Pub. online:1 Jan 2019Type:Research ArticleOpen Access
Volume 30, Issue 4 (2019), pp. 781–798
Brain Computer Interfaces (BCI) are devices that use brain signals for control or communication. Since they don’t require movement of any part of the body, BCI are the natural choice for assisted communication when a person is unable to move.
In this article, BCI based communicator for persons in locked-in state is described. It is based on P300 brain response of the user, thus does not require prior training, movement or imagination of movement. Auditory paradigm is selected in order to apply the communicator in cases where visual ability is also impaired. The communicator was designed to prove also whether low cost hardware with reduced electrode set could be used efficiently in everyday environment, without the need for expert personnel.
The design of the communicator is described first, followed by detailed analyses of the performance when used by either healthy or disabled subjects. It is shown that auditory paradigm is the primary factor that limits the accuracy of communication. Hardware characteristics and reduced electrode set influence the accuracy in a negative way as well, while different questions and answer types produce no major differences.
Pub. online:1 Jan 2011Type:Research ArticleOpen Access
Volume 22, Issue 3 (2011), pp. 383–394
In this paper we have proposed a novel method for image denoising using local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule. The algorithm performs separable column-wise and row-wise image denoising (i.e., independently by rows and by columns), combining the obtained results into the final image estimate. The newly developed method performs competitively among recently published state-of-the-art denoising methods in terms of the peak signal-to-noise ratio (PSNR), even outperforming them for small to medium noise variances for images that are piecewise constant along their rows and columns.