Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study

Grossi, Enzo; Olivieri, Chiara; Buscema, Massimo

doi:10.1016/j.cmpb.2017.02.002

Cited by 109 publications

(83 citation statements)

References 31 publications

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“…EEG signals have been commonly used in medicine to diagnose a diversity of pathological conditions and disorders [9], [10], [11], [12], [13], but their use has recently been extended to other fields of research. At the beginning of this century, EEG correlates of emotions reported in a number of neuropsychological studies [14] also motivated their use in the emotion (affect) recognition field [15], [16], [17], [18].…”

Section: State-of-the-artmentioning

confidence: 99%

On the Influence of Affect in EEG-Based Subject Identification

Arnau-González

Arevalillo‐Herráez

Katsigiannis

et al. 2021

IEEE Trans. Affective Comput.

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Biometric signals have been extensively used for user identification and authentication due to their inherent characteristics that are unique to each person. The variation exhibited between the brain signals (EEG) of different people makes such signals especially suitable for biometric user identification. However, the characteristics of these signals are also influenced by the user's current condition, including his/her affective state. In this paper, we analyze the significance of the affect-related component of brain signals within the subject identification context. Consistent results are obtained across three different public datasets, suggesting that the dominant component of the signal is subject-related, but the affective state also has a contribution that affects identification accuracy. Results show that identification accuracy increases when the system has been trained with EEG recordings that refer to similar affective states as the sample that is to be identified. This improvement holds independently of the features and classification algorithm used, and it is generally above 10% under a rigorous setting, when the training and validation datasets do not share data from the same recording days. This finding emphasizes the potential benefits of considering affective information in applications that require subject identification, such as user authentication.

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Section: State-of-the-artmentioning

confidence: 99%

On the Influence of Affect in EEG-Based Subject Identification

Arnau-González

Arevalillo‐Herráez

Katsigiannis

et al. 2021

IEEE Trans. Affective Comput.

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“…Signal decomposition using DWT is shown in Fig. 2 (Bosl et al, 2018) O X ASD classification using EEG and eye movement (Thapaliya et al, 2018) X X Classifying ASD using MS-ROM/I-FAST algorithm (Grossi et al, 2017) X ASD diagnosis using DWT, Shannon entropy and ANN (Djemal et al, 2017) O X X X Wavelet-based ASD classification (Cheong et al, 2015) O X X ASD diagnosis utilizing brain connectivity (Jamal et al, 2014) X X O Fuzzy synchronization likelihood methodology for ASD diagnosis (Ahmadlou et al, 2012a) O ASD diagnosis based on improved visibility graph fractality (Ahmadlou et al, 2012b) O X EEG as a biomarker for distinguishing ASD children (Bosl et al, 2011) X Classification of ASD using fractal dimensions (Ahmadlou et al, 2010) O X Frequency 3D mapping and interchannel stability of EEG as indicators towards ASD diagnosis (Abdulhay et al, 2017) X X O O Diagnosing ASD utilizing EEG spectral coherence (Duffy and Als, 2012) X X X X ASDGenus: channel optimised classification using EEG (Haputhanthri et al, 2019) X O X…”

Section: Wavelet-based Analysismentioning

confidence: 99%

“…Logistic regression is simple, easy to implement and does not require extreme computational power. Authors of (Thapaliya et al, 2018;Grossi et al, 2017) have used logistic regression to diagnose ASD.…”

Section: Logistic Regressionmentioning

confidence: 99%

EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Brihadiswaran¹,

Haputhanthri²,

Gunathilaka³

et al. 2019

Journal of Computer Science

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Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents a survey of major EEG-based ASD classification approaches from 2010 to 2018, which adopt machine learning. The methodology is divided into four phases: EEG data collection, pre-processing, feature extraction and classification. This study explores different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model. We analyze the strengths and weaknesses of the techniques and tools. Further, this study summarizes the ASD classification approaches and discusses the existing challenges, limitations and future directions.

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“…This test provides evidence of how the brain works over time, which is related to bodily functions such as the pumping of the heart, gland secretion, breathing, internal temperature, etc. EEGs are commonly used by scientists and physicians for analyzing brain functions and diagnosing neurological disorders, such as brain tumors, head injuries, sleep disorders, dementia, epilepsy, Alzheimer's, seizure disorder, attention deficit disorder, anxiety disorder, fetal alcohol syndrome, and autism, as well as monitoring the effects of anesthesia during surgery [22,12,15,9,8].…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Analysis of EEG Spectrograms using 2-D Gabor Filters for Epileptic Seizure Classification

Ramos-Aguilar¹,

Olvera-López²,

Olmos-Pineda³

et al. 2018

RCS

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EEG analysis of epileptic seizure is a nonstationary and changing process; these EEGs contain multiple frequencies and use only conventional methods based on frequency or time which limit their analysis. In this paper, texture representation of Gabor filter response based on a spectrogram applying a STFT is proposed to classify epileptic and healthy states. As initial part of our research, energy and entropy were extracted from the Gabor filters response; these features and statistical values were employed to train Support Vector Machines and multilayer Perceptron classifiers.

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Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study

Cited by 109 publications

References 31 publications

On the Influence of Affect in EEG-Based Subject Identification

On the Influence of Affect in EEG-Based Subject Identification

EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Time-Frequency Analysis of EEG Spectrograms using 2-D Gabor Filters for Epileptic Seizure Classification

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