Subject-Dependent and Subject-Independent Classification of Mental Arithmetic and Silent Reading Tasks

Abstract

In this study, the electrical activities in the brain were classified during mental mathematical tasks and silent text reading. EEG recordings are collected from 18 healthy male university/college students, ages ranging from 18 to 25. During the study, a total of 60 slides including verbal text reading and arithmetical operations were presented to the subjects. EEG signals were collected from 26 channels in the course of slide show. Features were extracted by employing Hilbert Huang Transform (HHT). Then, subject-dependent and subject-independent classifications were performed using k-Nearest Neighbor (k-NN) algorithm with parameters k=1, 3, 5 and 10. Subject-dependent classifications resulted in accuracy rates between 95.8% and 99%, whereas the accuracy rates were between 92.2% and 97% for subject independent classification. The results show that EEG data recorded during mathematical and silent reading tasks can be classified with high accuracy results for both subject-dependent and subject-independent analysis.