Detecting learning disabilities based on neuro-physiological interface of affect (NPIoA) (original) (raw)
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Journal of Computational and Theoretical Nanoscience, 2019
Children with autism spectrum disorder (ASD) is likely to have repetitive and restricted repertoire in its behaviors, activities and interests. Early detection and intervention of ASD can help these children to lead an almost normal life. Thus it is important to ensure that early detection of such ASD preschoolers can be carried out. The brain connectivity of ASD can be achieved better by capturing and analyzing through the EEG and machine learning. In this paper we presented both the time domain approach, which were used by most researchers to identify ASD and also the neurophysiological interface of affect (NPIA) at resting state. There seems to be consistency in results based on the NPIA at resting state for eyes opened and eyes closed while using time domain approach shows otherwise. Therefore, both models can be used to have a better accuracy in diagnosing an ASD. Future works also can have the NPIA model approaches on the other learning disabilities.
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IETE Journal of Research, 2019
Autism spectrum disorder is a brain disorder in children and is detected using magnetic resonance imaging scan and electroencephalography (EEG) signals. This paper presents a system to detect autism using EEG signals. It uses pre-recorded EEG signal, preprocesses it using a digital filter, and extracts features in time and frequency domain using discrete wavelet transform. The features obtained are given to classifiers such as neural networks, support vector machine, K-nearest neighbour (KNN), subspace KNN, and linear discriminant analysis. The results show that the use of subspace KNN provides the best accuracy of 92.8% for time-domain features.
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Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder
Computational Models for Biomedical Reasoning and Problem Solving, 2019
Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the electric signals of the brain via electrodes placed on various places on the scalp. These signals can be used to study complex neuropsychiatric issues. Studies have shown that EEG has the potential to be used as a biomarker for various neurological conditions including ASD. This chapter will outline the usage of EEG measurement for the classification of ASD using machine learning ...
Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study
Mathematical and Computational Applications, 2022
Autism Spectrum Disorder (ASD) is a neurodevelopmental life condition characterized by problems with social interaction, low verbal and non-verbal communication skills, and repetitive and restricted behavior. People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. Attention is a process that occurs at the cognitive level and allows us to orient ourselves towards relevant stimuli, ignoring those that are not, and act accordingly. This paper presents a methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD. The EEG signals are acquired with an Epoc+ Brain–Computer Interface (BCI) via the Emotiv Pro platform while developing several learning activities and using Matlab 2019a for signal processing. For this article, we propose to use electrodes F3, F4, P7, and P8. Then, we calculate the band power spectrum density to detect the Theta Relative Power (TRP), Alpha Relative Power (ARP), Beta Relative Power (BRP), Theta–Beta Ratio (TBR), Theta–Alpha Ratio (TAR), and Theta/(Alpha+Beta), which are features related to attention detection and neurofeedback. We train and evaluate several machine learning (ML) models with these features. In this study, the multi-layer perceptron neural network model (MLP-NN) has the best performance, with an AUC of 0.9299, Cohen’s Kappa coefficient of 0.8597, Matthews correlation coefficient of 0.8602, and Hamming loss of 0.0701. These findings make it possible to develop better learning scenarios according to the person’s needs with ASD. Moreover, it makes it possible to obtain quantifiable information on their progress to reinforce the perception of the teacher or therapist.
(EEG) for Delineating Objective Measure of Autism Spectrum Disorder
2020
Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child’s normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person’s ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the electric signals of the brain via electrodes placed on various places on the scalp. These signals can be used to study complex neuropsychiatric issues. Studies have shown that EEG has the potential to be used as a biomarker for various neurological conditions including ASD. This chapter will outline the usage of EEG measurement for the classification of ASD using machine learning algorith...