Directional predictions for 4-class BCI data

Brain-computer interfaces (BCI) can allow disabled people to drive a wheel chair, just by imagining movement. Therefore, present day BCIs use training data, recorded during four different conditions, to calibrate a classifier. This, however, causes jerky behavior while abruptly switching between discrete states. We propose a cost-sensitive support vector approach for estimating two-dimensional directions based on fourclass BCI data, that is recorded from three subjects. We found that our method reduces the number of severe errors compared to classical support vector machines and results in a smoother trajectory estimate for application in a wheel chair.