Literature DB >> 21096910

A comparison of monopolar and bipolar EEG recordings for SSVEP detection.

Pablo F Diez1, Vicente Mut, Eric Laciar, Enrique Avila.   

Abstract

This paper presents a comparative study over the detection of Steady-State Visual Evoked Potential (SSVEP) with monopolar or bipolar electroencephalographic (EEG) recordings in a Brain-Computer Interface experiment. Five subjects participated in this study. They were stimulated with four flickering lights at 13, 14, 15 and 16 Hz and the EEG was measured simultaneously with two bipolar channels (O(1)-P(3) and O(2)-P(4)) and with six monopolar channels at O(1), O(2), P(3), P(4), T(5) and T(6) referenced to F(Z). The EEG was processed by means of spectral analysis and the estimation of power at each stimulation frequency and its harmonics. In average, the monopolar recordings present accuracy in classification of 74.5% against an 80.1% for bipolar recordings. It was found that bipolar recording are better than monopolar recordings for detection of SSVEP.

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Year:  2010        PMID: 21096910     DOI: 10.1109/IEMBS.2010.5627451

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  4 in total

1.  Assisted closed-loop optimization of SSVEP-BCI efficiency.

Authors:  Jacobo Fernandez-Vargas; Hanns U Pfaff; Francisco B Rodríguez; Pablo Varona
Journal:  Front Neural Circuits       Date:  2013-02-25       Impact factor: 3.492

2.  Asynchronous BCI control using high-frequency SSVEP.

Authors:  Pablo F Diez; Vicente A Mut; Enrique M Avila Perona; Eric Laciar Leber
Journal:  J Neuroeng Rehabil       Date:  2011-07-14       Impact factor: 4.262

3.  Comparing Steady-State Visually Evoked Potentials Frequency Estimation Methods in Brain-Computer Interface With the Minimum Number of EEG Channels.

Authors:  Mehrnoosh Neghabi; Hamid Reza Marateb; Amin Mahnam
Journal:  Basic Clin Neurosci       Date:  2019-05-01

4.  Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate.

Authors:  Jisoo Ha; Seonghun Park; Chang-Hwan Im
Journal:  Front Neuroinform       Date:  2022-02-24       Impact factor: 4.081

  4 in total

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