180 related articles for article (PubMed ID: 35557842)
1. Deep Learning-Based Multilevel Classification of Alzheimer's Disease Using Non-invasive Functional Near-Infrared Spectroscopy.
Ho TKK; Kim M; Jeon Y; Kim BC; Kim JG; Lee KH; Song JI; Gwak J
Front Aging Neurosci; 2022; 14():810125. PubMed ID: 35557842
[TBL] [Abstract][Full Text] [Related]
2. Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain-Computer Interface Application.
Akhter J; Naseer N; Nazeer H; Khan H; Mirtaheri P
Sensors (Basel); 2024 May; 24(10):. PubMed ID: 38793895
[TBL] [Abstract][Full Text] [Related]
3. An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease.
Cicalese PA; Li R; Ahmadi MB; Wang C; Francis JT; Selvaraj S; Schulz PE; Zhang Y
J Neurosci Methods; 2020 Apr; 336():108618. PubMed ID: 32045572
[TBL] [Abstract][Full Text] [Related]
4. Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.
Asgher U; Khalil K; Khan MJ; Ahmad R; Butt SI; Ayaz Y; Naseer N; Nazir S
Front Neurosci; 2020; 14():584. PubMed ID: 32655353
[TBL] [Abstract][Full Text] [Related]
5. Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks.
Hamid H; Naseer N; Nazeer H; Khan MJ; Khan RA; Shahbaz Khan U
Sensors (Basel); 2022 Mar; 22(5):. PubMed ID: 35271077
[TBL] [Abstract][Full Text] [Related]
6. Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy.
Yang D; Huang R; Yoo SH; Shin MJ; Yoon JA; Shin YI; Hong KS
Front Aging Neurosci; 2020; 12():141. PubMed ID: 32508627
[TBL] [Abstract][Full Text] [Related]
7. Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach.
Yang D; Hong KS
J Alzheimers Dis; 2021; 80(2):647-663. PubMed ID: 33579839
[TBL] [Abstract][Full Text] [Related]
8. Early Detection of Alzheimer's Disease Using Non-invasive Near-Infrared Spectroscopy.
Li R; Rui G; Chen W; Li S; Schulz PE; Zhang Y
Front Aging Neurosci; 2018; 10():366. PubMed ID: 30473662
[TBL] [Abstract][Full Text] [Related]
9. A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis.
Mahavar A; Patel A; Patel A
Curr Top Med Chem; 2024 Jun; ():. PubMed ID: 38847164
[TBL] [Abstract][Full Text] [Related]
10. Deep Learning Approach for Early Detection of Alzheimer's Disease.
Helaly HA; Badawy M; Haikal AY
Cognit Comput; 2022; 14(5):1711-1727. PubMed ID: 34745371
[TBL] [Abstract][Full Text] [Related]
11. A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks.
Ramzan F; Khan MUG; Rehmat A; Iqbal S; Saba T; Rehman A; Mehmood Z
J Med Syst; 2019 Dec; 44(2):37. PubMed ID: 31853655
[TBL] [Abstract][Full Text] [Related]
12. Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface.
Khan H; Khadka R; Sultan MS; Yazidi A; Ombao H; Mirtaheri P
Front Hum Neurosci; 2024; 18():1354143. PubMed ID: 38435744
[TBL] [Abstract][Full Text] [Related]
13. Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy.
Xu L; Liu Y; Yu J; Li X; Yu X; Cheng H; Li J
J Neurosci Methods; 2020 Feb; 331():108538. PubMed ID: 31794776
[TBL] [Abstract][Full Text] [Related]
14. Benchmarking framework for machine learning classification from fNIRS data.
Benerradi J; Clos J; Landowska A; Valstar MF; Wilson ML
Front Neuroergon; 2023; 4():994969. PubMed ID: 38234474
[TBL] [Abstract][Full Text] [Related]
15. Transformer Model for Functional Near-Infrared Spectroscopy Classification.
Wang Z; Zhang J; Zhang X; Chen P; Wang B
IEEE J Biomed Health Inform; 2022 Jun; 26(6):2559-2569. PubMed ID: 34986110
[TBL] [Abstract][Full Text] [Related]
16. Empirical comparison of deep learning models for fNIRS pain decoding.
Fernandez Rojas R; Joseph C; Bargshady G; Ou KL
Front Neuroinform; 2024; 18():1320189. PubMed ID: 38420133
[TBL] [Abstract][Full Text] [Related]
17. Explainable artificial intelligence model to predict brain states from fNIRS signals.
Shibu CJ; Sreedharan S; Arun KM; Kesavadas C; Sitaram R
Front Hum Neurosci; 2022; 16():1029784. PubMed ID: 36741783
[No Abstract] [Full Text] [Related]
18. Functional near-infrared spectroscopy in elderly patients with four types of dementia.
Mei X; Zou CJ; Hu J; Liu XL; Zheng CY; Zhou DS
World J Psychiatry; 2023 May; 13(5):203-214. PubMed ID: 37303929
[TBL] [Abstract][Full Text] [Related]
19. Diagnosis of Mild Cognitive Impairment Using Cognitive Tasks: A Functional Near-Infrared Spectroscopy Study.
Yoo SH; Woo SW; Shin MJ; Yoon JA; Shin YI; Hong KS
Curr Alzheimer Res; 2020; 17(13):1145-1160. PubMed ID: 33583382
[TBL] [Abstract][Full Text] [Related]
20. Screening for Alzheimer's disease using prefrontal resting-state functional near-infrared spectroscopy.
Keles HO; Karakulak EZ; Hanoglu L; Omurtag A
Front Hum Neurosci; 2022; 16():1061668. PubMed ID: 36518566
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]