216 related articles for article (PubMed ID: 32250854)
1. The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data.
Kang J; Han X; Song J; Niu Z; Li X
Comput Biol Med; 2020 May; 120():103722. PubMed ID: 32250854
[TBL] [Abstract][Full Text] [Related]
2. The study of the differences between low-functioning autistic children and typically developing children in the processing of the own-race and other-race faces by the machine learning approach.
Kang J; Han X; Hu JF; Feng H; Li X
J Clin Neurosci; 2020 Nov; 81():54-60. PubMed ID: 33222968
[TBL] [Abstract][Full Text] [Related]
3. Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation.
Zhao Z; Tang H; Zhang X; Qu X; Hu X; Lu J
J Med Internet Res; 2021 Aug; 23(8):e29328. PubMed ID: 34435957
[TBL] [Abstract][Full Text] [Related]
4. Early identification of autism spectrum disorder based on machine learning with eye-tracking data.
Wei Q; Dong W; Yu D; Wang K; Yang T; Xiao Y; Long D; Xiong H; Chen J; Xu X; Li T
J Affect Disord; 2024 Aug; 358():326-334. PubMed ID: 38615846
[TBL] [Abstract][Full Text] [Related]
5. Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis.
Alcañiz M; Chicchi-Giglioli IA; Carrasco-Ribelles LA; Marín-Morales J; Minissi ME; Teruel-García G; Sirera M; Abad L
Autism Res; 2022 Jan; 15(1):131-145. PubMed ID: 34811930
[TBL] [Abstract][Full Text] [Related]
6. [Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos].
Cheng R; Zhao Z; Hou WW; Zhou G; Liao HT; Zhang X; Li J
Zhongguo Dang Dai Er Ke Za Zhi; 2024 Feb; 26(2):151-157. PubMed ID: 38436312
[TBL] [Abstract][Full Text] [Related]
7. Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis.
Wei Q; Cao H; Shi Y; Xu X; Li T
J Biomed Inform; 2023 Jan; 137():104254. PubMed ID: 36509416
[TBL] [Abstract][Full Text] [Related]
8. Stimulus dependent neural oscillatory patterns show reliable statistical identification of autism spectrum disorder in a face perceptual decision task.
Castelhano J; Tavares P; Mouga S; Oliveira G; Castelo-Branco M
Clin Neurophysiol; 2018 May; 129(5):981-989. PubMed ID: 29554581
[TBL] [Abstract][Full Text] [Related]
9. Combined frequency-tagging EEG and eye tracking reveal reduced social bias in boys with autism spectrum disorder.
Vettori S; Dzhelyova M; Van der Donck S; Jacques C; Van Wesemael T; Steyaert J; Rossion B; Boets B
Cortex; 2020 Apr; 125():135-148. PubMed ID: 31982699
[TBL] [Abstract][Full Text] [Related]
10. Comparison of three different eye-tracking tasks for distinguishing autistic from typically developing children and autistic symptom severity.
Kou J; Le J; Fu M; Lan C; Chen Z; Li Q; Zhao W; Xu L; Becker B; Kendrick KM
Autism Res; 2019 Oct; 12(10):1529-1540. PubMed ID: 31369217
[TBL] [Abstract][Full Text] [Related]
11. Detecting High-Functioning Autism in Adults Using Eye Tracking and Machine Learning.
Yaneva V; Ha LA; Eraslan S; Yesilada Y; Mitkov R
IEEE Trans Neural Syst Rehabil Eng; 2020 Jun; 28(6):1254-1261. PubMed ID: 32356755
[TBL] [Abstract][Full Text] [Related]
12. Linear and nonlinear analysis of intrinsic mode function after facial stimuli presentation in children with autism spectrum disorder.
Bakheet D; Maharatna K
Comput Biol Med; 2021 Jun; 133():104376. PubMed ID: 33866255
[TBL] [Abstract][Full Text] [Related]
13. EEG-based multi-feature fusion assessment for autism.
Kang J; Zhou T; Han J; Li X
J Clin Neurosci; 2018 Oct; 56():101-107. PubMed ID: 30318070
[TBL] [Abstract][Full Text] [Related]
14. Identifying autism using EEG: unleashing the power of feature selection and machine learning.
Ranaut A; Khandnor P; Chand T
Biomed Phys Eng Express; 2024 Mar; 10(3):. PubMed ID: 38457850
[TBL] [Abstract][Full Text] [Related]
15. Automated ASD detection using hybrid deep lightweight features extracted from EEG signals.
Baygin M; Dogan S; Tuncer T; Datta Barua P; Faust O; Arunkumar N; Abdulhay EW; Emma Palmer E; Rajendra Acharya U
Comput Biol Med; 2021 Jul; 134():104548. PubMed ID: 34119923
[TBL] [Abstract][Full Text] [Related]
16. Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach.
Zhao Z; Wei J; Xing J; Zhang X; Qu X; Hu X; Lu J
J Autism Dev Disord; 2023 Mar; 53(3):934-946. PubMed ID: 35913654
[TBL] [Abstract][Full Text] [Related]
17. A study on EEG feature extraction and classification in autistic children based on singular spectrum analysis method.
Zhao J; Song J; Li X; Kang J
Brain Behav; 2020 Dec; 10(12):e01721. PubMed ID: 33125837
[TBL] [Abstract][Full Text] [Related]
18. Mobile detection of autism through machine learning on home video: A development and prospective validation study.
Tariq Q; Daniels J; Schwartz JN; Washington P; Kalantarian H; Wall DP
PLoS Med; 2018 Nov; 15(11):e1002705. PubMed ID: 30481180
[TBL] [Abstract][Full Text] [Related]
19. Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework.
Liu W; Li M; Yi L
Autism Res; 2016 Aug; 9(8):888-98. PubMed ID: 27037971
[TBL] [Abstract][Full Text] [Related]
20. The "MS-ROM/IFAST" Model, a Novel Parallel Nonlinear EEG Analysis Technique, Distinguishes ASD Subjects From Children Affected With Other Neuropsychiatric Disorders With High Degree of Accuracy.
Grossi E; Buscema M; Della Torre F; Swatzyna RJ
Clin EEG Neurosci; 2019 Sep; 50(5):319-331. PubMed ID: 31296052
[No Abstract] [Full Text] [Related]
[Next] [New Search]