BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

197 related articles for article (PubMed ID: 38308997)

  • 1. Unraveling motor imagery brain patterns using explainable artificial intelligence based on Shapley values.
    Pérez-Velasco S; Marcos-Martínez D; Santamaría-Vázquez E; Martínez-Cagigal V; Moreno-Calderón S; Hornero R
    Comput Methods Programs Biomed; 2024 Apr; 246():108048. PubMed ID: 38308997
    [TBL] [Abstract][Full Text] [Related]  

  • 2. EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning.
    Perez-Velasco S; Santamaria-Vazquez E; Martinez-Cagigal V; Marcos-Martinez D; Hornero R
    IEEE Trans Neural Syst Rehabil Eng; 2022; 30():1766-1775. PubMed ID: 35759578
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.
    Tibrewal N; Leeuwis N; Alimardani M
    PLoS One; 2022; 17(7):e0268880. PubMed ID: 35867703
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Relevance-based channel selection in motor imagery brain-computer interface.
    Nagarajan A; Robinson N; Guan C
    J Neural Eng; 2023 Jan; 20(1):. PubMed ID: 36548997
    [No Abstract]   [Full Text] [Related]  

  • 5. EEG oscillatory patterns and classification of sequential compound limb motor imagery.
    Yi W; Qiu S; Wang K; Qi H; He F; Zhou P; Zhang L; Ming D
    J Neuroeng Rehabil; 2016 Jan; 13():11. PubMed ID: 26822435
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study.
    Wang K; Wang Z; Guo Y; He F; Qi H; Xu M; Ming D
    J Neuroeng Rehabil; 2017 Sep; 14(1):93. PubMed ID: 28893295
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network.
    Zhang T; Liu T; Li F; Li M; Liu D; Zhang R; He H; Li P; Gong J; Luo C; Yao D; Xu P
    Neuroimage; 2016 Jul; 134():475-485. PubMed ID: 27103137
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface.
    Nagarajan A; Robinson N; Ang KK; Chua KSG; Chew E; Guan C
    J Neural Eng; 2024 Jan; 21(1):. PubMed ID: 38091617
    [No Abstract]   [Full Text] [Related]  

  • 9. Explainable cross-task adaptive transfer learning for motor imagery EEG classification.
    Miao M; Yang Z; Zeng H; Zhang W; Xu B; Hu W
    J Neural Eng; 2023 Nov; 20(6):. PubMed ID: 37963394
    [No Abstract]   [Full Text] [Related]  

  • 10. Transcranial magnetic stimulation for individual identification of the best electrode position for a motor imagery-based brain-computer interface.
    Hänselmann S; Schneiders M; Weidner N; Rupp R
    J Neuroeng Rehabil; 2015 Aug; 12():71. PubMed ID: 26303933
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface.
    Moaveninejad S; D'Onofrio V; Tecchio F; Ferracuti F; Iarlori S; Monteriù A; Porcaro C
    Comput Methods Programs Biomed; 2024 Feb; 244():107944. PubMed ID: 38064955
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals.
    Tayeb Z; Fedjaev J; Ghaboosi N; Richter C; Everding L; Qu X; Wu Y; Cheng G; Conradt J
    Sensors (Basel); 2019 Jan; 19(1):. PubMed ID: 30626132
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs.
    Luo J; Shi W; Lu N; Wang J; Chen H; Wang Y; Lu X; Wang X; Hei X
    J Neural Eng; 2021 Mar; 18(3):. PubMed ID: 33540387
    [No Abstract]   [Full Text] [Related]  

  • 14. Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization.
    Yu S; Mao B; Zhou Y; Liu Y; Yi C; Li F; Yao D; Xu P; San Liang X; Zhang T
    IEEE Trans Neural Syst Rehabil Eng; 2024; 32():2187-2197. PubMed ID: 38837930
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding.
    Xue J; Ren F; Sun X; Yin M; Wu J; Ma C; Gao Z
    Neural Plast; 2020; 2020():8863223. PubMed ID: 33505456
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Classification of EEG signals related to real and imagery knee movements using deep learning for brain computer interfaces.
    Lee Y; Lee HJ; Tae KS
    Technol Health Care; 2023; 31(3):933-942. PubMed ID: 36617798
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation.
    Yao L; Sheng X; Zhang D; Jiang N; Mrachacz-Kersting N; Zhu X; Farina D
    IEEE Trans Neural Syst Rehabil Eng; 2017 Sep; 25(9):1674-1682. PubMed ID: 28328506
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A review of critical challenges in MI-BCI: From conventional to deep learning methods.
    Khademi Z; Ebrahimi F; Kordy HM
    J Neurosci Methods; 2023 Jan; 383():109736. PubMed ID: 36349568
    [TBL] [Abstract][Full Text] [Related]  

  • 19. GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.
    Hou Y; Jia S; Lun X; Hao Z; Shi Y; Li Y; Zeng R; Lv J
    IEEE Trans Neural Netw Learn Syst; 2024 Jun; 35(6):7312-7323. PubMed ID: 36099220
    [TBL] [Abstract][Full Text] [Related]  

  • 20. MI-CAT: A transformer-based domain adaptation network for motor imagery classification.
    Zhang D; Li H; Xie J
    Neural Netw; 2023 Aug; 165():451-462. PubMed ID: 37336030
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.