Speaker
Description
The primary objective of this study was to determine the feasibility of classifying emotions into three categories (positive, negative, and neutral) using event-related potentials (ERPs) for individual users. Visual stimuli from the International Affective Picture System (IAPS) database were utilized. Various features, such as signal samples, discrete wavelet transform, discrete Fourier transform, and discrete cosine transform, were computed from one-second electroencephalographic signal (EEG) segments following the presentation of the stimulus. For the classification task, a one-nearest neighbor classifier (1-NN) was employed. The research yielded a system for preprocessing and classifying emotions. The study involved eight participants. The experiments presented in this paper demonstrate the possibility of distinguishing emotions into three categories (pleasant, unpleasant, and neutral) for a single user, achieving an average accuracy level of 87%. However, when considering all users collectively, we achieved a classification accuracy of 96%.
