Brain-computer interface (BCI) is a device which allows paralyzed people to navigate a robot, prosthesis or wheelchair using only their own brains reactions. By creating a direct communication pathway between the human brain and a machine, without muscles contractions or activity from within the peripheral nervous system, BCI makes mapping persons intentions onto directive signals possible. One of the most commonly utilized phenomena in BCI is steady-state visually evoked potentials (SSVEP). If subject focuses attention on the flashing stimulus (with specified frequency) presented on the computer screen, a signal of the same frequency will appear in his or hers visual cortex and from there it can be measured. When there is more than one stimulus on the screen (each flashing with a different frequency) then based on the outcomes of the signal analysis we can predict at which of these objects (e.g., rectangles) subject was/is looking at that particular moment. Proper preprocessing steps have taken place in order to obtain maximally accurate stimuli recognition (as the specific frequency). In the current article, we compared various preprocessing and processing methods for BCI purposes. Combinations of spatial and temporal filtration methods and the proceeding blind source separation (BSS) were evaluated in terms of the resulting decoding accuracy. Canonical-correlation analysis (CCA) to signals classification was used.
The paper presents selected results of studies connected with modeling of a biological object which could be used for simulation and measurements of the selected human tissues optical transmittance. The studies were performed for transilluminated homogeneous tissue layers as well as for objects consisted of different tissues. During simulations the software built with LabVIEW environment was used. Experimental verification of the model structure was made with spectrophotometry. The presented examples of modeling concern the transmittance spectra for two selected specific objects: the venous blood and muscle tissue analyzed in the wavelength range extending from 360 nm to 900 nm. The implemented model could be used in estimating the content and thickness of particular layers distinguished in a complex object and prediction of their transillumination efficiency.