Electroencephalographic studies in children with autism spectrum disorders

Strzelecka, Jolanta

doi:10.1016/j.rasd.2013.11.010

Cited by 8 publications

(5 citation statements)

References 49 publications

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“…However, resting state quantitative EEG (qEEG) spectral power across the entire frequency spectrum from youth and adults with ASD versus unaffected controls has been summarized as an inverted-U shaped curve, with excessive power in low- and high-frequency bands and reduced power in the alpha band, hypothesized as consistent with dysfunctional gamma-aminobutyric acid (GABAergic) inhibitory tone and its effects on connectivity ( Wang et al, 2013 ). Advances in analytic approaches have revived interest in EEG as a possible diagnostic/risk biomarker and a window into ASD’s neurobiology ( Strzelecka, 2014 ; Schwartz et al, 2016 ; Heunis et al, 2016 , 2018 ; Shou et al, 2018 ). but with much variability across studies (Lefebvre et al, 2018).…”

Section: Opportunities For Asd Clinical Trial Improvementsmentioning

confidence: 99%

Drug development for Autism Spectrum Disorder (ASD): Progress, challenges, and future directions

McCracken

Anagnostou

Arango

et al. 2021

European Neuropsychopharmacology

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Section: Opportunities For Asd Clinical Trial Improvementsmentioning

confidence: 99%

Drug development for Autism Spectrum Disorder (ASD): Progress, challenges, and future directions

McCracken

Anagnostou

Arango

et al. 2021

European Neuropsychopharmacology

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“…The second reason is the former Figure 3. Location of electrodes in international [10][11][12][13][14][15][16][17][18][19][20] system; the used channels are shown with gray color and red line and sound room. studies based on the importance of mirror and MU rhythm in ASD studies [10,[18][19][20][21][22][23][24].…”

Section: De Nitionsmentioning

confidence: 99%

“…In this eld, widespread studies have been carried out using EEG in which di erent tools are applied to investigate asymmetry [8][9][10][11][12][13][14], and di erent indexes are introduced as biological indexes, some of which are mentioned in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Poincaré Section based Biomarkers of Hemispheric Asymmetry, applied to Autism Spectrum Disorder

Bajestani¹,

Sheikhani²,

Golpayegani³

et al. 2017

Scientia Iranica

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Abstract. Asymmetry and symmetry coexist in natural and human processes, and the interaction of asymmetric action (recursion) and symmetric opposition (sinusoidal waves) is instrumental in generating creative features. Autism Spectrum Disorder (ASD) is a disorder in which asymmetry and functionality of brain hemispheres are a ected. In this study, di erence in brain asymmetry in ASD and normal children and the e ect of voice on asymmetry are being investigated. Due to abnormal cortical voice processing in ASD, data recording is done in two situations: animation with audio (V-A) for 5 minutes and watching the animation with muted audio band (VwA). Two Indexes Divergence (D) and number of Poincar e section points further from threshold (HD) as new biomarkers are being extracted. Hemispheric asymmetry in ASD children does not follow norm patterns, and H and HD indexes con rm a disorder in hemisphere's functionality in all statistical tests which can be globally unveiled with Poincar e section and extracted information. Two remarkable features of the presented method are: data recording protocol specialized for ASD children and new practical time-series analysis for detecting episodic patterns (complexes) as hallmark of ASD dynamic and arrangement as an empirical measure of nonrandom complexity. The presented method could detect these complexes.

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“…Detecting autism through EEG signals is a crucial aspect of neuroscience research, with various studies exploring different approaches in this area. Some research papers have used machine learning techniques to analyze EEG data, achieving high accuracy in distinguishing between individuals with autism and those without it [17][18][19][20][21][22][23]. Others have suggested deep learning methods that extract key features from EEG signals, showing promising results in autism detection [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Autistic recognition from EEG signals by extracted features from several time series models

Rajabioun

2024

Preprint

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Autism is a neurological and psychological disorder that typically manifests in childhood and persists into adulthood. It is characterized by atypical social, behavioral, and communication skills, as well as diminished attention to the surrounding environment. The detection and recognition of autism can contribute to the prevention of its development and the enhancement of social and communicational abilities. Various methods are employed for autism recognition, including questionnaire tests and neurological techniques. One such neuroimaging method is electroencephalography (EEG), which records the brain's electrical activities through sensors placed on the scalp. This paper proposes a method for identifying individuals with autism using EEG signals and features extracted from a multivariate autoregressive moving average (MVARMA) and multivariate integrated autoregressive(ARIMA) models. The approach begins by estimating active sources through source localization methods, followed by the application of a dual Kalman filter to estimate source activity. Subsequently, the MVARMA and ARIMA models are applied to the EEG sensor and active source data, enabling the calculation of model parameters. Principal component analysis is then utilized to select important parameters, and a K nearest neighbor classifier is employed to classify participants as either autistic or neurotypical. The results demonstrate superior classification performance, achieving higher accuracy compared to alternative methods. The proposed method yields superior classification outcomes when compared to other approaches, as it exhibits improved classification measures.

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Electroencephalographic studies in children with autism spectrum disorders

Cited by 8 publications

References 49 publications

Drug development for Autism Spectrum Disorder (ASD): Progress, challenges, and future directions

Drug development for Autism Spectrum Disorder (ASD): Progress, challenges, and future directions

Poincaré Section based Biomarkers of Hemispheric Asymmetry, applied to Autism Spectrum Disorder

Autistic recognition from EEG signals by extracted features from several time series models

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