(untitled) (ELEKTRIKA- Journal of Electrical Engineering, 2024)
This paper delves into a study employing dry electroencephalograms (EEG) as input features to discern emotions in individuals with autism spectrum disorders (ASD). While EEG is a prevalent tool in emotion classification studies for typically developing individuals, less attention has been directed towards its application in the ASD population. In this study, ten participants diagnosed with ASD wore wireless dry EEG sensors to capture their EEG signals. These signals encompassed alpha, beta, delta, theta, and gamma waves, which were subsequently subjected to feature extraction techniques such as the t-test, principal component analysis (PCA), ReliefF, and Chi-Square. Classification of positive, neutral, and negative emotions was performed using various algorithms, including K-nearest neighbor (KNN), Multinomial Logistic Regression (MLR), Naive Bayes (NB), Random Tree (RT), Random Forest (RF), and Support Vector Machine (SVM). Ultimately, employing SVM with a t-test enhanced the accuracy of emotion classification for the ASD group from 66.4% to 74.1%.