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The three main Hjorth parameters, i.e., activity, mobility, and complexity, values were calculated for all channels of EEG signals. The feature extracted from EEG data were fed into the Neural Network (NN) model, which allowed classification for ASD and control participants.
Jun 11, 2023
Autism Spectrum Disorder Detection from EEG Through Hjorth Parameters and Classification Using Neural Network. June 2023. DOI:10.1007/978-3-031-34622-4_3. In�...
Addressing the challenge of dataset acquisition for ASD diagnosis with deep learning-based neural networks. Conference Paper. Jan 2024.
Feb 13, 2023 � This paper presents a novel application of the variational mode decomposition (VMD) technique in a BCI system involving ASD subjects for P300 signal�...
Jun 22, 2021 � This study extracts a wider range of 20 types of EEG features for utilization in autism screening and analyzes their effectiveness in�...
Oct 3, 2022 � In this study, we aimed to find biomarkers of autism in young children. We recorded magnetoencephalography (MEG) in thirty children (4–7 years) with autism.
Some of the key findings in current research on Autism detection through EEG signals using ML are highlighted in this section. Table 1 provides a.
Sep 21, 2024 � Abstract. This paper investigates the potential use of machine learning techniques in the analysis of Electroencephalography (EEG) time�...
Nov 8, 2022 � We recorded five minutes eyes-closed rest electroencephalography (EEG) from 186 adults (51% with ASD and 49% without ASD) and investigated the potential of EEG�...
Din, Automated classification of autism spectrum disorder using EEG signal and convolutional neural networks, Biomed. Eng.: Applications, Basis and�...