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

Year 2017, Volume: 9 Issue: 3, 186 - 195, 26.12.2017

Mustafa Turan Arslan , Server Göksel Eraldemir , Esen Yıldırım

https://doi.org/10.29137/umagd.348871

Cited By: 7

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.

Keywords

EEG classification, Hilbert Huang Transform, k-Nearest Neighbor

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