Impact of age, sex and hair type on SSVEP-based EEG signals analysis

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

The Brain Computer Interface (BCI) creates an alternative communication channel for individuals who cannot speak or provide their own physical needs, despite having regular conscious and brain activity. The processing of individual Electroencephalogram (EEG) data is a frequently used method in the literature to create this alternative communication channel. In this paper, EEG data of subjects whose details are given in the following sections were examined by steady-state visually evoked potentials (SSVEP) technique and the impact of age, sex and hair type on the accuracy of classification of these data was investigated. The results show that high classification accuracy can be achieved with the implementation of SSVEP, and different demographic characteristics may affect this accuracy rate positively/negatively.
Original languageEnglish
Title of host publication26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Print)9781538615010
DOIs
Publication statusPublished - 5 Jul 2018

Publication series

Name26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Fingerprint Dive into the research topics of 'Impact of age, sex and hair type on SSVEP-based EEG signals analysis'. Together they form a unique fingerprint.

Cite this