159 related articles for article (PubMed ID: 36848586)
1. Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.
Gupta A; Daniel R; Rao A; Roy PP; Chandra S; Kim BG
Big Data; 2023 Aug; 11(4):307-319. PubMed ID: 36848586
[TBL] [Abstract][Full Text] [Related]
2. Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning.
Gupta A; Siddhad G; Pandey V; Roy PP; Kim BG
Sensors (Basel); 2021 Oct; 21(20):. PubMed ID: 34695921
[TBL] [Abstract][Full Text] [Related]
3. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update.
Lotte F; Bougrain L; Cichocki A; Clerc M; Congedo M; Rakotomamonjy A; Yger F
J Neural Eng; 2018 Jun; 15(3):031005. PubMed ID: 29488902
[TBL] [Abstract][Full Text] [Related]
4. EEG-based detection of mental workload level and stress: the effect of variation in each state on classification of the other.
Bagheri M; Power SD
J Neural Eng; 2020 Oct; 17(5):056015. PubMed ID: 32987366
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Classification of mental workload using brain connectivity and machine learning on electroencephalogram data.
Safari M; Shalbaf R; Bagherzadeh S; Shalbaf A
Sci Rep; 2024 Apr; 14(1):9153. PubMed ID: 38644365
[TBL] [Abstract][Full Text] [Related]
8. Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain-Computer Interface.
Bagheri M; Power SD
Sensors (Basel); 2022 Jan; 22(2):. PubMed ID: 35062495
[TBL] [Abstract][Full Text] [Related]
9. Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI.
Fahimi F; Zhang Z; Goh WB; Lee TS; Ang KK; Guan C
J Neural Eng; 2019 Apr; 16(2):026007. PubMed ID: 30524056
[TBL] [Abstract][Full Text] [Related]
10. EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT.
Shao S; Han G; Wang T; Lin C; Song C; Yao C
IEEE J Biomed Health Inform; 2024 May; 28(5):2536-2546. PubMed ID: 37276109
[TBL] [Abstract][Full Text] [Related]
11. NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework.
Cho JH; Jeong JH; Lee SW
IEEE Trans Cybern; 2022 Dec; 52(12):13279-13292. PubMed ID: 34748509
[TBL] [Abstract][Full Text] [Related]
12. Motor imagery recognition with automatic EEG channel selection and deep learning.
Zhang H; Zhao X; Wu Z; Sun B; Li T
J Neural Eng; 2021 Feb; 18(1):. PubMed ID: 33181505
[No Abstract] [Full Text] [Related]
13. Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.
Xie Y; Oniga S
Sensors (Basel); 2023 Feb; 23(4):. PubMed ID: 36850530
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Imagined character recognition through EEG signals using deep convolutional neural network.
Ullah S; Halim Z
Med Biol Eng Comput; 2021 May; 59(5):1167-1183. PubMed ID: 33945075
[TBL] [Abstract][Full Text] [Related]
16. Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping.
Hill NJ; Gupta D; Brunner P; Gunduz A; Adamo MA; Ritaccio A; Schalk G
J Vis Exp; 2012 Jun; (64):. PubMed ID: 22782131
[TBL] [Abstract][Full Text] [Related]
17. Golden subject is everyone: A subject transfer neural network for motor imagery-based brain computer interfaces.
Sun B; Wu Z; Hu Y; Li T
Neural Netw; 2022 Jul; 151():111-120. PubMed ID: 35405471
[TBL] [Abstract][Full Text] [Related]
18. An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task.
Lashgari E; Ott J; Connelly A; Baldi P; Maoz U
J Neural Eng; 2021 Aug; 18(4):. PubMed ID: 34352734
[No Abstract] [Full Text] [Related]
19. Evaluation of synchronization measures for capturing the lagged synchronization between EEG channels: A cognitive task recognition approach.
Olcay BO; Karaçalı B
Comput Biol Med; 2019 Nov; 114():103441. PubMed ID: 31561099
[TBL] [Abstract][Full Text] [Related]
20. Electroencephalogram classification in motor-imagery brain-computer interface applications based on double-constraint nonnegative matrix factorization.
Su J; Yang Z; Yan W; Sun W
Physiol Meas; 2020 Aug; 41(7):075007. PubMed ID: 32590360
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
