198 related articles for article (PubMed ID: 34080925)
21. Automated FBSE-EWT based learning framework for detection of epileptic seizures using time-segmented EEG signals.
Anuragi A; Sisodia DS; Pachori RB
Comput Biol Med; 2021 Sep; 136():104708. PubMed ID: 34358996
[TBL] [Abstract][Full Text] [Related]
22. A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis.
Movahed RA; Jahromi GP; Shahyad S; Meftahi GH
J Neurosci Methods; 2021 Jul; 358():109209. PubMed ID: 33957158
[TBL] [Abstract][Full Text] [Related]
23. Depression recognition using machine learning methods with different feature generation strategies.
Li X; Zhang X; Zhu J; Mao W; Sun S; Wang Z; Xia C; Hu B
Artif Intell Med; 2019 Aug; 99():101696. PubMed ID: 31606115
[TBL] [Abstract][Full Text] [Related]
24. A Deep Learning-Based Classification Method for Different Frequency EEG Data.
Wen T; Du Y; Pan T; Huang C; Zhang Z
Comput Math Methods Med; 2021; 2021():1972662. PubMed ID: 34721654
[TBL] [Abstract][Full Text] [Related]
25. Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.
Perera D; Wang YK; Lin CT; Nguyen H; Chai R
Sensors (Basel); 2022 Aug; 22(16):. PubMed ID: 36015991
[TBL] [Abstract][Full Text] [Related]
26. Measuring time-varying information flow in scalp EEG signals: orthogonalized partial directed coherence.
Omidvarnia A; Azemi G; Boashash B; O'Toole JM; Colditz PB; Vanhatalo S
IEEE Trans Biomed Eng; 2014 Mar; 61(3):680-93. PubMed ID: 24144656
[TBL] [Abstract][Full Text] [Related]
27. A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia From EEG Connectivity Patterns.
Phang CR; Noman F; Hussain H; Ting CM; Ombao H
IEEE J Biomed Health Inform; 2020 May; 24(5):1333-1343. PubMed ID: 31536026
[TBL] [Abstract][Full Text] [Related]
28. A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals.
Acharya UR; Sudarshan VK; Adeli H; Santhosh J; Koh JE; Puthankatti SD; Adeli A
Eur Neurol; 2015; 74(1-2):79-83. PubMed ID: 26303033
[TBL] [Abstract][Full Text] [Related]
29. Comparison of different cortical connectivity estimators for high-resolution EEG recordings.
Astolfi L; Cincotti F; Mattia D; Marciani MG; Baccala LA; de Vico Fallani F; Salinari S; Ursino M; Zavaglia M; Ding L; Edgar JC; Miller GA; He B; Babiloni F
Hum Brain Mapp; 2007 Feb; 28(2):143-57. PubMed ID: 16761264
[TBL] [Abstract][Full Text] [Related]
30. EEG-based image classification via a region-level stacked bi-directional deep learning framework.
Fares A; Zhong SH; Jiang J
BMC Med Inform Decis Mak; 2019 Dec; 19(Suppl 6):268. PubMed ID: 31856818
[TBL] [Abstract][Full Text] [Related]
31. Computer-Aided Diagnosis of Depression Using EEG Signals.
Acharya UR; Sudarshan VK; Adeli H; Santhosh J; Koh JE; Adeli A
Eur Neurol; 2015; 73(5-6):329-36. PubMed ID: 25997732
[TBL] [Abstract][Full Text] [Related]
32. A nonlinear causality measure in the frequency domain: nonlinear partial directed coherence with applications to EEG.
He F; Billings SA; Wei HL; Sarrigiannis PG
J Neurosci Methods; 2014 Mar; 225():71-80. PubMed ID: 24472530
[TBL] [Abstract][Full Text] [Related]
33. 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]
34. A deep learning framework for automatic diagnosis of unipolar depression.
Mumtaz W; Qayyum A
Int J Med Inform; 2019 Dec; 132():103983. PubMed ID: 31586827
[TBL] [Abstract][Full Text] [Related]
35. Dimension reduction of frequency-based direct Granger causality measures on short time series.
Siggiridou E; Kimiskidis VK; Kugiumtzis D
J Neurosci Methods; 2017 Sep; 289():64-74. PubMed ID: 28687522
[TBL] [Abstract][Full Text] [Related]
36. Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease.
Kim S; Kim J; Chun HW
Int J Environ Res Public Health; 2018 Aug; 15(8):. PubMed ID: 30111710
[TBL] [Abstract][Full Text] [Related]
37. Exploring deep residual network based features for automatic schizophrenia detection from EEG.
Siuly S; Guo Y; Alcin OF; Li Y; Wen P; Wang H
Phys Eng Sci Med; 2023 Jun; 46(2):561-574. PubMed ID: 36947384
[TBL] [Abstract][Full Text] [Related]
38. AMGCN-L: an adaptive multi-time-window graph convolutional network with long-short-term memory for depression detection.
Wang HG; Meng QH; Jin LC; Hou HR
J Neural Eng; 2023 Oct; 20(5):. PubMed ID: 37844566
[No Abstract] [Full Text] [Related]
39. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.
Hosseinifard B; Moradi MH; Rostami R
Comput Methods Programs Biomed; 2013 Mar; 109(3):339-45. PubMed ID: 23122719
[TBL] [Abstract][Full Text] [Related]
40. Toward optimal feature and time segment selection by divergence method for EEG signals classification.
Wang J; Feng Z; Lu N; Luo J
Comput Biol Med; 2018 Jun; 97():161-170. PubMed ID: 29747059
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]