2011Journal of Neural EngineeringOpen access

SSVEP-based Bremen–BCI interface—boosting information transfer rates

Ivan Volosyak

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Abstract

In recent years, there has been increased interest in using steady-state visual evoked potentials (SSVEP) in brain-computer interface (BCI) systems; the SSVEP approach currently provides the fastest and most reliable communication paradigm for the implementation of a non-invasive BCI. This paper presents recent developments in the signal processing of the SSVEP-based Bremen BCI system, which allowed one of the subjects in an online experiment to reach a peak information transfer rate (ITR) of 124 bit min(-1). It is worth mentioning that this ITR value is higher than all values previously published in the literature for any kind of BCI paradigm.

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What this paper is about

In recent years, there has been increased interest in using steady-state visual evoked potentials (SSVEP) in brain-computer interface (BCI) systems; the SSVEP approach currently provides the fastest and most reliable communication paradigm for the implementation of a non-invasive BCI. This paper presents recent developments in the signal processing of the SSVEP-based Bremen BCI system, which allowed one of the subjects in an online experiment to reach a peak information transfer rate (ITR) of 124 bit min(-1). It is worth mentioning that this ITR value is higher than all values previously published in the literature for any kind of BCI paradigm.

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Available abstract

In recent years, there has been increased interest in using steady-state visual evoked potentials (SSVEP) in brain-computer interface (BCI) systems; the SSVEP approach currently provides the fastest and most reliable communication paradigm for the implementation of a non-invasive BCI. This paper presents recent developments in the signal processing of the SSVEP-based Bremen BCI system, which allowed one of the subjects in an online experiment to reach a peak information transfer rate (ITR) of 124 bit min(-1). It is worth mentioning that this ITR value is higher than all values previously published in the literature for any kind of BCI paradigm.

Key concepts: Brain–computer interface, Information transfer, Computer science, Interface (matter), Boosting (machine learning), Visual evoked potentials, Speech recognition, Support vector machine

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