Evaluating the EEG and Eye Movements for Autism Spectrum Disorder

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

“…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…”

“…However, it is hard to perform when the dataset contains long duration signals from many subjects. Visual inspection was used in (Thapaliya et al, 2018) as a pre-processing step in classifying ASD. Table 2 summarizes different pre-processing techniques used to process the EEG signal.…”

“…Standard deviation and mean are the commonly used statistical features. Statistical features were used in (Bosl et al, 2018;Cheong et al, 2015;Djemal et al, 2017;Thapaliya et al, 2018) to classify ASD.…”

“…There are many entropy-based methods such as sample entropy, Shannon entropy, multiscale entropy and modified multiscale entropy. Entropy has been used in (Bosl et al, 2018;Djemal et al, 2017;Thapaliya et al, 2018) for the diagnosis of ASD. Several EEG-based features for ASD classification including EEG rhythm, absolute and relative power, coherence, mu wave suppression, cordance and multiscale entropy have been discussed in (Hashemian and Pourghassem, 2014).…”

“…Several EEG-based features for ASD classification including EEG rhythm, absolute and relative power, coherence, mu wave suppression, cordance and multiscale entropy have been discussed in (Hashemian and Pourghassem, 2014). (Thapaliya et al, 2018) X X X X Classifying ASD using MS-ROM/I-FAST algorithm (Grossi et al, 2017) X X ASD diagnosis using DWT, Shannon entropy and ANN (Djemal et al, 2017) X X Wavelet-based ASD classification (Cheong et al, 2015) X ASD diagnosis utilizing brain connectivity (Jamal et al, 2014) X X Fuzzy synchronization likelihoodmethodology for ASD diagnosis (Ahmadlou et al, 2012a) X X ASD diagnosis based on Improved visibility graph fractality (Ahmadlou et al, 2012b) X X EEG as a biomarker for distinguishing ASD children (W. Bosl et al, 2011) X Classification of ASD using fractal dimensions (Ahmadlou et al, 2010) X X ASDGenus: channel optimized classification using EEG (Haputhanthri et al, 2019) X X…”