Elly Matul Imah, Erina Seviyanti Dewi, I.G.P. Asto Buditjahjanto
Electroencephalography (EEG) is a method for recording the electrical activity of the brain. In the EEG various frequency signals can analyse the brain and the brain's behaviour. EEG can detect the abnormalities in the brain, one of the abnormality is Autism Spectrum Disorder (ASD). ASD is a condition where a person has a combination of neurological disorders in social communication and behavior, limited interest in something and sensory behavior. Joint attention (JA) is the ability in sharing attention between interactive social partner with third external elements as objects or events. Joint attention can also be said as social interaction behaviors to follow the attention of others, and direct attention of another. Joint attention is the key point affecting social communication in individuals with autism spectrum disorder (ASD). In this study, the detection of the ability of ASD sufferers to respond to instructions to see a targeted object based on the EEG signal recording was conducted. The dataset used is BCIAUT P-300 that is non-linear separable and imbalanced class with a ratio of 1: 8. In handling the imbalanced data, undersampling was applied. The feature extraction method that compared are wavelet and principal component analysis. Based on the experiment result, Pz has the best channel to classify the Joint Attention of ASD using EEG P-300 data. The best accuracy is GRLVQ and the best G-mean is SVM. Over all the results, based on accuracy, g-mean, training and testing time show that GRLVQ is better performance than others, and SVM is the runner up. The differences of accuracy 9%, training time GRLVQ faster around 45 seconds than SVM, and testing time faster around 3 seconds. © 2021. All Rights Reserved.
Mathematics Department, Universitas Negeri Surabaya, Indonesia; Electrical Engineering Department, Universitas Negeri Surabaya, Indonesia
