Hybrid SSVEP/P300 BCI Keyboard - Controlled by Visual Evoked Potential

Felipe Alberto Capati, Rodrigo Prior Bechelli, Maria Claudia F. Castro

Abstract

This paper presents a two stage Brain Computer Interface (BCI) keyboard system that consumes Electroencephalography (EEG) signals based on two evoked potential detection methods: P300 and Steady-State Visual Evoked Potential (SSVEP). In order to develop a practical daily use EEG system, signals were captured with a standard low cost Emotiv-EPOC system and processed using OpenViBE platform. Fast Fourier Transform (FFT) and sample average were used as feature extraction methods while Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) were used as classifiers.

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Paper Citation


in Harvard Style

Capati F., Bechelli R. and Castro M. (2016). Hybrid SSVEP/P300 BCI Keyboard - Controlled by Visual Evoked Potential . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 214-218. DOI: 10.5220/0005705202140218


in Bibtex Style

@conference{biosignals16,
author={Felipe Alberto Capati and Rodrigo Prior Bechelli and Maria Claudia F. Castro},
title={Hybrid SSVEP/P300 BCI Keyboard - Controlled by Visual Evoked Potential},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)},
year={2016},
pages={214-218},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005705202140218},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)
TI - Hybrid SSVEP/P300 BCI Keyboard - Controlled by Visual Evoked Potential
SN - 978-989-758-170-0
AU - Capati F.
AU - Bechelli R.
AU - Castro M.
PY - 2016
SP - 214
EP - 218
DO - 10.5220/0005705202140218