Software and Data Engineer
Brazilian Jiu-jitsu Fighter
Neuroscience
Optimal stopping for an ERP-based BCI puzzle game
BCI-analysis for ERP-data relies on the repetition of stimuli in order to find reliable classifications. The more repetitions, the more reliable is the prediction. But this reliability comes at a cost of speed. The more repetitions are used, the longer it takes to form a decision, leading to a trade-off between time and accuracy in BCI's. The problem of optimal stopping tries to optimize this trade-off. For BCI's exist different approaches to optimal stopping. In this paper, we compare two of them, optimal fixed and t-test, in a BCI-based puzzle game.
Visual and Neural Processing during Search: What Colours Make Easy Targets
Can we explain why highlighter are yellow with neural data? From our daily experience, we know some colors are more attracting to eyes. The difference in stimulation is not due to the energy of lights (otherwise purple would be the strongest) but due to biological and evolutionary reasons. Some colors are more meaningful than others and the ability to identify them affects a population of species. For example, many ripe fruits are yellow and therefore animals that are able to locate items in the color may have an advantage in collecting food. In this research, we try to verify the sensibility of yellow and other colors also reflects in the EEG recordings in inferior temporal cortex of the brain, which is a part of the visual system involved in object recognition, and in the performance in an experimental search task. In conclusion, results are coherent to the presumption that yellow objects stimulate the subject significantly more and require less search time in the search task. Other colors and other features of the objects are also discussed.