Abstract
The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.
Highlights
A Brain-Computer Interface (BCI) enables a user to control a computer by pure brain activity without the need for muscle control
We evaluate the use of online adaptation to further improve a code-modulated visual evoked potentials (c-VEPs) BCI system
With unsupervised adaptation an average accuracy of 96.05% was achieved, which corresponds to an average bitrate of 143.56 bit/ min, while the online results with adaptation based on error-related potentials (ErrPs) yielded an average accuracy of 96.18% or 143.95 bit/min
Summary
A Brain-Computer Interface (BCI) enables a user to control a computer by pure brain activity without the need for muscle control. There are different kinds of BCIs, that are based on modulation of the sensorimotor rhythm (SMR), detection of a P300 or steady state visual evoked potentials (SSVEPs). In this paper we present a BCI that uses code-modulated visual evoked potentials (c-VEPs) to detect the user’s intention. In a c-VEP BCI, a pseudorandom code is used to modulate different visual stimuli. If a person attends one of those stimuli, a cVEP is evoked and can be used for controlling the BCI. This idea has been proposed by Sutter in 1984 [1] and has been tested 8 years later, when an ALS patient was reported to write 10 to 12 words/minute with a c-VEP BCI system using intracranial electrodes [2]. In [4] and [5] new methods for improving classification in a c-VEP BCI were presented and the possibility for establishing high-performance communication was demonstrated
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