Abstract
A brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP) is among the fastest BCIs that have ever been reported, but it has not yet been given a thorough study. In this study, a pseudorandom binary M sequence and its time lag sequences are utilized for modulation of different stimuli and template matching is adopted as the method for target recognition. Five experiments were devised to investigate the effect of stimulus specificity on target recognition and we made an effort to find the optimal stimulus parameters for size, color and proximity of the stimuli, length of modulation sequence and its lag between two adjacent stimuli. By changing the values of these parameters and measuring classification accuracy of the c-VEP BCI, an optimal value of each parameter can be attained. Experimental results of ten subjects showed that stimulus size of visual angle 3.8°, white, spatial proximity of visual angle 4.8° center to center apart, modulation sequence of length 63 bits and the lag of 4 bits between adjacent stimuli yield individually superior performance. These findings provide a basis for determining stimulus presentation of a high-performance c-VEP based BCI system.
Highlights
Under normal circumstances, communication between the human brain and the external world is accomplished by peripheral nerves and muscles
The evaluation criterion of the c-visual evoked potential (VEP) brain-computer interface (BCI) performance was classification accuracy, which was defined as the number of correctly recognized trials divided by the total number of trials conducted for one experimental subtask
In the size of visual angle 1.7°, the accuracies of four subjects already reached to a very high level of above 90%; In a medium size of 3.8°, only one subject yielded an accuracy below 90%; When the size changed to 7.1°, the performance of all subjects is satisfactory with accuracy above 95%
Summary
Communication between the human brain and the external world is accomplished by peripheral nerves and muscles. A brain-computer interface (BCI) translates intention into control commands of an external device and enables people to communicate without the involvement of peripheral nerves and muscles [1]. This feature makes BCIs popular in the field of neural engineering and clinical rehabilitation [2]. Electroencephalogram (EEG) recorded on scalp is widely used in BCI systems due to its non-invasiveness and ease to acquire. The performance of EEG based BCI systems has been improved considerably, they do not yet support widespread application in real-life environments
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