2021) Vision transformer-based tailing detection in videos. Appl Sci 11:11591. https://doi.org/10.3390/app112411591

Article  CAS  Google Scholar 

Li X, Fan H, Wang H, Wang L (2019b) Common spatial patterns combined with phase synchronization information for classification of EEG Signals. Biomed Signal Process Control 52:248–256. https://doi.org/10.1016/j.bspc.2019.04.034

Article  Google Scholar 

Li F, Wang J, Liao Y et al (2019a) Differentiation of schizophrenia by combining the spatial EEG brain network patterns of rest and task P300. IEEE Trans Neural Syst Rehabil Eng 27:594–602. https://doi.org/10.1109/tnsre.2019.2900725

Article  PubMed  Google Scholar 

Li F, Yi C, Liao Y et al (2021) Reconfiguration of brain network between resting state and P300 task. IEEE Trans Cogn Dev Syst 13:383–390. https://doi.org/10.1109/tcds.2020.2965135

Article  Google Scholar 

Li F, Yi C, Song L et al (2018) Brain Network reconfiguration during motor imagery revealed by a large-scale network analysis of SCALP EEG. Brain Topogr 32:304–314. https://doi.org/10.1007/s10548-018-0688-x

Article  PubMed  Google Scholar 

Li H, Zhang D, Xie J (2023) Mi-Daban: a dual-attention-based adversarial network for motor imagery classification. Comput Biol Med 152:106420. https://doi.org/10.1016/j.compbiomed.2022.106420

Article  PubMed  Google Scholar 

Luo Y, Lu B-L (2018) EEG data augmentation for emotion recognition using a conditional Wasserstein Gan. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2018.8512865

Mahmood MT, Choi T-S (2010) Image focus measure based on energy of high frequency components in S-transform. Opt Lett 35:1272–1274. https://doi.org/10.1364/ol.35.001272

Article  PubMed  Google Scholar 

Ormerod M, Martínez del Rincón J, Devereux B (2021) Predicting semantic similarity between clinical sentence pairs using transformer models: evaluation and representational analysis. JMIR Med Inform 9:e23099. https://doi.org/10.2196/23099

Article  PubMed  PubMed Central  Google Scholar 

Phadikar S, Sinha N, Ghosh R (2023) Unsupervised feature extraction with autoencoders for EEG based multiclass motor imagery BCI. Expert Syst Appl 213:118901. https://doi.org/10.1016/j.eswa.2022.118901

Article  Google Scholar 

Ren B, Yang K, Zhu L et al (2022) Multi-granularity analysis of brain networks assembled with intra-frequency and cross-frequency phase coupling for human EEG after stroke. Front Comput Neurosci 16:785397. https://doi.org/10.3389/fncom.2022.785397

Article  PubMed  PubMed Central  Google Scholar 

Roth HR, Lu L, Liu J et al (2016) Improving computer-aided detection usingconvolutional neural networks and random view aggregation. IEEE Trans Med Imaging 35:1170–1181. https://doi.org/10.1109/tmi.2015.2482920

Article  PubMed  Google Scholar 

Sarwar A, Javed K, Jawad Khan M et al (2021) Enhanced accuracy for motor imagery detection using deep learning for BCI. Comput Mater Amp Continua 68:3825–3840. https://doi.org/10.32604/cmc.2021.016893

Article  Google Scholar 

Sheykhivand S, Mousavi Z, Rezaii TY, Farzamnia A (2020) Recognizing emotions evoked by music using CNN-LSTM networks on EEG signals. IEEE Access 8:139332–139345. https://doi.org/10.1109/access.2020.3011882

Article  Google Scholar 

Shu X, Yao L, Sheng X et al (2017) Enhanced motor imagery-based BCI performance via tactile stimulation on unilateral hand. Front Hum Neurosci 11:585. https://doi.org/10.3389/fnhum.2017.00585

Article  PubMed 

Comments (0)

No login
gif